AI engineering
From architecture to production.
We design, build and operate AI systems with the rigour you demand of your critical systems: clear architecture, measured results, controlled costs.
How we work
Four steps, success criteria signed on day one
Scoping
Two to four weeks to pin down the use case, available data, security constraints and measurable success criteria.
Pilot
A narrow scope, real users and an evaluation set: the pilot proves the value — or disproves it — before any heavy commitment.
Production
Security hardening, scaling, observability and operating procedures: the system joins your IT estate with its runbooks.
Handover
Documentation, training your teams and tapering support: you operate on your own, we stay on call.
Capabilities
What we build
The engineering behind a reliable enterprise-AI system.
Architecture
Secure, evolvable foundations — access layers, identity, data partitioning — defined before the first line of code. Passes your CISO's audit.
Agents
Governable multi-agent systems — planner, specialists, verifier — with explicit action scopes, full logging and a human in the loop where it commits.
MCP servers
Your systems, cleanly accessible to AI: typed, authenticated, logged connectors that serve Claude, ChatGPT and your agents alike.
RAG & knowledge graphs
Answers grounded in your documents, with cited sources — no answer without a verifiable reference.
Evaluation
Measure before you believe: test sets built with your experts, fidelity and coverage metrics, systematic model comparison.
Observability
Latency, tokens, cost per use case, share of sourced answers — every request traced end to end. You see what your AI costs and where it drifts.
FAQ
Frequently asked
How long does it take to get an AI system into production?
Count eight to twelve weeks from scoping to a pilot evaluated on real cases, then six to ten more for industrialisation — security hardening, observability, operating procedures. The projects that drag are almost always the ones that skipped the evaluation step.
What is an MCP server and why do we need one?
The Model Context Protocol is an open standard that lets AI models reach your systems — ERP, CRM, document bases — through typed, authenticated and logged connectors. An MCP server is built once and serves all your assistants and agents, whatever the underlying model.
How do you limit hallucinations in production?
By design rather than by promise: answers grounded in your documents with mandatory source citation, evaluation sets built with your domain experts, confidence thresholds below which the system abstains, and continuous tracking of the sourced-answer rate in production.
Do you work with OpenAI, Anthropic or both?
Both — Hunter BI is a member of the OpenAI and Anthropic partner networks. We recommend the model that scores best on your evaluation set, and we design architectures that let you switch without rewriting your integrations.
Turn a use case into a production system?
Architecture, RAG, MCP, agents, evaluation: let's talk through your AI engineering — from scoping to production.