For tax, accounting, consulting, and professional-services operators who know AI should help — and are tired of prompt tips, vendor demos, and ad-hoc ChatGPT no one will defend on a real client file.
Real work depends on private documents, templates, precedents, judgment, source-of-truth materials, review standards, output formatting, and confidence that the AI result is not quietly wrong.
Generic AI training teaches features. It does not turn one messy workflow into a reviewable work product. What you keep running into instead:
That is what we build in a day. One workflow. One artifact. One review gate you will actually defend in front of a client or the partner who signs.
A schematic for turning a repeated workflow into something a professional can sign their name to. AI drafts, searches, extracts, compares, summarizes, or assembles. A human reviews, decides, approves, and owns the final work.
A short diagnostic conversation. No obligation. No raw private client files needed — sanitized descriptions are fine. If there's no workflow worth pursuing, the recommendation is no-build.
For professional-services operators who know AI should help but do not know where to start. We pick one repeated workflow you bring, look at it together, and decide: Lab, 1:1 implementation, Workflow Factory Sprint, nurture, or no-build.
Book the Workflow Diagnostic · freeBuilt from private workflow experiments and public pattern work across professional services, operations, outreach, and recurring reports. The useful pattern was the same every time: map the real artifact, sources, review gates, and implementation path before touching tools. Named customer details and measured outcomes stay private unless permission is explicit.
Private beta work mapped a proposal-style tax/advisory artifact around source decks, credential data, judgment steps, and human review. It is the strongest current proof signal, but not a public case study and not yet a measured before/after result.
A high-trust healthcare operations workflow showed the same design constraint: use synthetic or sanitized examples first, separate discovery from outreach, and keep operators responsible for anything sent externally.
Past outreach work turned a target-list workflow into a repeatable package: source list, enrichment path, draft-message structure, export, and human decision points. It supports the method; it is not published here as a measured case study.
Conversation workflows taught a hard boundary: AI may draft, classify, or suggest next moves, but platform limits and human approval must be settled before any automation promise is made.
Matching workflows look simple until the domain judgment appears. The useful pattern is to let AI shortlist or draft, then keep the human fit-check explicit before any follow-up goes out.
A repetitive quote-to-PO-to-supplier workflow showed why diagnostics matter before building. The useful output was a narrower first path: extraction and matching can assist; exception approval stays human-owned.
One prior engagement was too close to an existing workflow to justify a new AI build. That lesson is now part of the diagnostic: if the new leverage is unclear, the honest recommendation is no-build or wait.
The diagnostic starts from description, structure, and sanitized samples. Raw confidential material is not needed to begin, and nothing becomes a public case study without explicit permission.
If you do not leave the full-day Lab with one mapped workflow and a concrete next step — you get a free 1:1 follow-up or your money back.
And on the diagnostic side: if the conversation does not reveal a workflow worth pursuing, the recommendation is no-build. No forced sale.
Years sitting next to people doing the actual work — drafting proposals, client memos, purchase-order handoffs, recurring reports, and sales follow-ups — and watching them try to bolt AI onto the side of it.
The pattern is almost always the same. The tool is fine. The prompt is fine. The workflow was never mapped. Nobody agreed on the artifact, the sources, or the review gate. So "AI adoption" became a synonym for "individuals paste things into ChatGPT and hope."
The Lab is the smallest unit of useful work designed around that. One day. One workflow. One reviewable artifact. If it's worth doing, you can feel it by lunch. If it isn't, we don't pretend.
Then the answer is no-build, and the Workflow Diagnostic will say so. We'd rather tell you not to do the Lab than take your money for one that produces a polished artifact nobody uses.
The Diagnostic exists for exactly this reason: it sorts "this is worth a Lab" from "1:1 implementation" from "Workflow Factory Sprint" from "nurture" from "don't bother." All of those are legitimate outcomes.
No. The diagnostic works from your description of the workflow — the shape of the artifact, the sources it depends on, the people who touch it. You do not need to upload or send raw client files to begin. If we eventually look at samples during a Lab or Sprint, we use sanitized examples or your approved environment first.
No. Prompts are downstream of the workflow. We design the workflow first — what the artifact is, what the sources are, where AI assists, where the human reviews. Prompts that survive contact with a real client artifact fall out of that design naturally. We do not sell prompt libraries.
Whichever ones fit the workflow and your existing licences. The Lab is tool-agnostic: ChatGPT, Copilot, local/private models, or no AI tool at all until the review boundary is clear.
The AI-assistance plan you walk out with is portable: it documents what good looks like, not which vendor is meant to produce it.
A short, defensible human-review checklist your team can run before any AI-assisted artifact leaves the office. Typical items: source provenance, factual accuracy spot-check, judgment calls the human owns, sign-off owner, escalation path for exceptions. It looks like a real audit checklist, not a "is this output good?" thumbs-up.
Then we map the workflow anyway and design the review gate. The Lab outcome can include a memo describing what an AI-assisted version would look like, what data classes it would touch, and which approvals it would need. That turns a blanket AI conversation into a concrete risk review.
Optionally, yes — the 1:1 paid implementation starts from $1,500. There's also a Workflow Factory Sprint for larger scopes, quoted once the workflow, source boundary, buyer, and review owner are clear. We will not sell implementation before the Diagnostic / Lab; we don't know enough to scope it honestly.
Yes, if you produce a repeated artifact where accuracy and judgment matter — proposals, client memos, position papers, research, recurring reports. The Lab is scoped per workflow, not per headcount.
It means: a real version of the artifact you already produce — with the AI-assisted steps, sources, and human review gates drawn explicitly underneath it. A partner or client could trace any line back to its source, judgment call, and reviewer. That's the bar.
The Workflow Diagnostic is free. Short. No raw client files. If there's no valuable workflow to map, the answer is no-build — and you've lost an hour, not a contract.
The diagnostic is a short conversation. You do not need to send raw client files. Come with the shape of the workflow, the artifact it produces, and where review currently happens.
Continue to the diagnostic