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I brought up my data warehouse concept and started typing to Genesis in natural language: here's the schema I have in mind, here's the GitHub repo, start building this.
Genesis was rolling out commit after commit on that repo. Two hours later I was done. I had the prototype for the data warehouse. I said: this is the tool we're going to use.
As a digital-first bank, our mission is to make banking better for everyday consumers and small businesses. Leveraging Genesis Data Agents, GXS will be able to bring products to market faster for our customers by reducing our data development cycle from what would’ve taken months to hours.
Over the past year, AI agents have gone from unheard of to one of the key building blocks to increasing AI productivity and unlock insights that deliver real business value. Genesis Computing’s founders have taken advantage of this opportunity by building their autonomous data agents natively on the Snowflake AI Data Cloud, enabling them to accelerate adoption through instant access to thousands of enterprises that depend on the security and governance of the Snowflake platform.
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We’re seeing the demand for intelligent automation in enterprise data workflows skyrocket over the past two years. Genesis Computing’s autonomous data agents deliver precisely what these enterprises need: scalable, production-ready AI knowledge workers that boost productivity and ROI. Matt and Justin are incredible entrepreneurs and have their fingers on the pulse of what these large enterprises need having worked at stellar organizations in the past.
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The modern data stack unlocked unprecedented scale and efficiency for organizations, but the next step will be making AI a first-class participant in agentic enterprise workflows. By leveraging AI Agents, Genesis is enabling businesses to automate their complex data operations. I believe their approach represents the next big shift in the data space.
Genesis is an agentic system that powers AI data workers to execute complete data workflows from start to finish—not AI assistants that just suggest code. Genesis agents research data sources, ingest data, map data from bronze source to gold targets , write code, create documentation, test data pipelines, commit git PRs, monitor pipelines and fix errors while learning from your environment.
Traditional Stack
Genesis Agents
Genesis augments human data engineers, connects to and uses the traditional tools, and fits right into your existing data engineering / pipeline building / CI/CD process with agents that handle the entire workflow.
No. Genesis agents handle repetitive, undifferentiated work (source to target mapping, pipeline maintenance, monitoring, catalog updates) so your team can focus on strategic work (architecture, ML models, business analysis). Think "augmentation" not "replacement."
There are two deployment paths based on your needs:
Option 1: Enterprise Production (Snowflake Native App)
Option 2: Container Deployment (Advanced)
1. Data Engineering
2. Data Operations
3. Business Analysis
Data Platforms:
Orchestration:
Communication:
AI Models:
Yes! Genesis agents:
Absolutely. Genesis uses multi-agent orchestration:
With Snowflake Native App:
Only in Snowflake Native App:
Genesis CANNOT see:
No problem! When installed as a Snowflake Native App, Genesis uses Snowflake Cortex models by default—no data ever leaves your Snowflake environment. Coming soon: Amazon Bedrock support.
Absolutely! Genesis includes:
Genesis leverages your existing Snowflake compliance posture. If your Snowflake environment is SOC 2/HIPAA/GDPR compliant, Genesis inherits those controls by running natively inside your account.
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