AI agents that hold up in production.
Agents, LLM-powered features, and integrations, scoped around a measurable outcome and built to survive production.
We build agents that complete tasks end to end, LLM-powered features inside your product, and integrations that connect models to your data and tools. Every engagement is scoped around a measurable outcome: tickets resolved, documents processed, hours saved.
The safety work starts on day one, because that is where agent projects live or die. And when a plain function would do the job better than a model, we will say so. We work with the most capable current models and design so you can swap them as the field moves.
Typical engagements
- AI agent development: task-completing agents wired into your systems
- LLM features inside an existing product: search, drafting, extraction, classification
- Retrieval and knowledge systems over your own data
- Evaluation, guardrails, and monitoring for AI in production
- Model and architecture selection, including when buying beats building
- Integration with the tools and data you already run
How we approach it
We define 'working' in numbers first.
Tickets resolved, documents processed, hours saved — agreed before any code, measured after. Demos are easy. Week forty is the test.
Production is the hard part.
Most agent projects fail after the demo, when real inputs arrive. We build the test sets, the limits, and the monitoring first, because that is where the risk lives.
Model-agnostic by design.
We pick the model that fits the task, often Claude for reasoning-heavy work, and structure code so you can change models later without a rebuild.
Models & tools we work with
Questions
What can an AI agent reliably do today?
Well-scoped, bounded tasks with clear success criteria: triaging and resolving common support tickets, processing structured documents, drafting from your data, and we'll tell you where reliability drops off.
Which model do you use?
Whichever fits the task. We are model-agnostic and often reach for Claude on reasoning-heavy work, and we design so you can change the model later without a rebuild.
How do you stop it going wrong in production?
Evaluations against real cases, guardrails on inputs and outputs, deterministic fallbacks, and monitoring that tells you when quality drifts.
Can it use our internal data?
Yes — through retrieval over your documents and databases, and integrations with the tools you already run, scoped to exactly what the task needs.
Is our data safe?
We work to your data-handling requirements, keep sensitive data out of prompts where it does not belong, and choose providers and deployment models to match your constraints. You'll know the trade-offs before you choose.
Tell us what you're building.
A 30-minute call is enough to tell you whether we're the right fit — and what we'd do first.
17+ years of engineering leadership · Senior-led delivery · Global, remote-first