AI Blind Spots: How Enterprises Detect Hidden Model Failures
AI systems can fail due to hidden blind spots. Learn how enterprises detect edge cases and structural gaps before deployment.
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]]>AI Blind Spots: How Enterprises Detect Edge Cases, Structural Gaps, and Hidden Model Failures
Traditional training and evaluation pipelines often fail to account for the high variance and noise inherent in real-world data. Because of this evaluation gap, AI models may appear to perform well during testing but still contain hidden weaknesses that only emerge in production environments. As a result, enterprises sometimes deploy AI systems with blind spots that surface only when systems encounter real-world complexity.
The two primary sources of AI blind spots are structural gaps within the AI model and edge cases. So how can enterprises detect hidden weaknesses before their AI systems make critical mistakes?
What are AI's "Blind Spots?"
AI models sometimes encounter inputs they don’t understand and make wrong or unpredictable decisions. These “blind spots” are basically gaps in what the model knows, and moments when it hasn’t seen anything similar before, and makes its best guess. The lack of context in these scenarios leads to incorrect or irrelevant outputs.
Edge Cases vs Structural Gaps
Edge cases in AI models
- Real-world failures from narrow training that expose AI’s blind spots.
- They might involve blurred images, heavy accents in speech recognition, or unusual transaction patterns in finance.
- While statistically rare, these anomalies often define the moments when AI systems fail most visibly.
Structural Gaps in AI models
- These are weaknesses embedded within a model’s architecture, data design, or assumptions.
- Structural gaps make an AI system fragile, particularly in environments where data evolves faster than the model adapts.
Why do AI's Blind Spots Occur?
Blind spots are rarely caused by one factor. They usually arise when biased data, incomplete annotation, and limited evaluation intersect during development or deployment.
Statistical bias
- Blind spots often originate from uneven or incomplete representation in training data.
- Statistical bias arises when certain groups, contexts, or conditions are underrepresented, limiting the model’s generalization.
- Models may work well in one region, demographic group, or environment, but degrade sharply elsewhere.
Annotation gaps
- Inconsistent or incomplete labeling also introduces persistent blind spots.
- If annotators lack proper training to recognize rare scenarios or if guidelines fail to capture ambiguity, unusual cases would go mislabeled or omitted entirely.
- Fully automated pre-labeling without HITL or expert review often exacerbates this issue by carrying forward existing errors.
Missing context
- Some inputs require interpretation beyond raw data.
- Sarcasm, cultural references, uncommon object interactions, or unusual sensor readings may be simple for humans but complicated for models.
- Without contextual grounding, AI systems misinterpret inputs even when visual or textual signals appear clear.
Lapse in evaluation and adaptability
- Many systems perform well in demos but fail in operational settings because evaluation sets do not reflect real-world noise.
- If tests exclude ambiguous scenarios, out-of-distribution examples, or adversarial inputs, blind spots remain undiscovered until deployment.
How Common are Edge Cases and Structural Gaps?
Edge Cases
Edge cases, by definition, are rare and occur once or twice per thousand typical examples. But at enterprise scale, even rare failures can accumulate quickly, potentially disrupting workflows and operations.
For instance,
- A 0.3% misclassification rate in document workflows can result in thousands of incorrect outputs per month.
- A 1% anomaly in retail inventory detection can cause persistent stock inaccuracies.
- A fraction of a percent error in financial risk scoring can undermine regulatory compliance and trigger unintended behavior.
Structural Gaps
Structural gaps arise from routine conditions that an AI model was never designed to handle. It was found that in document-based or dialogue summarization, roughly one in three (30%) outputs can contain factual inconsistencies or hallucinations.
For example, Amazon suspended its ‘Just Walk Out’ system due to systemic mismatches between model assumptions and real-world complexity –
- Frequent occlusions and overlapping shoppers created constant ambiguity that the system couldn’t resolve automatically.
- Normal shopping behaviors like grabbing, comparing, and returning items triggered thousands of low-confidence events.
- The AI’s inability to interpret real-world motion required extensive human review to bridge the gap between design assumptions and reality.
Example: When an Edge Case Causes a Real Failure
Consider a financial institution that deploys an AI system to extract key fields from invoices to automate accounts payable workflows.
Most invoices in the training data follow predictable layouts. Vendor names appear in the header, totals are clearly printed, and currency symbols are consistent. Under these conditions, the system performs well during evaluation.
However, a supplier submits an invoice that deviates from the expected format. The total amount is handwritten, the vendor identifier appears within a footer image, and the currency format differs from what the model has seen during training.
The AI system extracts the following information:
Vendor: Unknown
Amount: $1000
Currency: USD
The actual invoice total was $1800 CAD.
Because the document passed basic validation checks, the automated workflow approves the payment without triggering an alert.
This type of failure illustrates how blind spots emerge in real-world systems. The model performed well in testing because the evaluation dataset contained mostly clean, standardized invoices. When exposed to irregular formats and handwritten annotations, the system encountered conditions it had not learned to handle.
For enterprises operating at scale, even rare edge cases like this can accumulate into meaningful operational risk.
What Happens When AI Encounters an Edge Case?
Models rarely, if ever, send a signal when operating outside their comfort zone, as they may not clearly identify ‘uncertainty.’ So when AI encounters an unfamiliar input, its behavior often falls into one of three patterns:
- Silent errors: The model produces a confident but wrong answer without signaling uncertainty.
- Breakdowns in logic: Outputs appear incoherent or contradictory, which are common in LLMs when under ambiguity.
- Operational failures: Automated systems deviate from their intended functions, producing incorrect outputs. For example, they could misclassify products, reject valid applications, or trigger false alerts.
Short-Term Risks of Inaction
Unaddressed blind spots can cause:
- Project delays
- Unexpected costs
- Operational errors
- Compliance issues
- Customer trust erosion
Small failures can have a disproportionate impact when AI supports critical decisions.
Long-Term Value of Mitigating Risk
- Enterprises that invest in detecting these blind spots and mitigation build AI systems that are more adaptable and rarely drift.
- High-quality data, continuous evaluation, and structured oversight reduce operational friction and improve long-term performance.
- Over time, this becomes a competitive advantage.
How to Prepare for Edge Cases
Diverse, scenario-driven data
- Models need exposure to varied, realistic conditions: rare events, ambiguous cases, environmental noise, and domain-specific anomalies.
- Scenario-driven datasets help the system generalize beyond idealized samples.
Annotation, guidelines, and reinforcement learning
- High-quality annotation includes detailed guidelines, expert review, adjudication workflows, and processes for labeling ambiguous cases.
- Reinforcement learning frameworks can help models learn from rare or complex scenarios.
Strengthen Evaluation and Testing
- Model evaluation must include noise, anomalies, distribution shifts, adversarial examples, and “long-tail” data.
- Structured stress testing helps reveal blind spots early.
Human Oversight and AI Governance
- Robust monitoring ensures continuous adaptation as environments evolve.
- Governance frameworks like thresholds, escalation triggers, and audit trails help enterprises detect failures before they reach end users.
Building Trustworthy AI with Quality Data and Expertise
Reliable AI depends on how well systems handle the unexpected. Structural gaps, biased data, and poorly covered edge cases are often the root causes of failures in production. By improving data quality, annotation practices, and integrating continuous evaluation, enterprises can reduce blind spots and improve model performance in real-world settings.
Innodata supports enterprises across every stage of AI development from data collection and annotation to evaluation and model testing. Connect with an Innodata expert today to strengthen your AI pipelines and eliminate the risks hidden inside blind spots.
Bring Intelligence to Your Enterprise Processes with Generative AI.
Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.
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]]>The post Trace Datasets for Agentic AI: Structuring and Optimizing Traces for Automated Agent Evaluation appeared first on Innodata.
]]>Trace Datasets for Agentic AI: Structuring and Optimizing Traces for Automated Agent Evaluation
Agentic AI refers to multi-agent systems that plan and execute complex goals, using role-based orchestration and persistent memory. Through trace datasets and automated agent evaluation, enterprise AI leaders, platform and governance owners can manage operational challenges at scale, reducing costs, improving reliability, and ensuring compliance.
Traditional input–output evaluation assumes intelligence is expressed in a single response. However, agentic AI’s intelligence is reflected in the sequence of decisions, actions, retries, and adaptations that lead to an outcome.
