AI integration that ships.
We build assistants, retrieval workflows, and automations into products that already have users — with evals, cost controls, and a clear owner for every failure mode.
What we build.
Assistants that answer from your data
Support and internal assistants grounded in your documents, tickets, and product data — not generic model knowledge. Scoped to what each user is allowed to see.
Retrieval workflows
Ingestion, chunking, embeddings, and hybrid search wired into a pipeline that stays fresh as your content changes. Answers cite the source they came from.
Workflow automation
The repetitive middle of a process — triage, extraction, summarisation, routing — handed to a model with clear guardrails and a human checkpoint where it matters.
Evals before launch
A test set built from your real cases, scored on every prompt or model change, so quality is a number you can watch rather than a feeling.
Process
How an AI build moves.
Scope
One week to find the workflow worth automating, agree the success metric, and rule out the ideas that only look good in a demo.
Prototype
A working slice against your real data — enough to judge quality, latency, and cost before anyone commits to a build.
Harden
Evals, fallbacks, rate limits, cost caps, logging, and access rules. This is the part that separates a demo from production.
Ship & watch
Rollout behind a flag, dashboards for usage and spend, and a review loop so the system improves on evidence rather than vibes.
Tools we reach for.
- OpenAI
- Google Gemini
- Anthropic Claude
- Vector search
- Postgres / pgvector
- n8n
- TypeScript
- Python