AI features.

Production AI. Not pilot projects.

We've shipped AI in production since the GPT-3 era — across financial services, healthcare, and global SaaS. Agents that complete multi-step work, retrieval grounded in your business knowledge, fine-tuning when prompting isn't enough, and the eval harnesses that make 'better' a number you can defend.

AI in production is not the same problem as AI in a demo. Cost, latency, accuracy, and safety all need to be measured, monitored, and bounded. We build with that reality from the first prompt.

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What you get

Deliverables, every engagement.

The things you can expect to receive — and own — by the end of a ai features project.

LLM agents and tool-using systems
RAG over your private knowledge bases
Eval harnesses and prompt A/B testing
Fine-tuning pipelines (SFT, DPO)
Computer vision and document AI
Cost monitoring, fallback routing, prompt caching

How we deliver

A repeatable path, tuned to your scope.

Define quality

Before we ship, we agree what 'correct' looks like — and write the eval set to measure it.

Build behind the eval

Every prompt, model, and architecture change is graded against the eval before it hits production.

Ship behind a flag

Rollout to 1% of traffic, then 10%, then 100%. Monitor accuracy and cost in real time.

Iterate on data

Real-world failures get added to the eval set. The system gets measurably better month over month.

Common projects

Where ai features usually shows up.

Compliance and review copilots
Customer-facing assistants and chatbots
Document and image understanding
Internal automation and triage
Generative product features
Search and recommendation

Tools we reach for

Tools, not religions.

A snapshot of what we typically use. We pick the right tool for your team, your scale, and your stack — not the latest framework on the front page of Hacker News.

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Industries

Financial services · Healthcare · SaaS · Legal

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