AI engineering
AI features that are accountable, not just impressive
We build AI into products where it earns its place — and we keep the decisions that matter out of the model. Language models are excellent at extracting, summarising and explaining; they are a poor choice for anything that must be correct every time.
What we do
How we approach AI engineering
Assistants and chat interfaces
Product-aware assistants grounded in your own data, with the retrieval layer built so answers can be traced back to a source.
Retrieval and semantic search
Vector search over your documents and records, so the system answers from what you actually hold rather than from the model’s memory.
Model Context Protocol servers
Custom MCP servers that give assistants safe, scoped, auditable access to your systems instead of a blanket API key.
Deterministic decision layers
Where eligibility, pricing or compliance is at stake, the rules live in tested code. The model explains the outcome; it never decides it.
Content and workflow automation
Generation and triage pipelines with a human review step, because unreviewed output is how AI features lose trust.
Evaluation and cost control
Prompt and output evaluation in CI, plus token budgeting and caching, so quality is measurable and spend is predictable.
What you get
Deliverables
- Working AI feature integrated into your product
- Retrieval pipeline over your own data
- Evaluation suite covering the cases you care about
- Cost and usage monitoring
Have a project in mind?
Tell us what you are building and we will come back with a scope, a timeline and a price.
Get in touch