The Enterprise Challenge
In agentic systems, this loss of visibility is a material business risk, not a minor evaluation gap. For teams responsible for deploying and governing agentic AI in production, limited insight into agent behavior directly impacts operational cost, incident response time, and regulatory risk. Using traditional evaluation approaches, enterprises cannot understand:
- How decisions were made
- Where failures originated
- Whether systems are reliable, safe, and compliant
Trace datasets and automated agent evaluation together form an enterprise-ready foundation for evaluating and improving agentic AI systems. By converting raw agent execution into a repeatable pipeline from traces to structured datasets to automated evaluation, enterprises gain the observability and governance capabilities required to operate agentic systems with confidence at scale.
This article covers:
- What agent traces look like in practice
- How the agent traces are structured into evaluation-ready trace datasets
- How those datasets enable automated agent evaluation, and
- How evaluation results feed into observability and governance workflows
Traditional Evaluation and Its Limits in Agentic Systems
Traditional evaluation assesses the relationship between an input and its resulting output using metrics such as accuracy, relevance, and correctness.
In agentic systems, this approach captures what happened but not how it happened, creating gaps in transparency, accountability, and trust. For example, two agents may produce the same output while following very different paths, with different costs, risks, and failure modes.
Agentic evaluation requires accounting for:
- Multi-step reasoning and planning
- Tool usage and orchestration
- Partial failures mid-execution
- Retry and recovery logic
- Multiple agents coordinating
Without measuring these behaviors, enterprises cannot reliably diagnose errors, compare agent performance, or enforce policies.
Trace Datasets in Agentic AI Systems
A trace dataset is a structured record of an agent’s behavior across a task. For example, consider this structured trace:
{
“task”: “Customer refund request,”
“agent”: “Customer support AI”,
“trace”: [
{
“step”: “Understand request”,
“action”: “Identify refund intent”,
“result”: “Refund request detected”
},
{
“step”: “Check eligibility”,
“action”: “Query billing system”,
“result”: “Order not eligible”,
“time_ms”: 420
},
{
“step”: “Apply policy”,
“action”: “Escalate to human agent”,
“result”: “Escalation triggered”
}
],
“final_outcome”: “Escalated to human”,
“policy_compliant”: true
}
Key components
- task: What the agent was supposed to do.
- agent: Which AI handled the task.
- trace: Step‑by‑step records of what the agent did, including:
- step: Stage or intent within the workflow
- action: Attempted behavior
- result: Outcome
- time_ms: Step duration
This trace becomes a unit of evaluation, capturing the sequence of decisions and actions leading to the outcome. A collection of such standardized traces forms a trace dataset for automated agent evaluation.
How Trace Datasets Differ From Traditional Logs and Datasets
Evaluation-ready trace datasets preserve execution context and decision flow, including:
- Decision-making paths
- Planning and task decomposition
- Tool selection and sequencing
- Efficiency, latency, and retries
- Safety, policy adherence, and risk
Examples of Trace Datasets:
- Agentic trace benchmarks
- System performance and latency traces
- Observability traces from production systems
- End-to-end task execution traces from production customer support or compliance workflows
Why Trace Datasets Matter in the Enterprise
Trace datasets support:
- Explainability
- Auditing and compliance
- Debugging and root-cause analysis
- Continuous system improvement
By evaluating and enriching trace data, enterprises can, for example:
- Cut mean time to debug failures
- Surface recurring policy violations early, and
- Demonstrate end‑to‑end decision trails for audits.
Can Your Agentic Traces Support Automated Agent Evaluation?
Agent traces are often unstructured and difficult to analyze or compare.
Before structuring | After structuring |
Fragmented logs | Ordered traces |
Tool-specific events | Unified fields |
Unordered outputs | Comparable runs |
Structuring traces and optimizing workflows for evaluation
Preparing for automated agent evaluation requires standardizing trace data:
- Standardization converts unstructured logs into machine-readable, evaluation-ready trace datasets, making agent behavior comparable across runs.
- Structured traces enable automated scoring, labeling, and analysis.
- In practice, this involves defining core fields such as task ID, step ID, action, tool used, latency, outcome, and policy signals.
Once structured, agent workflows can be optimized using these traces by:
- Prioritizing high-risk steps
- Tuning retry policies
- Refining tool selection based on observed agent behavior.
This optimization loop enables continuous improvement without rearchitecting agent workflows.
Consistent trace data formats and standards
- Machine-readable schemas (e.g., JSON, OpenTelemetry) capture execution context, sequence events, and link them to outcomes.
- Standardized formats improve interoperability across evaluation tools, monitoring systems, and governance platforms, reducing friction as agentic systems evolve and scale.
Enterprise value: With structured trace data, enterprises can compare performance, conduct automated analysis at scale, and integrate the insights into evaluation and governance pipelines.
Automated Agentic AI Evaluation: Measuring Agent Behavior at Scale
Automated agentic AI evaluation measures behavior across tasks rather than judging outcomes in isolation.
Step-level evaluation asks
- Were the right tools selected at each step?
- How many retries or recoveries occurred?
- Where latency or failures emerged in the workflow?
Outcome-level evaluation asks
- Did the task complete successfully?
- Was the final response correct or policy-compliant?
These metrics are computed directly from individual trace steps rather than inferred solely from the final output. For example, escalation appropriateness can be measured by comparing policy-required escalation steps in the trace against the agent’s actual actions, while efficiency metrics such as cost or latency are computed from cumulative tool calls and step-level execution times within a trace.
Agentic AI Evaluation & Insight
Automated agentic AI evaluation platforms use trace data in live and offline environments to:
- Monitor system health
- Detect regressions
- Identify inefficiencies
- Support governance and audits
Labeling and Enriching for Learning and Governance
Labeling and enrichment typically occur at the trace-step level, turning evaluations into reusable training and analytics assets.
Example:
- Marking a failed tool call
- Annotating a reasoning error
- Flagging a policy-compliant escalation
Common trace data labels include:
- Success/failure
- Safe/unsafe
- Correct/incorrect
The resulting labeled and enriched trace data becomes a long-term asset, supporting continuous learning and automated agent evaluation.
Automated annotations add context by
- Explaining errors or edge cases
- Clarifying intent
- Linking traces to gold standards
- Connecting agent behavior to business outcomes.
- Human-in-the-loop improves reliability and reduces room for critical errors.
Use Case: Customer Support Agent
- In high-volume customer support environments, a customer support agent handles thousands of requests per day, generating trace datasets across chat conversations, ticketing platforms, and internal knowledge bases.
- Automated agent evaluation uses these traces to assess outcomes such as first-contact resolution rates, escalation appropriateness, average tool calls per ticket, and recovery from partial failures.
- Agent workflows are designed to be evaluation-ready, producing clear, structured traces at each step of execution so that agent behavior can be measured consistently over time.
- This supports automated agent evaluation at scale while reducing time to diagnose failures and maintaining privacy controls, auditability, and regulatory compliance.
Challenges, Best Practices, and Considerations
Before adopting automated evaluation, it is important to understand the common challenges that can impact evaluation accuracy.
Key challenges to evaluating agentic AI
- Sampling strategies: Evaluating every trace is impractical; selective sampling is needed to capture rare but high-impact failures.
- Storage and retention tradeoffs: Trace datasets must balance regulatory and audit requirements with storage costs and retention limits.
- Step-level privacy redaction: Sensitive information often needs masking at the individual trace-step level, rather than across entire tasks or sessions.
- Evaluation drift: As agents evolve, evaluation criteria must remain consistent or be explicitly versioned to maintain meaningful comparisons over time.
- Operational and business alignment: Evaluation workflows must balance tooling and process complexity while ensuring alignment with business objectives, risk tolerance, and domain priorities.
These challenges can be made tractable by:
- Using trace datasets to drive targeted sampling
- Tiered retention and redaction policies
- Versioned evaluation criteria, and
- Tight alignment between agent workflows and business risk
Best Practices for Agentic AI Evaluation
Trace-based evaluation makes tradeoffs such as speed versus safety or autonomy versus escalation explicit and measurable. By grounding these tradeoffs in trace data, enterprises can tune agent behavior deliberately rather than discovering unintended risk only after failures occur in production.
To enable this,
- Define clear, behavior-level evaluation metrics based on an agent’s reasoning steps, tool usage, and recovery behavior, rather than relying solely on final outputs.
- Start with high-impact, high-risk agent workflows where evaluation gaps have clear business or compliance consequences.
- Combine automated evaluation with targeted human-in-the-loop review for ambiguous decisions, policy edge cases, and high-severity failures.
- Align evaluation criteria with business objectives, domain risk tolerance, and operational constraints, in addition to model performance metrics.
- Design agent workflows to be evaluation ready from day one by ensuring every decision, tool call, and recovery step produces structured, traceable signals.
