one workflow, not an AI strategy
We take on a specific job your team does today. Not a platform, not a transformation programme.
We build AI systems for slow, manual, knowledge-heavy work — then hand your team the code, the tests, and the runbook.
We take on a specific job your team does today. Not a platform, not a transformation programme.
Your permissions, your data, your handoffs. The AI goes where the work already happens.
One team does the product design, the AI engineering, the integrations, and the cloud setup.
most AI projects
die as demos.
Work out which AI project is worth doing first, and why.
Products built on language models, tested against your real cases.
Answers drawn from your own documents, with the source attached.
Multi-step work that runs without somebody remembering to start it.
The web application and interfaces the whole thing runs on.
Hosting, deployment, and monitoring your own team can operate.
Read what came in, gather the context around it, and draft the reply for a person to send.
Pull the fields you need out of invoices, forms, contracts, and scanned files.
First drafts of proposals, summaries, and recurring reports, ready for someone to edit.
Answers from your own policies and past cases, with the source and its review date shown.
Assemble the evidence, check it against the rule, and flag what a human must decide.
Watch a source, notice what actually changed, and open the exceptions as work.
Quality is tested against a task-specific benchmark before release.
Teams can see cost, latency, failures, drift, and user outcomes.
Permissions, escalation, and human review live inside the workflow.
Your team gets the code, infrastructure, documentation, and runbook.
A prototype only has to work once; production has to keep working after we leave.
You need a clear opportunity, a justified architecture, and an honest path to production.
You have evidence of value but not the controls, integrations, or operating model to release it.
You need a senior product-engineering unit that can own a hard stream and leave the system stronger.
Provider-flexible by design. The stack follows the operating constraint, not a preferred logo.
Every engagement is scoped and priced after the free thirty-minute call. You see a written price for a defined stage before any work starts.
The code, the infrastructure definitions, the tests, the documentation, and a runbook. Your team can deploy a change without asking us how.
Usually. We define the minimum trustworthy data path, test it, and make the gaps explicit in the roadmap.
Where they are sound, yes. Replacing familiar tools without a clear operating reason creates risk instead of value.
A useful vertical slice should appear in weeks. The exact sequence depends on access, risk, and the workflow being changed.
We can operate alongside your team, transition ownership, or continue into the next proven scope. The handover is designed from the start.
Map the work, evidence, constraints, and the decision AI is meant to improve.
Put one end-to-end slice in front of real users and measure the hard assumption.
Add tests, observability, governance, documentation, and a clear operating owner.
Thirty minutes, no charge. Bring the workflow that is stuck.
Bring the workflow that is slow, fragile, or too dependent on a few people.