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.
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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, Private Workflow Factory sprint, nurture, or no-build.
Book a free Workflow DiagnosticThe strongest current proof: a Big Four tax director's proposal workflow, built in two passes with full human review at every gate. The same pattern follows in adjacent professional-services workflows — recurring reports, BD outreach, internal ops, member services, growth. Map the real artifact, sources, and review gates before touching tools. When the new AI leverage isn't clear, the honest call is no-build. Click any case to read the full story.
A Big Four tax director's repeated proposal workflow, built in two passes: a web form that looks up the company's UEN, tax status, and GST then drafts the proposal, and a private assistant that assembles a near-sendable deck from the firm's master materials. Human review at every gate. A partner asked about rolling it out; still beta until it runs on a live proposal.
Read the case →A Web3 product manager turned scattered product material — meeting transcripts, Notion, Google Docs — into a decision-ready report. We set up Claude Code around his sources, mapped the report, and taught him to run it himself; he used it to produce the report behind his company's acquisition.
Read the case →A reusable outbound workflow for a Web3 BD rep, built in Claude Code across his real stack — Google Sheets → Apollo → drafts → Instantly → Asana. Find contacts, draft the sequence, export a ready-to-send CSV; the rep approves every message that goes out.
Read the case →A recurring member service buried in near-identical signup messages each cycle. We built the full workflow — auto-reply → confirm → log to a shared sheet → day-of check-in — with protected contacts, strict rate caps, and a kill switch. Installed on the operator's machines; honest status — not switched on yet.
Read the case →A sales creator's Instagram inbound was full of the same qualifying conversations. We built a DM conversation engine — an eight-stage playbook, tuned over five iterations and graded turn-by-turn against real chats — and deployed it as a live demo with full Instagram integration. Honest status: blocked on Meta's app review, never connected to the live account; inbound-only by platform rules.
Read the case →An affiliate promoting a membership community needed to reach thousands of prospects on X — personally — and post awareness content daily. We built a running system of three automated loops: one personalizes DMs from each prospect's profile, one sends them on an hourly cadence, and one drafts awareness tweets into a review queue. Live and running on schedule.
Read the case →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.
I've spent years sitting next to people doing the real work — tax directors drafting memos, ops leads chasing purchase orders, founders writing proposals at midnight — watching them try to bolt AI onto the side of the job. The tool was fine. The prompt was fine. The workflow was never mapped.
So I built the Lab around the smallest unit of useful work: one day, one workflow, one reviewable artifact you'd put your name on. If it's worth doing, you'll feel it by lunch. If it isn't, I'll tell you — no-build is a real answer here.
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 "Private 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. Raw client files are never uploaded, transmitted, or required to begin. If we eventually look at samples (typically during the Lab itself), they're sanitized first, on your machines.
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. We've worked with teams running everything from enterprise Microsoft 365 + Copilot to a single shared ChatGPT Team seat to fully local models on the firm's own hardware.
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, and the Lab outcome includes a memo describing exactly what an AI-assisted version would look like and what data classes it would need to touch. That memo becomes the conversation you take to compliance. We've seen several "blocks" turn into "approved with conditions" once the workflow and review gate were drawn clearly.
Optionally, yes — the 1:1 paid implementation starts from US$1,500. There's also a Private 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 — and during the founder phase, each Lab Day is private 1-on-1 with Kuan.
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.
Skip the diagnostic? Book a US$500 founder day directly →
One short conversation. You bring one repeated workflow — proposal, memo, report, research pack, or client process. No raw files. No obligation. We decide together whether a Lab is worth doing.
Book a free Workflow Diagnostic