We build LLM applications that answer from your own data, act through your own systems, and are tested before every release like any other production software.
Accuracy0.93
Groundedness0.95
Safety0.99
Tone0.88
All checks passed · ready to ship
What it means in practice
- Retrieval-augmented generation (RAG) over your documents and data, with sources shown for every answer.
- Agents that call your tools and APIs, with clear limits on what they are allowed to do.
- Evaluation suites that score answers for accuracy, grounding and safety before each release.
- Guardrails for personal data, company policy and prompt injection.
What you get
- A working application in your cloud account, with the code in your repositories
- An evaluation dataset and an automated test suite
- Guardrails, monitoring and cost tracking
- A runbook and a handover to your team
Typical stack
- Python
- Amazon Bedrock
- OpenAI and Anthropic APIs
- Vector search (OpenSearch, pgvector)
- Evaluation tooling
We work with the tools your teams already use.
How it runs
- AssessWe review your architecture, data, goals and constraints, and agree what success looks like.
- DesignTarget architecture, a delivery plan and the measures we will be judged on.
- BuildShort iterations, working software early, built alongside your team in your accounts.
- RunWe operate it to agreed service levels, improve it, or hand it over with runbooks.
Tell us where your AI is stuck.
One conversation with an engineer. No sales script.