AI transformation for professional services.

We price against the specific build, on the call, once we both know what we are solving. Monthly rolling, no annual contract, no setup fee, no minimum term. You can stop whenever it stops being worth it, and we would rather you did than sit on a retainer you have stopped valuing.

Most sell a project: one chatbot, one workflow, one dashboard, and nothing underneath it that the next project can use. We architect the layer every agent needs once, then prove it by running live agents on top of it. The people building it have run businesses, not just advised them.

The three to five agents we agree with you, and the shared layer those agents need in order to work. Nothing else. The parts of the foundation your first agents do not require get written down and left alone until an agent genuinely needs one. That is what keeps the first build contained, and it is also how you can tell afterwards whether it worked.

They own the decisions, we do the build alongside them. Your IT and security people set what agents may connect to and what they are never allowed near, and they review it before anything is connected. If you already have an internal AI team, they usually take the agent work while we take the layer underneath, which is the part that is hardest to justify building for any single project. Everything is documented as we go and handed over. If your engineers own part of the stack we are touching, they still own it after we leave.

Yes. Customer-facing AI runs on the same foundation as your internal agents: the same company knowledge, the same connections, the same identity and approval rules. It is built on your codebase and it stays yours.

Scope is tied to the named agents. If a piece of work is not required by one of the first agents, it does not go into the build, it goes onto a list. That single rule removes most of the drift on its own. Beyond it: the architecture is agreed in writing before the build starts, each agent has an owner on your side, and every stage ends with something running in production rather than a document about what could be.

Only if you choose to. We run them with you, and what comes next gets chosen from what actually happened in production rather than from the original wish list. The second wave is quicker, because the knowledge, the connections, the identities and the checks are already in place. Some companies keep us on to build the next set. Some take it in-house, which is a fair outcome and the one the handover is designed for.

You are ready if the company already runs on proper systems and AI is turning up in several teams at once. Usually there is a founder or CEO who can see what AI should be doing, a few teams who have started on their own, and nobody who owns the architecture underneath. You do not need an existing AI team to start. You do need somebody senior who can decide what agents are allowed to touch.

Generative Engine Optimization is the practice of making your business clear enough for AI answer engines like ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews to understand, trust, and cite. SEO still matters, but GEO adds answer-first pages, structured data, consistent definitions, and credible external signals that help AI systems know when your company belongs in the answer.

Every agent gets its own named identity rather than a shared login, and it inherits the same limits a person in that role would have. Wherever a mistake would cost you money, a customer or your reputation, a named person approves before anything leaves the building. Those approvals happen inside the tools your team already uses, so nobody has a new queue to check. Every action is logged against the identity that took it, so the question of who did what always has an answer.