Production AI Pipeline — automatic classification of high-volume unstructured text into reliable, auditable, executive-ready data.

This project is an evolution of a data-driven processing pipeline for reading, interpreting, and automatically classifying large volumes of unstructured text.

Designed for scenarios where operations need to:

• Identify critical events across high volumes of text documents
• Transform natural language into structured, persistent data
• Deliver results in a continuous, auditable, and resilient flow
• Convert operational output into executive-level analytical reports

The technical value lies in four pillars:

• Batch processing with controlled orchestration — avoiding context overflow and bottlenecks in AI calls
• Database-first architecture — reducing coupling with intermediate files and increasing transactional consistency
• Inference layer with observability — latency tracking, token consumption, and full execution trail
• Executive analytics generation — turning operational output into management-level dashboards

This is not just an LLM classification script. It's a production pipeline oriented around reliability, scale, traceability, and analytical reuse.

flowchart TD
    A[Scheduler / Manual Trigger] --> B[Pipeline Orchestrator]
    B --> C[Read Pending Items from DB]
    C --> D[Safe Batch Assembly]
    D --> E[Structured Prompt for LLM]
    E --> F[Inference Layer]
    F --> G[Response Validation & Sanitization]
    G --> H[Idempotent Persistence to DB]
    H --> I[Technical Logs & Usage Metrics]
    H --> J[Consolidated Base for Consumption]
    J --> K[Analytical Report — PDF]

    F -. fallback / multi-provider .-> L[Alternative AI Provider]
    G -. parsing failure .-> M[Controlled Error Flagging]
    M --> H

The solution separates responsibilities into four clear blocks:

• Orchestration — controls execution targets, batch size, pauses, and fault tolerance
• Infrastructure — centralizes DB connection, engine caching, and AI provider communication
• Processing rules — fetches pending items, prepares payload, sanitizes response, persists results
• Analytics — transforms consolidated data into a visual tracking report

This design favors incremental evolution, simplifies maintenance, and reduces the cost of future changes.

The pipeline needed to handle recurring input volume without manual item-by-item execution.

How I solved it:

• Execution driven by processing targets per cycle
• Small, safe batches with pauses between cycles
• Clean interruption when the queue is empty

Generative models are excellent at interpreting text but are not naturally reliable as structured write interfaces.

How I solved it:

• Prompt strictly oriented to JSON output
• Defensive extraction of the returned array
• Boolean and text field sanitization
• Controlled error flagging when output is incomplete or invalid
• Idempotent persistence to support safe reprocessing

In real pipelines, cost, latency, and unavailability can halt the entire flow.

How I solved it:

• Inference layer decoupled from business logic
• Multi-provider support with architectural fallback
• Technical logging to compare behavior across models

Most pipelines end when data is written to the database. Here, the goal was to go further — turning operational data into executive-level insight.

How I solved it:

• Analytics module built on Pandas
• Automated PDF dashboard generation
• Visual synthesis of most relevant classifications
• Direct reuse of the consolidated base — no manual rework

Database-first over intermediate files — spreadsheet-based pipelines lose traceability and create version divergence. A DB-first approach ensures consistency, auditability, and lower friction in production.

Explicit orchestrator over monolithic script — separating execution control from processing logic improves observability and simplifies error recovery and resumption.

Idempotent persistence over blind inserts — reprocessing is inevitable in AI pipelines. The system accepts repetition without duplicating side effects, making corrections safe and predictable.

Post-LLM sanitization over trusting raw output — generative responses can include noise, unexpected formatting, or partial omissions. An explicit sanitization layer reduces production failures.

Analytics coupled to consolidated base — manual reports create lag and duplicate effort. Automated PDF generation delivers executive insight directly from the same operational pipeline.

• 60–85% reduction in manual reading effort through automatic text classification
• 70%+ reduction in time between data input and structured output availability
• Increased operational reliability with explicit failure handling and safe reprocessing
• Full observability with latency, token usage, and AI call status logging
• Operational data converted to executive intelligence via automated analytical report

1. Add a real async queue — evolve to distributed workers with async queuing for higher throughput and better operational control

2. Typed output contract validation — use typed models and formal schema validation at the inference boundary (e.g. Pydantic)

3. Dedicated observability stack — move logs, metrics, and tracing to a centralized telemetry stack for historical analysis and production troubleshooting

4. Automated prompt regression tests — fixed test cases to detect semantic regressions when switching models, prompts, or heuristics

5. Expose analytics as a web application — a web dashboard with filters and time series would significantly increase the product's value beyond PDF reports

pip install -r requirements.txt
python src/pipeline.py

Environment variables, database credentials, and integration details have been intentionally omitted for this public portfolio version.

Marcelo Manara — Software Engineer | AI Systems · Cloud · Python · AWS