Services/03
AI that survives contact with production.
The gap between an impressive AI demo and a dependable AI system is data engineering. We build LLM-powered agents, document pipelines, and natural-language interfaces with evaluation harnesses, guardrails, and production discipline — and we fix the data layer underneath when that's the real problem.
Signals
You probably need this if…
- 01An AI pilot impressed everyone and then quietly never shipped
- 02You want AI features but your data is too scattered or dirty to feed them
- 03Teams are pasting sensitive data into public chatbots because there's no sanctioned tool
- 04Your engineers want to use AI coding tools like Claude Code, but there's no setup, standard, or guardrails — so adoption is ad hoc and risky
- 05Leadership wants an 'AI strategy' and you need something real to show
What we build
Deliverables, not decks.
LLM agents & workflows
Tool-using agents wired into your systems — Claude, OpenAI, Gemini, Bedrock, or self-hosted models — with structured outputs, evaluation suites, and human-in-the-loop checkpoints where the stakes demand them.
Document intelligence
Extraction, classification, and summarization pipelines for contracts, claims, and forms — with confidence scoring and review queues, not blind automation.
Natural-language analytics
Ask-your-data interfaces grounded in a governed semantic layer, so the answers are right and traceable — not plausible hallucinations.
AI-assisted development enablement
We set up Claude Code and AI-assisted engineering workflows for your team — agent configuration, repo conventions, and review guardrails that turn AI into fast, safe development wins instead of unreviewed sprawl. It's one of the quickest ways to get measurable value from AI inside an engineering org.
FAQ
Common questions
- Why do enterprise AI pilots fail, and how do you prevent it?
- Most fail on the data foundation, not the model — the data the system needed was scattered, stale, or wrong, and a human was quietly compensating in the demo. We consolidate and quality-check the data the use case needs first, then build the AI on top with an evaluation harness so quality is measurable before you scale.
- Which AI models and stacks do you build on?
- Claude, OpenAI, Gemini, AWS Bedrock, or self-hosted models, wired into your systems with FastAPI, pgvector, and RAG — chosen for the workload and its cost profile rather than the hype cycle.
- Can you help our team adopt Claude Code and AI-assisted development?
- Yes. We set up Claude Code, agent configurations, and review guardrails tuned to your codebase, and coach your engineers on the workflows that produce quick, safe development wins — rather than unreviewed AI sprawl. It's one of the fastest ways to get measurable value from AI inside an existing team.
→ Start a project
Have a ai systems problem on the roadmap?
Describe it in three sentences. We'll come back with how we'd approach it, what it likely costs, and whether we're the right team — usually within two business days.