AI Engineering
We ship AI in products people pay for, not demos.
Retrieval-augmented generation, agents, automation and on-device inference — built as production systems with evaluation, cost ceilings and failure modes, not as a prompt in a chat box. Three of our own apps run AI features in front of paying users, which is where you learn what actually breaks.
What this includes
- Retrieval-augmented generation (RAG) over your own documents and data
- Vector search: embedding strategy, chunking, hybrid retrieval and reranking
- Agent and workflow design, with tool use and human approval gates
- On-device inference for mobile, where latency and privacy rule out an API call
- LLM evaluation harnesses, regression suites and output grading
- Cost and latency budgets: caching, routing, model selection, token accounting
- Prompt injection defence and data-boundary design
- Workflow automation that connects the models to the systems you already run
Typical stack
- TypeScript
- Python
- OpenAI
- Anthropic
- pgvector
- LangGraph
- Core ML
Proof, not claims
Our own products that rely on this capability
Need ai engineering?
Tell us what you are building. An engineer replies, usually within one business day.