Engineering Review
For an organisation deciding whether an AI use case can move into production.
Typical duration: 2 to 3 weeks
AI engineering
AI has to fit the systems around it.
A useful model is only the beginning. The service also needs data access, permissions, interfaces, testing, monitoring and a named owner in production. We design and build those parts with your engineering team.
What we work on
The model and everything around it.
01
Large language model systems
Model selection, structured outputs and tool use for large language models (LLMs), plus orchestration: how model calls, tools and application logic work together.
02
Retrieval and enterprise knowledge
Retrieval-augmented generation (RAG) lets a model answer using approved company information. We connect it to the documents, databases and internal services a team is allowed to use.
03
Search and indexing
Embeddings are numeric representations that make semantic search possible. We design the indexing, retrieval quality and database behind them.
04
Heuristic optimisation
Optimisation algorithms for search, planning and allocation, where a useful result must be found within real operating constraints.
05
Choosing the first use case
Value, technical feasibility, data access and risk are compared before a team commits to a build.
06
Data and retrieval boundaries
Users and models can retrieve only the data they are authorised to use, and every answer can be traced back to the information behind it.
07
Integration
AI services connect to existing applications, APIs, message flows and workflows.
08
Evaluation
Real tasks become the test set, so it is visible where the system succeeds and where it fails.
09
Human approval
A named person approves high-impact actions. The reviewer sees the evidence behind each one, and escalation is defined in advance.
10
Operation
We measure quality, response time and cost after release, so problems are visible before they become operational failures.
Start with
Start with a focused review.
Specialist Engineering Intervention
For a team stuck on data access, retrieval quality, integration or performance.
Typical commitment: 5 to 10 senior-engineer days
Tell us what the AI service needs to do.
We will identify the applications, data, controls and operational changes needed to put it into production.