AI Engineering
Almost every stalled AI project we look at is stuck on the same four questions: who is allowed to see what, where the data physically sits, how you would know if quality dropped, and what it costs at ten times the volume. None of those are model problems.
Outcomes we hold ourselves to.
- A system that still works once permissions are enforced properly
- A way to tell whether a change made it better or worse, before your customers do
- A cost curve that does not rise in a straight line with usage
What we usually find.
Not a sales pitch — these are the things that turn up again and again when we open the estate. If two or three sound familiar, you are in normal territory.
The prototype works because it can read everything. Apply real permissions and half the answers disappear.
There is no evaluation set, so "is it better?" gets settled by whoever is most senior in the room.
Cost was modelled on pilot traffic, not on what happens when the whole company starts using it.
Nobody can reconstruct why the model gave a particular answer three weeks ago — and the auditor has asked.
None of this is unusual, and none of it means anyone did a bad job. Estates accumulate. The work is finding out what is actually there before deciding what to change.
How we deliver it.
AI readiness & use-case selection
Which workflows repay automation, which don't, and what it will cost.
Private LLM platforms
Model gateways, private endpoints, data residency, logging and access control.
RAG & knowledge systems
Ingestion, chunking, embeddings, vector search, reranking and evaluation.
Agentic workflows
Tool-calling agents with human checkpoints, tracing and hard permission boundaries.
MLOps & inference platforms
Training and serving infrastructure, model registry, GPU capacity planning.
AI governance & assurance
Model risk, data lineage, prompt and output controls, and red-teaming of AI systems.
Evaluation & observability
Golden datasets, regression suites, tracing, drift detection and quality scoring in production.
AI cost engineering
Routing, caching, context budgeting and model selection — spend that scales sub-linearly with usage.
Tools & platforms
Senior engineers, start to finish.
The engineer who scopes this work delivers it. You will know the team by name, they will sit in your standups, and they will still be there at the handover.
How we workReady to move on this?
Book thirty minutes with a senior engineer and get a straight answer on scope and cost.