AI inside your product. Done right.
RAG systems, agentic workflows, on-prem LLM serving, function-calling pipelines. We build AI features that hold up in production — not demos that hold up on Twitter.
If the AI breaks, your product still works.
RAG over your own data
pgvector or Qdrant, chunking that actually respects the document, citations on every answer, evaluation harnesses so you know when retrieval regresses. No vendor lock-in to a managed vector DB you can't audit.
Agentic workflows that don't loop forever
Tool-using agents with budgets, timeouts, escape hatches, and an audit log of every call. We use Anthropic's Claude with MCP servers, OpenAI's tool-calling, or whatever the right shape is for the job.
On-prem LLM serving
vLLM on your GPUs, model selection, quantisation, batching, observability. For regulated industries that can't ship customer text to OpenAI or for cost reasons when volume is high.
The boring parts of AI ops
Prompt versioning, eval harnesses with real production traffic, regression detection, prompt-injection defences, PII redaction. The stuff that's the difference between "demoed it" and "running it for two years."
It's a phone call. That's the worst it can get.
No discovery deck. No 45-minute "qualification" call. 30 minutes, your problem, my opinion. If we're a fit, you'll know by minute 12.