AI solutions built on your stack
Semantic layers, RAG, and production LLM workflows — wired directly into Snowflake, dbt, and Fivetran. No rip-and-replace, no vendor sprawl. Faster to value, lower risk, and your team already knows how to run the foundation.
Scope an AI solutionWhat we deliver
Semantic layer implementation
Define revenue, churn, conversion, and other metrics once in the dbt semantic layer (or equivalent) so every dashboard, agent, and AI tool reads the same truth — no conflicting numbers across tools.
RAG and retrieval architecture
Pipelines that chunk, embed, and retrieve your documents and structured data so LLMs answer with your internal context — not generic training data. We design ingestion, vector stores, and guardrails for production use.
Governed LLM integrations
Not “paste an API key into ChatGPT.” Role-based access, audit trails, and quality checks so answers respect who can see what — and you can explain outputs to compliance and leadership.
CI/CD for data + AI artifacts
Models, prompts, and pipelines versioned and deployed like software — so changes are reviewable and rollbacks are possible when something drifts.
Why “connect ChatGPT to the warehouse” fails
A generic LLM pointed at raw tables has no single definition of metrics, no enforced access rules, and no guarantee that answers match what finance or ops already trusts. That's fine for a demo — it's not production AI.
We build the layer that makes AI systems accurate, governed, and operable: semantic definitions, retrieval you control, and security that matches how you already run data — not an afterthought.
RAG when you need it — not by default
RAG (retrieval-augmented generation) lets models use your documents and records for grounded answers. If you need company-specific Q&A over policies, support history, or knowledge bases, RAG is usually the right pattern.
If your use case is mostly structured prediction or reporting, a strong warehouse model may be enough. We scope what you actually need in the first strategy call — no surprises.
Security-first delivery
Same security posture as our AI readiness work: access controls and auditability designed in, not bolted on after launch.
Ready to scope timeline and investment?
Focused RAG or semantic work often lands in 4–8 weeks; larger multi-system programs typically 8–16 weeks. We nail scope on the first call.
Book a strategy call