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Overview
What we actually do here.
We build AI that ships to production and earns its keep — retrieval systems grounded in your data, custom integrations inside your product, and the evaluation harnesses that keep them reliable. No demos that fall apart in the real world; systems engineered to run and be maintained.
What’s included
Everything this discipline covers.
Retrieval-augmented generation (RAG) on your data
Custom LLM integrations inside your product
Evaluation harnesses and quality monitoring
Data pipelines, embeddings, and vector infrastructure
Guardrails, safety review, and prompt engineering
Deployment, observability, and cost optimization
How we work
Our approach.
A clear sequence, run the same way for every engagement so nothing falls through the gaps.
01 /
Scope the use case
We separate what AI genuinely improves from what it just complicates, and define success in measurable terms before writing code.
02 /
Ground it in your data
Retrieval, embeddings, and pipelines that connect the model to your real information — so answers are accurate and specific to your business.
03 /
Evaluate relentlessly
We build evals and monitoring up front, so quality is measured, regressions are caught, and you can trust the system as it changes.
04 /
Ship & maintain
Production deployment with observability and cost controls, plus documentation so the system holds up long after launch.
Deliverables
What lands in your hands.
- A production AI system integrated with your stack
- A grounded RAG or model pipeline on your data
- An evaluation harness and quality dashboard
- A security and safety review before launch
- Architecture docs and runbooks for your team
Why it works
What it changes for you.
01
AI that survives contact with reality
Evals and grounding mean the system works on real inputs, not just the happy path in a demo.
02
Your data, your advantage
Retrieval on your own information makes the output specific and defensible — not generic model boilerplate.
03
Safe and observable
Guardrails, monitoring, and a security review mean you can put AI in front of customers with confidence.
Questions
Good to know.
Still curious? The chatbot in the corner answers instantly — or book a call.
Yes. We use zero-retention model providers, keep data in your environment where possible, and run a security review on every system before launch. Your data trains your system, not someone else's model.
We build evaluation harnesses up front — measuring quality against real examples — so accuracy is a number we track, not a hope. Monitoring catches regressions after launch too.
Almost always the latter. Most value comes from integrating strong existing models with your data and workflows. We only build custom models when there's a clear reason the off-the-shelf options can't meet.