For organisations past the pilot stage, you can hire AI developers from us who embed directly into your team, identifying what to build, building it, and leaving your people able to run it. Dedicated AI engineers or AI staff augmentation, with scope that evolves with your priorities instead of being frozen in a statement of work.
The first AI system a company builds almost always teaches it something that changes what the second one should be. A fixed-scope contract treats that learning as a change request. This does not.
We work out what matters next alongside your team, with the context of what the last thing actually did once it hit real users.
Systems get built when the need is understood, not when a document written six months ago says they should be.
Early wins go into your existing workflows and get tuned on how people actually use them, which is rarely how anyone predicted.
Governance, documentation and training are part of the work, so the systems keep running after we step back.
All four engagement models draw on the same senior AI engineers. The difference is how much of them you need and for how long, from a single dedicated AI developer to a full dedicated AI development team.
One senior AI engineer, full-time, on your product only. Your standups, your tools, your priorities.
The same model at roughly half the hours, for teams whose roadmap does not yet justify a full-time seat.
Two or more engineers with a lead, plus design or QA when the work calls for it. One roadmap, one invoice.
Our engineers embed into your existing team, sprints and codebase. Extra capacity without adding another vendor relationship.
Our engineers join your team, your tools and your rituals, and spend the first weeks understanding how work actually flows.
We identify the bottlenecks worth automating first, chosen partly because they prove value quickly.
Systems go into the workflows people already use, rather than sitting alongside them as another tool to remember.
Performance is tuned against real adoption. What people actually do with a system is never quite what was designed.
Multiple systems in production, with governance and training so your team owns them once we step back.
“We needed a voice agent that could actually qualify leads, not a chatbot pretending to be one. The team shipped a sub-700ms pipeline in 6 weeks. It now handles 5k calls a day.”
“What sold us was their willingness to put AI engineers and product designers on the same call. We got working prototypes by week two and a production rollout in three months.”
“AGI designed a CRM system tailored to our client management process. It is intuitive, reliable, and has centralized all our communication and history in one dashboard. This has greatly improved client retention.”
A project has a fixed scope agreed up front. Transformation does not. Priorities shift as you learn what AI is actually good for in your business, and the engagement is built to absorb that instead of raising a change request every time.
Typically three to twelve months. Shorter than that and there is not enough time to get past the first system; much longer and your team should be running it themselves, which is the intended outcome.
Yes. Your repository, your ticketing system, your standups. The point is to be part of how your team already works rather than running a parallel process.
It runs as a time-and-materials engagement, priced by the size and shape of the team you need. Get in touch and we will put a number against your situation.
Yes. Engagements run from a single dedicated AI engineer to a full dedicated team, typically three to twelve months, and you can start at half capacity and scale up as the work proves itself. Staff augmentation suits teams that already have engineering leadership and need AI-specific capability inside it.
If you are past pilots and want AI to become a normal part of how your organisation works, this is the engagement built for that.