We build AI agents, knowledge retrieval systems and evaluation harnesses into the workflows you already run, so your team gets hours back without learning a new tool.
We implement AI across the systems you already run
Most of what we build is not the model, it is the software around it, and it goes where your team already works.
The AI we build with
We add AI features and agent workflows to the platforms teams already run, and we build AI-native products from the ground up. Small pieces, or the whole system.
Most clients start by automating the workflows they already run, then come back for custom software built around their business.
Outlook, your CRM, Teams, whatever you track work and clients in. The time usually goes missing in the gaps between them, where somebody is reading one system and retyping it into another. We connect those systems and put an automation pipeline in the gap. We start with a single workflow small enough to ship, prove it holds up on your real data, then widen it from there.
Web platforms, mobile apps, and the architecture underneath them. When the tool you need does not exist, we build it around your workflow instead of asking you to bend the workflow around somebody else's product, with the AI part designed in from the start rather than bolted on at the end. You get what your work actually needs and nothing else.
Before anything is automated we work out what your regulations actually allow: what may be processed, where it is allowed to run, and who has to sign it off. Putting AI into production and staying on the right side of the rules are the same job, so we design them together rather than asking for approval once it is already built.
Secure storage, GDPR compliant, and access limited to the people who need it. We keep the least data a job requires, we are explicit about where it lives and how long it stays, and privacy is a constraint we design around rather than a form we fill in at the end.
Everything we build is backed up, and changes reach production through a pipeline that tests first and can be rolled back if it misbehaves. Your team hears what changed and when, so nobody meets a new behaviour by walking into it on a Monday morning.
Two questions come before any build: what to try first, and what it should all add up to.
We build with the current generation of AI engineering tools, Claude Code, Codex and MCP servers wired into our own stack, which is what puts a real system in front of you in days rather than quarters. Fifteen years of ordinary software engineering is what makes that safe: the speed comes from the tooling, and the judgement about what is worth shipping comes from having run production systems long before any of this existed. Pick one process rather than a whole department, and let a working version tell you whether the idea holds.
Most companies do not need AI everywhere. They need it in the few places that pay, in an order that makes sense, and with a reason for that order they can defend internally. We work that out with you: what your industry and your regulator allow, where the return is clearest, how much change your team can absorb at once, and what the end state should look like so every step points at it. You get a starting point and a direction, not a shopping list.
Questions
No handoffs, tight scoping, and few concurrent projects by design. We scope one workflow, not a platform, and senior engineers build it directly, so we skip the layers that slow most agencies down.
Yes. We work in staged 10-day increments: each stage ships something working, and we expand from there. You’re never betting six months on an unproven idea.
You do. We build production code you keep, with docs and evals.
We tell you, on Day 0, before you have spent anything. A scope call that ends in “this doesn’t need AI, here is the cheaper fix” is a good outcome for both sides, and it happens often enough that we plan for it.
We scope the data boundary explicitly on Day 0 and build to it. Depending on your constraints that means hosted model APIs, a cloud provider’s models such as AWS Bedrock inside your own account, or fully self-hosted models, and we will tell you plainly what each option costs you in accuracy and money.
Yes. We connect to most systems directly, and where access has to stay in your own team’s hands we work alongside your IT people rather than around them, so nothing gets opened up that should not be. We can also start on mock data that mirrors your real structure, which gets the process right before a single live record is involved, and you can then run live testing yourselves in a sandbox. Whichever route fits, the data boundary is agreed on Day 0 and built to.
You decide: scale it to the next workflow, take the code and run it yourselves, or stop. There is no retainer you have to opt out of and no lock-in. Most teams who continue do so one 10-day stage at a time.
Bring one workflow to a 20-minute call. Ten days later, it runs itself, a working AI system built on your own data.