Designing Evaluation-Ready Agent Workflows
Agentic AI cannot be governed, improved, or trusted using output-only evaluation.
Trace datasets provide the foundation for understanding and managing agent behavior. Trace-based evaluation ensures that agentic systems continue to operate as intended when embedded within enterprise workflows.
Innodata focuses on creating evaluation-ready trace datasets through trace structuring, step-level labeling, enrichment, and human-in-the-loop workflows. Our work complements existing agent frameworks and observability tools, enabling enterprises to evaluate agent behavior across both development and production environments consistently.
Connect with our experts to explore how trace-based evaluation fits into your agentic roadmap.
Bring Intelligence to Your Enterprise Processes with Generative AI.
Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.
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]]>Turning Human Motion into Better AI: How Kinematics Improves Data Labeling and Model Quality
Using physics-based motion analysis to improve annotation accuracy, automated QC, and computer vision models.
Frank Tanner, VP of Computer Vision and Robotics
February 5, 2026
When most people hear “data labeling,” they picture someone clicking points on a screen all day. At Innodata, that’s only the starting point. We build labeling systems that capture structure, context, and motion, not just pixel locations.
Video from Wikimedia Commons, pose added by Innodata
In this post, I’ll walk through how we use the physics of motion—kinematics—to improve labeling accuracy for exercise and sports video, automatically detect annotation errors, and evaluate whether trained models are tracking motion realistically.
Beyond Dots on a Screen: What “Sophisticated Labeling” Means
At the core of many AI systems is labeled data: keypoints on a body, bounding boxes around objects, or trajectories over time. One of Innodata’s main lines of business is providing this kind of high-precision annotation at scale, including detailed keypoint extraction on people, animals, and objects.
But doing this well involves more than hiring a team of annotators and giving them a drawing tool. We design workflows and tooling that:
- Capture fine-grained structure, including joints, limbs, and equipment, using keypoints and skeletal representations.
- Enforce consistent definitions across thousands of clips so that each keypoint (for example, “wrist”) always corresponds to the same anatomical location.
- Integrate pre-labeling models and human-in-the-loop review to make annotation faster and more accurate.
That’s the foundation. The next step is understanding the motion itself.
Why Kinematics Matters for Labeling
Kinematics is the study of how things move: positions, velocities, and accelerations over time. For many types of video—exercise, physical therapy, and sports—this motion is the signal we care about.
If we understand the motion, we can move beyond frame-by-frame labeling and reason about the behavior itself:
- We can ask whether a movement “makes sense” for the activity (e.g., a smooth repetition versus a sudden jump).
- We can detect labeling mistakes automatically when the motion breaks the expected pattern.
- We can evaluate whether models trained on that data are tracking motion plausibly, not just optimizing a loss function.
In other words, kinematics turns raw labels into a structured description of behavior.
Example 1: A Smooth Triceps Press-Down
In one of our exercise videos, I perform a triceps press-down while we track keypoints on my arms and torso (this is my unofficial side gig as a middle-aged fitness model).
If you plot the vertical position of my hand or wrist over time, you see a motion that looks very close to a smooth, periodic sine wave: down, up, down, up, with no abrupt spikes.
Video and annotation information provided by Innodata
Why that matters:
- For this kind of exercise, we expect controlled, periodic motion with relatively constant tempo.
- A smooth curve tells us the keypoints are consistent across frames and the annotators (or models) followed the motion correctly.
- It gives us a reference pattern (a “healthy” signal) for that type of movement.
This simple example shows how combining labels with motion analysis gives us a sanity check: the data behaves the way the underlying physics of human motion predicts it should.
Example 2: Bench Press and Automatic Anomaly Detection
Now compare that to a bench press video we use, sourced from Wikimedia Commons and annotated using a pose extraction model. When we examine the keypoint trajectories, we sometimes see abrupt jumps in the wrist position—sudden changes that don’t match how a human actually moves during a controlled bench press repetition.
Video data from Wikimedia Commons, pose and kinematics added by Innodata
To make this concrete, we:
- Track the wrist position frame by frame.
- Compute the change over time. In mathematical terms, this is expressed as the first derivative of the position signal (𝑑𝑥/𝑑𝑡), which you can think of as the instantaneous speed of the point.
- Look for spikes where 𝑑𝑥/𝑑𝑡 suddenly becomes much larger than normal for a single frame.
Those spikes are strong indicators of anomalies:
- They might be annotation errors (the point “snaps” to the wrong place for one frame).
- They might be model tracking failures (the model loses the wrist and re-acquires it incorrectly).
Instead of manually scrubbing through every video, our quality control tooling flags these suspect segments automatically. Human reviewers can then quickly confirm, correct, or re-label them, closing the loop between analytics and annotation.
Closing the Loop: Labels, Models, and Automated Quality Control
Innodata’s strength is not just in advanced labeling techniques but in using motion analytics as part of a continuous quality control loop.
Our approach ties together:
- Advanced annotation: high-quality keypoints, custom workflows, and domain-specific label taxonomies.
- Motion modeling: kinematic analysis (positions, velocities, and patterns over time) to describe how things should move in a given context.
- Automated anomaly detection: using measures like 𝑑𝑥/𝑑𝑡 (change over time) to flag suspicious labels and model outputs for review.
This combination lets us deliver datasets and models that aren’t just “labeled,” but physically coherent, statistically robust, and aligned with real-world behavior across applications ranging from fitness and sports to robotics and beyond.
Treating motion as a first-class signal is essential for AI systems that need to interpret how people or objects move. When labeling and quality control reflect the underlying physics of motion, teams can build models that perform more reliably in real-world conditions.
Innodata works with organizations building motion-heavy systems across fitness, sports analytics, robotics, and physical therapy. Our computer vision and robotics experts help design kinematics-driven labeling workflows, automated anomaly detection, and motion-aware evaluation pipelines that improve both data quality and downstream model performance.
To explore how this approach could apply to your use case, connect with an Innodata expert.
Bring Intelligence to Your Enterprise Processes with Generative AI.
Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.
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]]>The post Innodata Selected by Palantir to Accelerate Advanced Initiatives in AI-Powered Rodeo Modernization appeared first on Innodata.
]]>Innodata’s data engineering and annotation capabilities support Palantir’s expanding AI platform deployments for event analytics
NEW YORK, NY / ACCESS Newswire / January 29, 2026 / INNODATA INC. (Nasdaq:INOD) today announced that it has been selected to provide high-quality training data and data engineering services to Palantir Technologies (Nasdaq:PLTR), supporting Palantir’s AI-enabled platforms for rodeo event analysis.
In support of Palantir’s partnership with rodeo operations, Innodata is now further empowering these customers by providing them with specialized annotation and data engineering for thousands of hours of rodeo video footage. This work enables computer vision models to detect animals, riders, and skeleton joints, allowing for the automated calculation and display of performance metrics in bull riding, bronc riding, bareback riding and barrel racing.
Innodata will be providing specialized annotation, multimodal data engineering, and generative-AI workflow support for select Palantir programs. Innodata teams work directly within Palantir’s development and deployment workflows, processing highly complex data modalities – including video, imagery, documents, and multimodal sensor data – with the scale, precision, and security standards required for customer use cases.
“Palantir is developing some of the most sophisticated AI capabilities in the world – from computer vision and geospatial analytics to secure, model-driven decision systems,” said Dimitrios Lymperopoulos, Head of Machine Learning at Palantir. “Innodata’s high-quality training data and data engineering expertise can help us to scale these capabilities with the accuracy, rigor, and operational excellence our customers demand.”
“Our work with Palantir reinforces Innodata’s role as a trusted data engineering partner to the world’s leading AI companies,” said Vinay Malkani, Senior Vice President, Innodata Federal. “Together, we are enabling next-generation enterprise AI deployments. Palantir’s requirements validate the investments we have made in domain-expert annotation, end-to-end generative-AI workflow enablement, rigorous quality systems, and secure global operations.”
Innodata’s engagement with Palantir reflects the accelerating demand for high-quality data engineering capabilities as AI becomes central to national competitiveness and enterprise value creation. As organizations increasingly seek to deploy AI in high-stakes, real-world environments, we believe that the need will continue to grow for trusted data partners with the ability to operate at scale, with precision and security.
