AI practice
AI systems, built to production standards
We integrate language models, retrieval, and agent workflows into systems that already have users, compliance requirements, and uptime expectations. The interesting problems are never the model.
What we actually do
Integrate models into existing products.
Most clients don't need a new AI product. They need AI inside the system they already run, without destabilising it.
Build retrieval that returns the right thing.
Chunking, embedding strategy, hybrid search, reranking, and evaluation. Retrieval quality determines output quality far more than model choice does.
Ship agent workflows that fail safely.
Tool use, orchestration, human checkpoints, and explicit behaviour when the model is confidently wrong.
Control cost and latency.
Token budgets, caching, model routing, batching. The difference between a viable feature and one that gets switched off at the end of the quarter.
Evaluate continuously.
Test suites for probabilistic systems. Regression detection when a provider silently changes a model under you.
Tell you what not to build.
A meaningful share of AI proposals should be a database query, a rules engine, or nothing. We'd rather say so in week one.
Book a technical call
Talk to an engineer about your architecture, constraints, and whether an LLM is actually the right tool for the job.
Start a project