AI engineering
Enterprise AI engineering in Morocco
We design, build and operate AI systems with the rigour you demand of your critical systems: clear architecture, measured results, controlled costs.
In brief
Enterprise AI engineering turns a use case into an operable system: secure architecture, RAG pipelines with cited sources, MCP servers connecting business tools to models, supervised agents, systematic evaluation and cost observability. Hunter BI delivers projects from Morocco in four stages — scoping, pilot, production and handover — with measurable success criteria agreed at the outset.
How we work
Four steps, success criteria signed on day one
Scoping
Define the use case, available data, security constraints and measurable success criteria. Agree the schedule after assessing dependencies.
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
Access layers, identity and data boundaries defined before implementation, with evidence prepared for your security team's review.
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
Defined tools for compatible assistants, with validated inputs, authentication and logging. Client, transport and version compatibility are tested.
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?
The schedule depends on data access, the interfaces available, the scope and acceptance requirements. We agree a pilot and production plan after scoping rather than promise the same duration for every system.
What is an MCP server and why do we need one?
MCP standardises the connection between a compatible assistant and defined tools. The server still needs an authorised interface to the underlying software. Each target client, transport and authentication configuration must be tested; compatibility is not universal.
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.
Start with an architecture that answers the business task
A production system needs more than a model call. We identify the source of truth, the permitted data, the responsible user and the point where an answer becomes an action. The architecture separates retrieval, tool execution, identity and approval so that model behaviour cannot silently expand its permissions.
The initial deliverable is an agreed scope with dependencies and acceptance criteria. It explains what is excluded as well as what will be built. If data quality, an unavailable interface or an access restriction prevents the use case, that finding must be visible before further implementation.
RAG for information, MCP for controlled tools
Retrieval-augmented generation brings relevant material into a response; an MCP integration gives a compatible assistant access to defined tools. They can be combined, but they solve different problems. A document answer needs identifiable sources and a policy for missing evidence. A tool call needs validated parameters and an authorised execution context.
We test retrieval against representative questions, including cases where the corpus does not contain the answer. For connectors, we check refusal paths, ambiguous identifiers, response limits and write approvals. The design should allow the assistant to state a limitation instead of inventing a successful outcome.
Acceptance tests and operating evidence
The evaluation set is assembled with people who know the process. It includes normal requests and important edge cases: stale records, incomplete information, conflicting references and attempts to reach another team's data. Expected results are recorded before the test so that an attractive response cannot redefine success.
A release decision considers quality, latency, running cost and the effort of checking the output. Operations documentation names incident owners, access-revocation steps and the procedure for suspending the integration. We agree the maintenance scope and retest relevant workflows when dependencies change.
Turn a use case into a production system?
Architecture, RAG, MCP, agents, evaluation: let's talk through your AI engineering — from scoping to production.