About Innodata
Innodata (Nasdaq:INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. That’s why we’re on a mission to help the world’s leading technology companies and enterprises drive Generative AI / AI innovation. We provide a range of transferable solutions, platforms and services for Generative AI / AI builders and adopters. In every relationship, we honor our 35+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
Recently recognized by Wedbush Securities as one of 30 companies defining the future of AI, Innodata has been noted for expertise in domain-specific, high-accuracy AI solutions where precision, compliance, and subject matter expertise are essential. The Company serves five of the “Magnificent Seven” tech giants, leading AI innovation labs, and numerous Fortune 1000 enterprises, providing critical data engineering services that power the next generation of AI innovation. With Innodata Federal, we extend our mission to support U.S. government agencies with AI solutions that enhance national security, improve government services, and accelerate digital transformation.
For more information, visit www.innodata.com.
Forward-Looking Statements
This press release may contain certain forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934, as amended, and Section 27A of the Securities Act of 1933, as amended. These forward-looking statements include, without limitation, statements concerning our operations, economic performance, financial condition, developmental program expansion and position in the generative AI services market. Words such as “project,” “forecast,” “believe,” “expect,” “can,” “continue,” “could,” “intend,” “may,” “should,” “will,” “anticipate,” “indicate,” “guide,” “predict,” “likely,” “estimate,” “plan,” “potential,” “possible,” “promises,” or the negatives thereof, and other similar expressions generally identify forward-looking statements.
These forward-looking statements are based on management’s current expectations, assumptions and estimates and are subject to a number of risks and uncertainties, including, without limitation, impacts resulting from ongoing geopolitical conflicts; investments in large language models; that contracts may be terminated by customers; projected or committed volumes of work may not materialize; pipeline opportunities and customer discussions which may not materialize into work or expected volumes of work; the likelihood of continued development of the markets, particularly new and emerging markets, that our services support; the ability and willingness of our customers and prospective customers to execute business plans that give rise to requirements for our services; continuing reliance on project-based work in the Digital Data Solutions (“DDS”) segment and the primarily at-will nature of such contracts and the ability of these customers to reduce, delay or cancel projects; potential inability to replace projects that are completed, canceled or reduced; our DDS segment’s revenue concentration in a limited number of customers; our dependency on content providers in our Agility segment; our ability to achieve revenue and growth targets; difficulty in integrating and deriving synergies from acquisitions, joint ventures and strategic investments; potential undiscovered liabilities of companies and businesses that we may acquire; potential impairment of the carrying value of goodwill and other acquired intangible assets of companies and businesses that we acquire; a continued downturn in or depressed market conditions; changes in external market factors; the potential effects of U.S. global trading and monetary policy, including the interest rate policies of the Federal Reserve; changes in our business or growth strategy; the emergence of new, or growth in existing competitors; various other competitive and technological factors; our use of and reliance on information technology systems, including potential security breaches, cyber-attacks, privacy breaches or data breaches that result in the unauthorized disclosure of consumer, customer, employee or Company information, or service interruptions; and other risks and uncertainties indicated from time to time in our filings with the Securities and Exchange Commission (“SEC”).
Our actual results could differ materially from the results referred to in any forward-looking statements. Factors that could cause or contribute to such differences include, but are not limited to, the risks discussed in Part I, Item 1A. “Risk Factors,” Part II, Item 7. “Management’s Discussion and Analysis of Financial Condition and Results of Operations,” and other parts of our Annual Report on Form 10-K, filed with the SEC on February 24, 2025, and in our other filings that we may make with the SEC. In light of these risks and uncertainties, there can be no assurance that the results referred to in any forward-looking statements will occur, and you should not place undue reliance on these forward-looking statements. These forward-looking statements speak only as of the date hereof.
We undertake no obligation to update or review any guidance or other forward-looking statements, whether as a result of new information, future developments or otherwise, except as may be required by the U.S. federal securities laws.
Company Contact:
Aneesh Pendharkar
[email protected]
(201) 371-8000
SOURCE: Innodata Inc.
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Trace datasets reveal how AI agents behave and enable automated agentic AI evaluation for reliability, safety, and compliance.
Turning Human Motion into Better AI: How Kinematics Improves Data Labeling and Model Quality
How kinematics-based motion analysis improves data labeling, automated quality control, and computer vision models for fitness and robotics.
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]]>The post AI Evaluation: 7 Core Components Enterprises Must Get Right appeared first on Innodata.
]]>AI Evaluation: 7 Core Components Enterprises Must Get Right
AI systems often drift silently rather than loudly. Unlike traditional software, AI is probabilistic in nature, which means failures may emerge gradually through biased outputs, degraded accuracy, or unsafe behavior rather than obvious system crashes. When this happens in production, the cost is rarely technical alone. It shows up as regulatory exposure, loss of customer trust, or operational risk that compounds over time.
AI evaluation quantifies and measures how models behave, adapt, and whether they can be trusted in real-world use. If these systems can generalize beyond training data, handle uncertainty responsibly, and maintain fairness across contexts, they remain reliable and defensible as business assets rather than experimental tools.
Why Is Evaluating AI Important?
The same model that performs flawlessly in a demo can fail in production when data shifts or conditions change. That’s why AI model evaluation must be a continuous discipline, not a one-time test.
AI evaluation strengthens reliability, exposes vulnerabilities, and ensures fairness and ethical performance.
- AI models learn patterns, and those patterns evolve along with environments, users, and markets.
- Red teaming and ongoing assessment ensure that systems remain accurate, interpretable, and aligned with ethical and operational goals.
- Without continuous evaluation, AI systems optimize locally and fail globally, meeting performance benchmarks while undermining trust, compliance, or accountability.
- Evaluation turns AI from a promising feature into a dependable business asset that performs as intended, explains itself clearly, and adapts intelligently over time.
7 Key Components for AI Evaluation
1. Data quality Every trustworthy AI model starts with clean, representative data. Inconsistent or biased data quietly undermines every metric.
- Identify data gaps and biases using automated profiling to ensure accurate and fair inputs.
- Track data drift and performance with continuous monitoring for sustainable reliability.
- Record data lineage for transparency, compliance, and audit readiness.
Data quality focuses on input integrity. If the foundation is unstable, no amount of post hoc evaluation can fully correct model behavior.
2. Bias & Fairness
Unchecked bias can turn automation into a liability, unless its outcomes are equitable across user groups.
- Measure fairness using metrics such as demographic parity and equal opportunity to assess equitable outcomes.
- Re-balance datasets through weighting or synthetic augmentation to reduce bias.
- Audit models regularly to ensure ongoing fairness and compliance with regulations.
While data quality addresses what goes into a model, bias and fairness evaluation examines how model decisions affect people in the real world. This distinction becomes critical as models scale across diverse populations and use cases.
3. Functional testing
In addition to traditional testing, enterprises must also evaluate the functionalities unique to AI models.
- Ask a deeper question than software QA: does the model behave correctly, and for the right reasons?
- Test outputs across varied inputs to confirm model consistency.
- Stress-test edge cases with noisy or conflicting data to reveal failure points.
- Validate through real-world scenarios to ensure reliable performance.
Unlike deterministic software, AI systems may produce different outputs for semantically similar inputs. Functional testing must account for non-determinism, context sensitivity, and emergent behavior that traditional test cases fail to capture.
4. Performance & Adaptability
Even accurate models decay as data and contexts evolve. Evaluation keeps them fast, efficient, and relevant.
- Assess latency and scalability under real-world load to confirm operational resilience.
- Track shifts in accuracy and response patterns to catch emerging performance issues early.
- Build automated retraining into MLOps pipelines to enable models to adapt naturally to changing data and conditions.
Adaptability requires tradeoffs. Enterprises must balance retraining frequency, system stability, and operational cost to prevent performance gains from introducing new risks.
5. Explainability & Transparency
Transparency makes an untraceable AI model into a system that stakeholders can trust, thanks to explainability and risk management frameworks.
- Use explainability tools like SHAP or LIME to reveal how inputs influence predictions.
- Document decision logic and known limitations to strengthen accountability.
- Create clear and readable summaries so non-technical reviewers can easily interpret the outcomes.
Explainability is not designed for data scientists alone. It is essential for executives, auditors, regulators, and risk teams who must understand why a system behaves as it does and whether it should continue operating.
6. Security & Adversarial Resilience
AI introduces new threats, such as adversarial attacks, that require more than classic IT security. To address this –
- Simulate attacks like data poisoning or prompt injection to expose vulnerabilities.
- Strengthen pipelines with source validation and input screening for stronger defenses.
- Deploy anomaly detection to identify and contain attacks in real time.
As AI systems become more visible and influential, adversarial misuse becomes inevitable. Evaluation is the difference between detecting exploitation early and discovering it after reputational or financial damage occurs.
