AI business consulting tied to the P&L.

How an engagement actually runs

Week one is an audit, not a workshop. We sit inside your real workflows: the inbox where leads land, the spreadsheet where the numbers live, the CRM nobody updates. Out of that comes a short list of automation candidates, each scored on one axis: hours saved or revenue recovered per month, divided by build cost. McKinsey's State of AI survey found 78 percent of organizations now use AI in at least one function, but only around 1 percent of leaders describe their rollout as mature. The gap is not access to models. It is picking the wrong first project.

Then we build the top candidate. Not a pilot in a sandbox: the real workflow, wired to your real tools, with a human approval step anywhere money or messages leave the building. You see working software inside the first month, because a recommendation you cannot click is just an opinion.

Handover is part of the scope, not an upsell. You get the code, the prompts, the documentation, and a session walking your team through how to run and extend it. We stay available, but you are not renting your own system back from us.

What it costs, in plain numbers

Most of our consulting engagements land between five and thirty thousand dollars depending on integrations, data condition, and how many workflows we touch. An audit alone is less. A multi-system build with custom interfaces is more. We price fixed-scope: the number is agreed before work starts, and the full breakdown of what drives it is published in our AI consulting cost guide.

The cheaper path is the one most firms skip: try the workflow yourself first. Our build library has sixty free, runnable versions of the systems we sell, finance packs, diligence checklists, reporting stacks. Run one this weekend. If it moves a number, the paid engagement is wiring it into your stack properly.

What an AI business consultant actually does, week by week

An AI business consultant is not a researcher and not a vendor reseller. The job is to sit with the people doing the work, find the workflow that burns the most hours or leaks the most revenue, and put a number on it before anyone writes code. In week one that looks unglamorous: reading your inbox rules, opening the spreadsheet three people maintain by hand, watching how a lead actually travels from form fill to first reply.

Week two is the honest part. Some of what we find should not be automated at all. A workflow that runs twice a month, or one where the judgment call is the whole job, is cheaper left alone. A good AI business consultant tells you that before invoicing you for a build. What survives the cut gets a build estimate and a payback period, and the list gets ranked by that ratio rather than by which use case sounds most impressive in a board update.

From week three the consultant becomes the builder. That is the part most engagements outsource and most projects die in. Here the same person who mapped the workflow writes the integration, wires the approval step, and sits with your operator while the first real cases run through it. You see working software inside the first month, and the handover includes the code, the prompts, and a session teaching your team to extend it.

If you are comparing AI business consultants, the question worth asking is what they built that is running in production today, and who owns it afterwards. Our build library is public and free to run, so you can test the thinking before you pay for any of it.

Why one partner instead of a strategy firm plus a dev shop

Most AI consulting projects die in the handover between the people who wrote the recommendation and the people who have to build it. The deck says "automate intake triage"; the dev shop quotes against a spec that no longer matches reality; six months pass. We close that gap by being both halves: the person who maps the workflow writes the code that automates it.

It also changes the incentive. A firm paid for strategy is rewarded for more strategy. A partner paid to ship is rewarded for the shortest path to a working system. That is why every recommendation we make ties to one measurable number, and why we will tell you when the right answer is an off-the-shelf tool and no engagement at all.