AI ENGINEERING
AI Engineering
Generative AI, agents and retrieval systems engineered to run in production — with evaluation, cost control and failure behaviour designed in rather than added after launch.
Most AI work fails somewhere between a convincing demo and a system an operations team is willing to own. The gap is rarely the model. It is retrieval quality nobody measures, permissions applied in a prompt instead of at the data layer, an agent with no defined failure path, and an inference bill that only becomes visible in month three. Xcelerates engineers those parts first.
What an engagement produces
- A working system in your environment, not a notebook
- An evaluation set and the harness that runs it
- Instrumentation for quality, latency and cost per request
- Documented failure behaviour for every external model call
- Handover documentation written for the team that will operate it
Capabilities
What this covers.
- Generative AI and LLM integration
- Language models placed inside an existing product or workflow. Context architecture, structured output with schema validation, defined behaviour when a provider is slow or unavailable, and a per-request cost model you can forecast before launch.
- AI agents and multi-agent systems
- Agents that plan, call tools and hand work to each other. Explicit state, bounded retries, idempotent tool calls, and a human approval point in front of every action that cannot be undone.
- AI copilots and assistants
- Domain assistants that work against your data and your existing permission model — contract review, internal knowledge, operational reporting, engineering support. Scoped to a job somebody actually does.
- RAG and enterprise knowledge systems
- Retrieval over enterprise content with access control enforced at retrieval time, not asked for in the prompt. Chunking strategy, hybrid search, reranking, and citations a reader can follow back to the source document.
- Intelligent automation
- Model-driven steps wired into the systems that already run the business, with the boring parts handled properly: queueing, retries, dead letters, replay, and an audit trail of what the system decided and why.
- Evaluation and monitoring
- An offline evaluation set built from real cases before launch, and online measurement after it, so a change in prompt or model can be measured against something rather than argued about.
Architecture
One system, drawn the way we would review it.
A retrieval-grounded agent with an approval gate in front of anything irreversible. Not every system needs every stage — but every stage here exists because leaving it out causes a specific, predictable failure.
Architecture diagram of a retrieval-grounded agent system, in three lanes.
Request path: a request passes through an input guard, which validates the payload and screens for prompt injection. The planner then selects between two paths. The retrieval path filters by the caller’s access control list before searching, runs hybrid keyword and vector search, reranks the results and returns the top matches; recall at k and citation validity are measured here. The tool path validates arguments against a schema and attaches an idempotency key. Both paths feed synthesis, which composes an answer grounded in the retrieved passages and cites them.
Action path: the output check verifies grounding, schema conformance and policy before anything leaves the system. A reversible action proceeds directly to execution. An irreversible action is routed to a human approval step first, and only reaches execution once approved. Execution then returns the response.
Observation lane: every stage emits a trace carrying tokens, cost, latency, retrieved citations and tool outcomes. Traces are sampled into an evaluation set, and prompt or model changes are replayed against that set before release.
Related
AI Governance & Assurance
Find out what AI is already running in your organisation, what it exposes, and what has to be true before the next system goes live.
Cloud & Platform
Cloud architecture, delivery pipelines, reliability and security-aware engineering — so what gets built can be operated by the people who inherit it.
Next step
Tell us what has to work.
Describe the problem in your own words — the system, the constraint, the thing that keeps not shipping. A senior engineer reads every enquiry and replies with a view rather than a brochure.
What happens next
A reply from an engineer
From someone who could scope the work. Not an automated sequence.
A conversation, not a pitch
Thirty to forty-five minutes on the problem and the constraints.
A written position
What we would do, what it would take, and whether we are right for it.