7. Compliance & Governance
Lasting reliability comes from governance that outlives the development and post-training stages. Since active governance closes the feedback loop, evaluation becomes an ongoing process that is necessary for accountability.
- Assign clear ownership through a dedicated board or leader to oversee ethics, governance, and compliance.
- Adopt recognized standards and frameworks to align documentation, risk management, and regulation.
- Embed these practices throughout the AI lifecycle to ensure responsible, transparent operations.
Governance ensures that evaluation does not degrade into a checklist, but remains an active control system as models evolve.
AI Testing vs. AI Evaluation: What is the Difference?
Testing checks if an AI model works, whereas evaluation checks if it works as intended for people, policies, and purposes. Many enterprises stop at testing and assume their systems are production-ready. The distinction below explains why that assumption creates risk.
Aspect | AI Testing | AI Evaluation | Impact on Enterprise AI |
Core Question | Does the model work as designed? | Does the model behave responsibly in the real world? | Testing ensures the model’s features are reliable while evaluation helps maintain accountability and trust. |
Objective | To validate accuracy and performance. | To assess fairness, robustness, compliance, and societal impact. | Evaluation adds governance and ethics layers on top of testing practices to connect model performance and business integrity. |
Timeline | Usually, pre-deployment or during development. | Continuous across the AI model lifecycle. | The added layer of evaluation results in compliance, ongoing oversight, and adaptive risk management for enterprise AI models. |
Methods | Unit tests, regression checks, performance benchmarks. | Bias audits, red-teaming, and Human-in-the-Loop reviews. | Together, they introduce a multi-dimensional measurement that captures technical and ethical failure modes. |
Outcome | Technical validation that ensures the AI system operates as expected. | Provides strategic assurance that the system aligns with relevant policies, regulations, and public trust. | Enterprises can integrate a compliant AI model that is both technically accurate and culturally sensitive to produce reliable outcomes. |
Continuous Evaluation of AI
Once the core components are in place, maintaining performance and accountability requires ongoing oversight. Continuous evaluation embeds monitoring, feedback, and governance directly into the AI lifecycle.
Real-time monitoring
- Embed feedback loops in production to track automated metrics like accuracy, latency, and fairness to spot decay or violations early.
- Integrate with CI/CD pipelines to determine and set quality thresholds that automatically pause deployments when performance drops.
Use live dashboards to maintain transparency throughout the development and production phases, providing stakeholders with immediate visibility
AI-driven continuous testing: automated prompts and live feedback help detect vulnerabilities early.
Structured feedback loops
- Every retraining should trigger a comprehensive evaluation suite that covers accuracy, bias, and interpretability for each model version.
- Make side-by-side comparisons of new and previous versions to detect regressions or unexpected behavior.
- Integrate a continuous improvement cycle of testing → learning → iterating to minimize guesswork and create more space for innovation.
Governance by design
- Connecting evaluation metrics with policy checks builds compliance, fairness, and safety into the evaluation criteria.
- Conduct routine audits to identify privacy risks, bias, and unsafe outputs, and address any issues found.
- Apply the learnings from the evaluations to adjust policies, aligning oversight with the changes in the AI system.
Measuring ROI
- Measure success through practical indicators, such as resolution rates, cost savings, and improvements in uptime and reliability.
- Evaluate how often AI models complete tasks correctly, especially in complex or multi-step scenarios, to quantify the improvements from evaluation.
- Each cycle of assessment and standards feeds back into the system, enriching data quality and improving prediction accuracy.
Over time, continuous evaluation builds institutional knowledge that compounds, improving data quality, prediction accuracy, and organizational confidence in AI systems.
Are You Evaluating Your AI Across All Essential Components?
From data quality to governance and from fairness to explainability, evaluating AI isn’t just about metrics or compliance. It is about maintaining control over systems that learn, adapt, and influence decisions at scale.
Partner with Innodata to develop and deploy governed, transparent, and future-ready solutions that foster both innovation and trust.
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Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.
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]]>The post Innodata Awarded Prime Contract Position on U.S. Missile Defense Agency’s IDIQ SHIELD Program appeared first on Innodata.
]]>Innodata Awarded Prime Contract Position on U.S. Missile Defense Agency's IDIQ SHIELD Program
NEW YORK CITY, NY / ACCESS Newswire / January 20, 2026 / INNODATA INC. (Nasdaq:INOD) today announced that it was awarded a contract for the Missile Defense Agency Scalable Homeland Innovative Enterprise Layered Defense (SHIELD) indefinite-delivery/indefinite-quantity (IDIQ) contract.
The SHIELD program is designed to drive rapid innovation and deliver next-generation capabilities that strengthen the nation’s multi-layered homeland defense architecture. As part of the broader Golden Dome strategy, this selection positions Innodata to compete for future task orders across research, development, engineering, prototyping, and operations of critical Missile Defense Agency systems that support U.S. national security objectives.
The award was made as part of a list of companies eligible to compete under the program publicly announced by the U.S. Government on 15-Jan-26.
“We are proud to support our nation’s mission to defend the homeland,” said Vinay Malkani, SVP Federal of Innodata. “This contract award reflects our commitment to delivering innovative AI and data engineering solutions that strengthen America’s defense capabilities.”
About Innodata Inc.
Innodata (Nasdaq:INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. That’s why we’re on a mission to help the world’s leading technology companies and enterprises drive Generative AI / AI innovation. We provide a range of transferable solutions, platforms and services for Generative AI / AI builders and adopters. In every relationship, we honor our 35+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
Recently recognized by Wedbush Securities as one of 30 companies defining the future of AI, Innodata has been noted for expertise in domain-specific, high-accuracy AI solutions where precision, compliance, and subject matter expertise are essential. The Company serves five of the “Magnificent Seven” tech giants, leading AI innovation labs, and numerous Fortune 1000 enterprises, providing critical data engineering services that power the next generation of AI innovation. With Innodata Federal, we extend our mission to support U.S. government agencies with AI solutions that enhance national security, improve government services, and accelerate digital transformation.
For more information, visit www.innodata.com.
Forward-Looking Statements
This press release may contain certain forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934, as amended, and Section 27A of the Securities Act of 1933, as amended. These forward-looking statements include, without limitation, statements concerning our operations, economic performance, financial condition, developmental program expansion and position in the generative AI services market. Words such as “project,” “forecast,” “believe,” “expect,” “can,” “continue,” “could,” “intend,” “may,” “should,” “will,” “anticipate,” “indicate,” “guide,” “predict,” “likely,” “estimate,” “plan,” “potential,” “possible,” “promises,” or the negatives thereof, and other similar expressions generally identify forward-looking statements.
These forward-looking statements are based on management’s current expectations, assumptions and estimates and are subject to a number of risks and uncertainties, including, without limitation, impacts resulting from ongoing geopolitical conflicts; investments in large language models; that contracts may be terminated by customers; projected or committed volumes of work may not materialize; pipeline opportunities and customer discussions which may not materialize into work or expected volumes of work; the likelihood of continued development of the markets, particularly new and emerging markets, that our services support; the ability and willingness of our customers and prospective customers to execute business plans that give rise to requirements for our services; continuing reliance on project-based work in the Digital Data Solutions (“DDS”) segment and the primarily at-will nature of such contracts and the ability of these customers to reduce, delay or cancel projects; potential inability to replace projects that are completed, canceled or reduced; our DDS segment’s revenue concentration in a limited number of customers; our dependency on content providers in our Agility segment; our ability to achieve revenue and growth targets; difficulty in integrating and deriving synergies from acquisitions, joint ventures and strategic investments; potential undiscovered liabilities of companies and businesses that we may acquire; potential impairment of the carrying value of goodwill and other acquired intangible assets of companies and businesses that we acquire; a continued downturn in or depressed market conditions; changes in external market factors; the potential effects of U.S. global trading and monetary policy, including the interest rate policies of the Federal Reserve; changes in our business or growth strategy; the emergence of new, or growth in existing competitors; various other competitive and technological factors; our use of and reliance on information technology systems, including potential security breaches, cyber-attacks, privacy breaches or data breaches that result in the unauthorized disclosure of consumer, customer, employee or Company information, or service interruptions; and other risks and uncertainties indicated from time to time in our filings with the Securities and Exchange Commission (“SEC”).
Our actual results could differ materially from the results referred to in any forward-looking statements. Factors that could cause or contribute to such differences include, but are not limited to, the risks discussed in Part I, Item 1A. “Risk Factors,” Part II, Item 7.
