AI products that hold up in production.
We design and build software where models do real work: agents that complete multi-step tasks, copilots inside your product, search over your own knowledge, and workflows that run without manual handoffs. Every feature ships with the evals, guardrails and cost controls it needs to stay reliable after launch.
Build
What we build
Agents and agentic workflows
Agents that plan, call your systems through typed tools, and hand off to people at the right moments.
Copilots and in-product assistants
Assistants that work inside your product's context and permissions, rather than a chat box on the side.
Retrieval over your knowledge
Search and answers grounded in your documents, tickets and databases, with sources the user can check.
Document and data extraction
Structured data from contracts, invoices, forms and emails, with confidence scores and review queues.
Evals and quality gates
Task-specific test sets, automated graders and regression checks that run on every change to prompts, models or code.
LLMOps and cost control
Model routing, caching, observability and per-task cost tracking, so quality and spend stay visible.
How it runs
From first week to handover.
Durations are typical. We agree the actual plan with you during scoping.
Discovery
1 to 2 weeksWe map the workflow, the data it needs and how success will be measured. You get a scored list of use cases and a build plan.
Prototype on real data
2 to 3 weeksA working slice on your own data with a first eval set. This is where we learn what the model can and cannot do for you.
Production build
4 to 10 weeksIntegration with your systems, permissions, guardrails, monitoring and the full eval suite, released behind flags to real users.
Run and improve
OngoingWe track quality, latency and cost in production and improve against the eval set, or hand over to your team with runbooks.
AI in the loop
How AI changes the way we build it
Evals written before features
We define what good looks like as test cases before writing the feature, so progress is measured rather than demoed.
Model-agnostic architecture
Models sit behind an interface, so you can switch providers or bring a model in-house without a rewrite.
Agents in our own pipeline
Our engineers use coding agents for scaffolding, tests and documentation, which leaves more of their time for design and review.
Human review built in
Low-confidence results route to people with the context they need, and their decisions become new eval cases.
What you own at the end
- Production code in your repositories
- An eval suite with baseline scores
- Dashboards for quality, latency and cost
- Architecture decision records and runbooks
- A ranked backlog of next improvements
Ways to engage
Readiness sprint
A defined question answered: an assessment, an architecture review or a scored use-case portfolio.
Pilot to production
One use case built on your data and released to real users, against success measures agreed up front.
Forward-deployed pod
A small senior team embedded in your business that owns an outcome end to end.
FAQ
Questions we hear.
Which models do you work with?
We work with the major frontier model providers and with open-weight models, and choose per task based on quality, latency, cost and where your data is allowed to go. The architecture keeps that choice reversible.
Can it run inside our own cloud?
Yes. We deploy into your AWS, Azure or GCP accounts and can use private model endpoints or self-hosted open-weight models where data residency requires it.
How do you stop the model making things up?
Grounding in your own data, constrained outputs, citations the user can check, confidence thresholds and human review for the cases that matter. The eval suite measures how often each of these holds.
What if the prototype shows it will not work?
Then you find out in weeks rather than after a full build. Discovery and prototype are priced separately, so you can stop there with a clear answer.
Related
Often paired with.
Transform
AI Modernization of Legacy Workflows
Digitize paper, email and spreadsheet processes and modernize the legacy systems behind them, using AI to read old code, recover business rules and automate the work.
Extend
Forward-Deployed Engineering
Senior engineers embedded in your business who own a problem end to end, from the first conversation with users to the system running in production.
What are we building?
Tell us about the problem. An engineer, not a sales team, will reply.