Your whole team,
working with AI.

There is no price list, because the honest number depends on team size and how much we take on. The shape is the same for everyone: a one-time engagement for the readiness audit, the workshops and the first automations, then a monthly fee for ongoing support once the tools are in daily use. The audit produces a prioritized 90-day plan with a baseline, and we agree what to measure before any spend: time saved per task, how fast the automations run, how many people actually use them. We work on the subscriptions your team already pays for where we can, and any metered API spend is capped and shown up front. If you want AI features inside your product rather than inside your team, that is scoped separately under our Build track. Send us your team size and the workflows you want to target, and we send a written proposal within two business days.

An AI readiness assessment is a structured look at whether your workflows, tools, data and team can absorb AI before you spend on it. We map how work moves through the company, find where AI returns real hours, and check what is in the way: messy data, an unclear process, or a team that has not been trained on the tools it already pays for. The output is a prioritized 90-day plan with a baseline to measure against, not a maturity score. It names the one or two automations to build first, chosen from the tasks people already dislike, and the training the team needs to use Claude, ChatGPT, Gemini or Copilot properly. The assessment is the first step of every engagement here, and it is where the monthly report a client receives later gets its baseline. Without it, nobody can say afterwards whether the rollout worked.

Using ChatGPT is not a strategy. We look at how your team actually works, find the places AI makes a real difference, train the people who will use it, and build the automations that stick. The difference is outcomes you can measure, not just access to a tool.

We start with the tasks they already hate, not the tool. The first wins are time they get back. Nobody gets replaced by a prompt. The teams we have worked with end up defending their AI tools, not hiding from them.

No. We use Claude, ChatGPT, Gemini, Copilot, and whatever fits the job. If a tool is wrong for you, we tell you to skip it.

No. LLMs power the tools we train your team on (Claude, ChatGPT, Gemini, Copilot), but your team does not need to understand the model to get value from the workflow. We handle the technical side, so your team can focus on the work.

We agree on what to measure before we start. Time saved per task, how fast the automations run, how many people actually use them. You get a monthly report against the baseline from the audit.

Both. Internal rollouts are one engagement. If you want AI features inside your product, that lives under our Build track and we scope it separately. Same team, different contract.

First wins in the first 30 days. A full rollout across a company takes a quarter, not a year.

Yes. Some clients keep everything inside their own company. Others run their own AI models on their own servers. We match the setup to your data rules.