“Management’s Discussion and Analysis of Financial Condition and Results of Operations,” and other parts of our Annual Report on Form 10-K, filed with the SEC on February 24, 2025, and in our other filings that we may make with the SEC. In light of these risks and uncertainties, there can be no assurance that the results referred to in any forward-looking statements will occur, and you should not place undue reliance on these forward-looking statements. These forward-looking statements speak only as of the date hereof.
We undertake no obligation to update or review any guidance or other forward-looking statements, whether as a result of new information, future developments or otherwise, except as may be required by the U.S. federal securities laws.
Company Contact
Aneesh Pendharkar
[email protected]
(201) 371-8000
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]]>The post Achieving State-of-the-Art UAV Tracking on the Anti-UAV Benchmark: Innodata Results appeared first on Innodata.
]]>Achieving State-of-the-Art UAV Tracking on the Anti-UAV Benchmark: Innodata Results
Frank Tanner, VP of Computer Vision and Robotics
January 9, 2026
Tracking unmanned aerial vehicles has become a critical challenge for aviation safety, security, and defense organizations. UAVs are now linked to a growing number of near midair collisions around major airports and repeated disruptions of airport operations¹ ². At the same time, small UAVs have become central to modern warfare, particularly in Ukraine, where inexpensive drones are deployed on a massive scale and have transformed the conflict into a proving ground for new battlefield tactics and autonomous systems³ ⁴ ⁵.
These developments have elevated UAV tracking from a niche technical problem to a real-world operational requirement. Detecting and tracking small, fast-moving objects in noisy visual environments places extreme demands on computer vision systems, especially when reliability and low false alarm rates are non-negotiable.
The research community has responded accordingly. CVPR has hosted an Anti-UAV track for the past five years, complete with a dedicated benchmark and public leaderboard for drone tracking models (https://anti-uav.github.io/). At Innodata, we have developed deep expertise in identifying small objects and building algorithms that can reliably detect and track targets under challenging, real-world conditions.
Putting Our Expertise to the Test
Given Innodata’s experience with small-object data and the growing importance of UAV tracking, I decided to run a series of experiments using the published Anti-UAV benchmark to evaluate how far we could push our tracking pipeline.
The dataset spans a wide range of scenes and sensor modalities, including RGB and infrared video. Drones in the dataset can appear as large as approximately 11,000 pixels in a frame or as small as just 12 pixels. Roughly 69 percent of labeled objects fall in the 1,000 to 5,000 pixel range, about 25 percent are between 500 and 1,000 pixels, and only a small fraction are either very large or extremely small. This long tail of small targets is exactly where many tracking systems begin to fail, making the benchmark a particularly demanding test of both sensitivity and robustness.
Benchmark-Leading Performance on the Anti-UAV Dataset
On Track 1 of the Anti-UAV benchmark, Innodata’s current tracking pipeline exceeds previously reported results on the published test set by 6.45 percentage points. This includes surpassing strong baselines such as SiamSRT (Huang et al., 2024)⁶ and related Siamese-network trackers that have dominated thermal infrared drone tracking in recent years⁶ ⁷ ⁸.
Figure 1. Innodata’s approach compared with other published results on the Anti-UAV Track 1 benchmark.
Figure 2. Detection demonstration across infrared and combined IR+RGB video frames. Full video available via provided link.
On Track 3, our multi-object tracking setup achieves strong performance across accuracy, precision, and recall, backed by thousands of true positives and only a handful of false alarms. In practical terms, the system does not just detect drones. It detects almost all of them, almost all of the time.
Key Performance Highlights (Track 3)¹
- MOTA: 94.76 percent (Multiple Object Tracking Accuracy)
- Precision: 99.74 percent (minimal false positives)
- Recall: 95.01 percent (captures nearly all UAVs)
- Average IoU: 78.87 percent (tight bounding boxes)
- Detection statistics: 2,304 true positives, 6 false positives, and 121 false negatives
¹ Track 3 metrics are evaluated against a sequestered portion of the validation set, as the full test set is not publicly available.
Figure 3. Sample frames from Innodata’s multi-object tracker. Full demonstration videos can be found here for the left, and here for the right.
Real-World Flexibility
In operational settings, benchmark accuracy alone is not sufficient.
The same tracking pipeline described here can be tuned for deployment on SWaP-constrained edge devices, optimized for maximum probability of detection, or configured for ultra-low false alarm rates depending on mission requirements. Whether the use case involves protecting critical infrastructure, monitoring airspace around airports, or deploying on resource-constrained platforms in the field, the system adapts to operational needs.
While the tracking system is primarily focused on infrared channels, it generalizes effectively to RGB and combined RGB+IR sensor configurations, making it suitable for a wide range of deployment scenarios and sensor suites.
What This Means
As UAV threats continue to grow in both civilian and military contexts, reliable high-performance detection and tracking capabilities are becoming essential. These benchmark results demonstrate that Innodata’s approach delivers the level of accuracy and robustness required for real-world deployment, whether securing an airport perimeter, protecting a military installation, or monitoring critical infrastructure.
Interested in learning more about Innodata’s UAV detection and tracking capabilities? Contact Innodata to discuss how this technology can support your specific operational requirements.
References
- Scripps News. Drones linked to most near midair collisions at 30 US airports. April 20, 2025.
- AP News. Drones pose increasing risk to airliners near major US airports. April 22, 2025.
- Hudson Institute. The Impact of Drones on the Battlefield: Lessons of the Russia-Ukraine War. November 12, 2025.
- Arizona Center for Investigative Reporting. Ukraine’s “battle-tested” drones and militarization. December 16, 2025.
- War on the Rocks. Gamified War in Ukraine: Points, Drones, and the New Moral Economy of Killing. January 6, 2026.
- Huang et al. (2024). Searching Region-Free and Template-Free Siamese Network for Tracking Drones in TIR Videos. IEEE TGRS.
- Huang et al. (2022). Learning Spatio-Temporal Attention Based Siamese Network for Tracking UAVs in the Wild. Remote Sensing.
- Wu et al. (2024). Biological Eagle-Eye-Based Correlation Filter Learning for Fast UAV Tracking. IEEE T-ITS.
Bring Intelligence to Your Enterprise Processes with Generative AI.
Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.
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]]>The post Domain-Specific AI: Smarter, Safer, and Built for Your Industry appeared first on Innodata.
]]>Domain-Specific AI: Smarter, Safer, and Built for Your Industry
Today, there is an AI for nearly everything. From simple tasks to solving problems that are unique to your domain, how can AI models improve industry-specific metrics?
What are Domain-Specific AI Models?
Domain-specific AI models are fine-tuned on industry data. Contrary to general-purpose Large Language Models, they are precise, know the specifics of your industry, and context-aware. Generic LLMs often misinterpret specialized workflows, leading to unexpected outcomes and poor performance in high-stakes domains.
So, enterprises need AI models that are trained specifically on quality data and specific industry use cases. Making AI models aware of an enterprise’s context helps them solve common problems in their domain during training itself.
What are the Benefits of Industry-Specific AI Models?
Domain-specific AI models understand niche workflows. When enterprises deploy them, it increases efficiency, trust, and makes the AI models reliable in the long run. Integrating them improves –
- Precision: These AI models understand and use domain-specific language, processes, and reduce misinterpretation.
- Scalability: Serves as a foundation for multiple workflows within a sector. Lowers the cost of ownership by enabling faster retraining and deployment.
- Governance & Trust: Embeds regulatory requirements directly into workflows. Provides auditability and transparency to meet evolving standards.
- Efficiency Across the Value Chain: Faster time-to-value through targeted training and reduced post-processing. Filters noise to identify relevant insights quickly.
- Competitive advantage: Speeds adoption by aligning with industry-specific needs. Unlocks new services and differentiates enterprises in the market.
6 Best Practices for Implementing Industry-Specific AI Models
Building industry-specific AI models needs alignment with business priorities and enterprise-level complexities. The following practices help enterprises reduce risk, accelerate adoption, and maximize long-term value.
1. Define critical use cases before model selection
Identify the business problems where AI can create measurable value and which model is the best fit for your enterprise.
Align model design with ROI and long-term goals to avoid over-engineering generic solutions.
2. Leverage hybrid training data
Combine real-world datasets with synthetic data to capture both everyday and rare edge-case scenarios.
Use synthetic data techniques to address data limitations and unavailability in regulated or sensitive fields like finance or healthcare.
3. Embed explainability and compliance from day one
Incorporate explainable AI (XAI) frameworks that provide transparent reasoning for model outputs.
Include compliance checks to align with sector-specific regulations like HIPAA, GDPR, FINRA, etc.
4. Select the right build strategy
Evaluate whether to fine-tune a pre-trained foundation model or to develop a custom model from scratch.
Decide based on data availability, domain specificity, and long-term scalability requirements.
5. Adopt robust evaluation metrics
Go beyond accuracy to measure fairness, bias, precision, recall, and total cost for real-world reliability.
Set continuous monitoring triggers to detect drift and performance degradation over time.
6. Plan for integration with existing workflows and enterprise systems
Ensure models can plug into existing data pipelines, decision systems, and APIs with minimal disruption.
Design deployment strategies that allow for human-in-the-loop oversight during critical decisions.
Use Cases for Domain-Specific AI Models for
1. Healthcare, Life Sciences & Pharmaceuticals
- Precision Care in the healthcare industry helps professionals develop predictive diagnostics. AI made for healthcare can analyze patient data to help doctors choose better treatments faster, without a lot of guesswork.
- Faster Drug Discovery is possible with accelerated clinical trials and molecular modelling. In life sciences. AI models can shorten R&D cycles by simulating outcomes of the research and designing optimized trials.
- Operational Efficiency in hospitals to enable smarter scheduling and hospital resource planning. Optimized workflows reduce wait times and increase the number of patients that receive care.
- Compliance by Design for built-in adherence to HIPAA, FDA, and data governance. Embedding regulatory requirements ensures trust and safe deployment.
2. Banking, Financial Services & Fintech
- Fraud Protection for real-time detection of suspicious transactions. AI trained on domain-specific patterns helps reduce financial risk.
- Smart Decisions allow for AI-powered credit scoring and risk modeling to protect financial institutions against risk and defaults.
- Customer-First Approach creates hyper-personalized banking experiences. AI models can curate customized product offers and adaptive advisory services to improve client experience and engagement.
- Governed Finance makes explainable and audit-ready decision-making possible. AI outputs can be traced to build stakeholder trust.
3. E-Commerce, Manufacturing & Transportation
- Predictive Uptime by proprietary AI models helps enterprises optimize maintenance cycles and reduce downtime. This helps them anticipate equipment failures before they happen.
- Agile Supply Chains optimize inventory and demand forecasting with AI models trained for logistics. This makes enterprises adaptive to unexpected disruptions and market shifts.
- Smart Operations with domain-trained AI models to optimize inventory management and order fulfillment for faster shipping and improved customer satisfaction.
- Safety & Sustainability enable worker safety monitoring and energy optimization. AI models trained specifically around safety can cut risks and support ESG goals.
Key Challenges You’ll Face (and How to Overcome Them)
Data Availability & Quality
- Enterprises often face fragmented, siloed, or limited datasets that produce biased or unfair outcomes.
- Domain-specific data annotation and bias testing can help create precise, diverse, and reliable datasets to overcome skewed outcomes.
Regulatory Compliance
- Domain-specific rules around data privacy, usage, and accountability make compliance complex and high-stakes.
- Compliance-focused data services build audit-ready, trustworthy models to make AI adoption faster amid strict regulations.
Integration Complexity with Legacy Systems
- Many enterprises rely on outdated or rigid infrastructures that make connecting modern AI workflows costly and slow.
- Custom tooling and platform engineering enable smooth integration with legacy systems.
Ensuring Long-Term Adaptability
- Proprietary or closed architectures can lock enterprises into inflexible systems, limiting innovation as technologies evolve.
- Open standards and portable data pipelines future-proof AI investments and enable scalability.
Does Your AI Model Know Your Industry?
Domain-specific models consistently outperform generic AI in enterprises by delivering higher accuracy, trust, and relevance. They enable multi-modal integration, continuous learning loops, and AI agents with embedded domain expertise that autonomously execute complex tasks.
Partner with Innodata to design, train, and deploy AI models that understand your industry and your enterprise. Connect with an Innodata expert today.
Bring Intelligence to Your Enterprise Processes with Generative AI.
Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.
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]]>The post Implementing AI TRiSM in Agentic AI Systems: A Guide to Enterprise Risk Management appeared first on Innodata.
]]>How to Implement AI TRiSM in Agentic AI Systems for Enterprise Risk Management
Implementing AI TRiSM in Agentic AI Systems: A Guide to Enterprise Risk Management
What happens when AI drives itself? Agentic AI systems introduce new dimensions of risk. Unlike static models that follow predetermined workflows, agents can:
- Generate unique outputs
- Take independent actions
- Evolve continuously
This opens new attack surfaces, trust and compliance risks, making oversight more complex. So, how to make agentic AI secure, reliable, and easy to monitor?
AI TRiSM (Trust, Risk & Security Management) secures agentic AI by enforcing real‑time policy controls, transparent decision trails, and automated compliance. This gives enterprises a competitive advantage by enabling safe, scalable autonomous agents.
This guide shows you how to implement trust, governance, and security management (TRiSM) at every level of agentic AI.
The Five Pillars of AI TRiSM in Agentic AI Systems
- Trust & Explainability
- Risk Management
- Security Management
- Governance
- ModelOps & Privacy
AI TRiSM in Agentic AI Systems
- Trust and Explainability ensure agents act predictably and transparently. Using explainability and immutable logs makes every autonomous decision auditable and aligned with business objectives.
- Risk Management identifies dynamic threats that are unique to agents, like reward hacking, goal misalignment, data poisoning, and coordination failures. Mapping these vectors to real‑time controls can prevent costly incidents.
- Security Management embeds runtime policy enforcement and kill switches into every decision loop. Integrating checks into CI/CD pipelines and ModelOps workflows secures agents from prompt injection and adversarial exploits.
- Governance orchestrates policy design, human-in-the-loop escalation, continuous approvals, and lifecycle oversight. This establishes ethical alignment and prepares agentic AI systems for centralized audits.
- ModelOps & Privacy integrates TRiSM into CI/CD and ModelOps pipelines, ensuring agents remain compliant through updates. Implements data minimization and privacy-preserving techniques to secure agent-generated or exchanged data.
What are the Practical Benefits of Implementing AI TRiSM for Enterprise?
Stakeholder Trust
- Executives need more than technical accuracy; they need trust at scale.
- TRiSM empowers leaders to prove that agentic AI operates safely, even as it evolves.
- Transparent logs and explainability frameworks allow boards, regulators, and customers to see not just what an agent did, but why it acted.
- It reduces fear and enables faster executive buy-in.
Compliance Acceleration
- Airtight compliance accelerates adoption.
- TRiSM aligns agentic behavior with global standards like the EU AI Act and GDPR, turning compliance into growth.
Competitive Edge
- Scalable, precise risk controls create a competitive edge.
- By embedding real-time oversight and policy-aware constraints, enterprises can confidently deploy agents faster, enter new markets, and innovate without compromise.
Proven Outcomes with AI TRiSM
- In multi-agent tests, adding enforcement agents improved safety metrics by 27%, transforming previously uncontrolled systems into governed environments.
- In autonomous anomaly detection studies, runtime policy filters reduced critical errors by up to 45 %. This could help enterprises deploy safer, more reliable agentic AI systems.
Strategies for Implementing AI TRiSM in Agentic AI Systems
1. Dynamic Policy Controls
Enforce policy‑aware action filters at runtime to enforce rules while agentic AI makes decisions. This blocks unsafe actions with great precision.
Deploy runtime governance mechanisms such as rule checks to ensure agents stay within approved boundaries.
This prevents incidents before they happen, preserving autonomy and uptime.
2. Transparent Decision Trails
Fuse explainability tools like LIME or SHAP with fixed logs for full visibility into agent actions.
Tag each decision with agent ID, policy version, and context metadata.
Enterprises can achieve 100% audit trail availability, boost stakeholder trust, and speed up reviews when done with rigorous implementation.
3. Implement Performance Metrics & KPIs
Track false positives vs. false negatives to fine‑tune filters to improve enforcement accuracy.
Measure the time it takes from data capture to report delivery and automate regulatory‑grade report compilation to improve audit readiness.
Monitor anomaly‑to‑alert latency and aim for under 2 seconds for autonomous agent monitoring.
Key Technologies to Implement AI TRiSM in Agentic AI Systems
Observability Platforms
- Observability platforms provide real-time visibility into agent performance, detect drift early, and surface anomalies before they escalate.
- They integrate open-source telemetry into your existing stacks.
- Resolve issues faster, reduce incident resolution time, and support continuous resilience.
- Monitor agent performance at scale with centralized dashboards and runtime health checks.
Policy Engines
- Policy engines apply fine-grained, dynamic rules during runtime to ensure agents operate safely and stay within compliance boundaries.
- Implement domain-tuned policy templates or customize open-source engines inside your security and DevOps workflows.
- Cut policy breach rates, enabling safe autonomy without slowing down innovation.
- Adjust policies as agents learn and evolve, ensuring continuous alignment.
CI/CD Integration Patterns
- CI/CD integration embeds TRiSM checks directly into deployment pipelines, so governance keeps pace with every agent update.
- Use CI/CD playbooks to automate trust, risk, and security validations at every stage.
- Achieve continuous compliance and reduce manual audits.
- Enable safe, rapid rollouts across environments with full alignment between code and policy controls.
AI TRiSM: Ensure Regulatory & Ethical Compliance in Agentic AI
Global Standards
- EU AI Act: Needs transparency, human oversight, and risk categorization for high-risk AI systems.
- GDPR “Right to Explanation”: Requires that decisions affecting individuals be explained and justified.
- US AI Executive Orders: Emphasize safety, security, and public trust in AI deployments.
- India’s AI Bill focuses on responsible AI use and strong data privacy protections.
How to Mitigate Risk and Align Agentic AI Systems with Global Standards?
- Embed compliance checks into TRiSM workflows to turn governance into a proactive strength rather than a last-minute barrier.
- Automate audit trails and policy reporting to meet evolving regional standards without slowing innovation.
- Map agent actions to regulatory requirements to ensure accountability and avoid costly penalties.
Is Your Enterprise Secure & Compliant on Every Front?
TRiSM empowers enterprises to build trust, enforce precision controls, and scale agentic AI safely. As agents evolve, leaders in TRiSM will define the next era of responsible AI.
Discover how Innodata can help secure your autonomous AI systems. Connect with our experts today.
Bring Intelligence to Your Enterprise Processes with Generative AI.
Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.
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]]>Why Did My AI Lie? Understanding and Managing Hallucinations in Generative AI
What happens when your AI confidently lies and nobody corrects it?
AI hallucinations can have serious consequences, so much so that some companies now offer insurance to cover AI-generated misinformation. Hallucinations tend to not only make fabricated outputs that appear credible but also insist that they are correct. In high-stakes industries like healthcare, legal, and finance, hallucinations can undermine trust, compliance, and safety.
To prevent this, enterprises need to understand the ‘Why’ behind AI hallucinations. This makes it easier to address the five main root causes of hallucinations and understand their impact. Enterprises need to thoroughly evaluate their models and prepare to mitigate the impact if they occur.
Why do AI Models Hallucinate?
AI Hallucination refers to when AI models generate outputs that are factually incorrect, misleading, or entirely fabricated. They typically occur due to five broad categories of root causes:
1. Data ambiguity causes an AI model to fill in gaps by itself when the input is unclear or limited. This happens if an AI model is trained on incomplete or flawed data, and leads to overgeneralization or making up information.
2. Stochastic decoding refers to when AI guesses the next word randomly. Even with accurate training data, the AI might generate a likely quote or statistic rather than checking for truth. This occurs because it picks a “likely sounding” word, even if it is not necessarily a factual one.
3. Adversarial and prompt vulnerabilities occur when a poorly phrased or intentionally manipulative input confuses the model. This leads the model to generate offensive, harmful, or nonsensical outputs.
4. Ungrounded generation by an AI model happens when there is no reference point for the model to verify facts. This phenomenon is usually observed in AI models that are trained on static text with no chance for data retrieval. Since there is no verifiable information available, the model generates responses based only on patterns in their training data.
5. Cross-modal fusion errors occur in AI models that handle more than one type of input together. Such AI models could sometimes mismatch them, making up things that don’t exist. For instance, you upload a photo of a dog, but the AI says, “This is a cat wearing sunglasses.” The image and text interpretations get misaligned.
What’s the Impact of AI Hallucinations on an Enterprise?
- Quantified Business Risks: Misleading AI outputs can lead users to abandon a brand after a single error, and a drop in digital revenue streams.
- Qualitative Risks: Spreading misinformation, bias, user manipulation, and irreparable reputational damage.
- Compliance & Legal Costs: In regulated industries, hallucinated instances led to investigations, resulting in fines.
- Innovation Upside: While often framed as a risk, controlled hallucinations can spark creative ideation, when clearly labeled and used in the right context.
Real-World Hallucinations: Case Studies and Mitigation Tactics
OpenAI’s Whisper:
- The medical transcription tool hallucinates dialogues, which sometimes also include imagined medical treatments.
- This could be due to overgeneralization when the model is uncertain, so unclear speech or silence is misinterpreted.
- Human-in-the-loop, strict vocabulary constraints, and employing SMEs to perform domain-specific data annotation could help mitigate this.
Microsoft Tay:
- Microsoft’s chatbot Tay reused toxic and offensive language from users to produce harmful outputs. It had to be discontinued by Microsoft.
- Adversarial user inputs manipulated Tay’s online learning algorithm.
- Input filtering to check what users submit and rate‑limiting to limit the number of user submissions could mitigate this. Using toxicity classifiers to block bad prompts could prevent spam or abuse.
Norwegian User:
- When a user asked ChatGPT about himself, the model mixed fabricated crimes with real personal details. To the question “Who is Arve Hjalmar Holmen?”, it confidently asserted that he had murdered two of his children and was serving a 21‑year sentence.
- Ungrounded LLM generation (extrinsic hallucination), combined with insufficient entity verification leads could cause such defamatory fabrications.
- Entity‑level fact validation using trusted knowledge sources and real‑time web searches before presenting personal data could prevent such mishaps. Using quality training data could reduce the risk of generating extrinsic hallucinations and make the model reliable and fair.
Google Bard Fabricates Science Facts:
- Google’s AI chatbot Bard falsely claimed that the James Webb Telescope was the first to capture an image of an exoplanet. The Very Large Telescope (VLT) was the first to do so.
- The reason could be that the training data it learned before launch didn’t cover everything or had gaps. When there is no retrieval augmentation to verify facts, AI can sometimes get things wrong.
- Integrating Retrieval‑augmented Generation (RAG) pipelines to fetch and cite up‑to‑date scientific publications or databases in real time could ensure that claims are backed by verifiable sources.
Lawyer Cites Non-existent Cases:
- In Mata v. Avianca, ChatGPT generated entirely fictitious legal precedents, leading to potential sanctions for unsupported citations.
- This could be an incident of an extrinsic hallucination driven by the model’s stochastic decoding. LLMs trained to predict plausible text sequences without grounding can generate unsupported but syntactically correct references.
- RAG can ground legal briefs in verified case law databases and automatically validate citations against official repositories.
What to Do if Your AI Starts Hallucinating?
- Contain and Correct the Damage
Audit recent AI-generated content and classify the severity of each hallucination. Publicly correct any misinformation, whether it was customer-facing or public. Immediately notify the affected parties if any decisions were made based on faulty outputs.
- Conduct a Root Cause Analysis
Determine where and how hallucinations occurred, evaluate the AI model, and analyze the process breakdown.
- Improve AI Governance and Implementation Tools
Define where AI can and cannot be used and establish accountability for AI-assisted decisions. Refine prompt engineering to include more constraints and clarify expected outputs. Introduce mandatory human review for sensitive outputs and use dual-validation systems for all important tasks.
- Rebuild Trust with Ongoing Monitoring
Communicate with employees and involved parties about what happened, show accountability, steps taken, and the improvements made. Transparency is important to restore credibility.
- Calibrated “I Don’t Know” Training
Train models to refuse low-confidence queries and embed refusal examples to cut harmful confabulations.
- Controlled Creativity Modes
Provide a “creative” generation setting that relaxes factuality constraints but flags output as speculative, ideal for brainstorming sessions.
Is Your AI Built to be Trustworthy?
AI Hallucinations are a significant risk to enterprise trust, reliability, and reputation. By using quality data for training, proper oversight, and RAG pipelines, organizations can both prevent and correct fabricated outputs.
Is your AI framework equipped to deliver consistently accurate and verifiable insights? Innodata’s Generative AI experts can help you assess your model’s risk profile and implement robust mitigation strategies. Our expertise includes RAG, human-in-the-loop validation, SMEs, and domain-specific, high-quality training data.
Connect with Innodata’s Generative AI experts today to design custom AI services and turn potential liabilities into your competitive advantage!
Bring Intelligence to Your Enterprise Processes with Generative AI.
Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.
Follow Us
The post Why Did My AI Lie? Understanding and Managing Hallucinations in Generative AI appeared first on Innodata.
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