Proof
See the work, step by step.
Every example on this page is labeled. Client workflows show work we built for a real client. Demonstrations show how a workflow runs, using sample data. Client results are published only with the client's written permission and only with numbers we measured.
A real workflow we build, run on sample data. Shows the steps, the approval points, and what gets measured. Not a client result.
A workflow we built for a real client, shown step by step on a real case, with the client's permission. Customer details are withheld.
A real engagement with measured before-and-after numbers. Our first measured results are being documented now.
HVAC
Estimate follow-up for a residential HVAC company
Scenario. A five-person HVAC company sends 15 to 25 replacement estimates a week in peak season. The office manager means to follow up, but calls and dispatch come first. Most estimates get one email and then silence.
Diagnosis. The theory of operation says every estimate should get a day-2 and a day-7 follow-up. In practice, follow-up depends on memory. This is an AI-assisted gap: a person should still send the message, but they shouldn't write it from scratch.
What we measure. Share of estimates followed up on time, minutes spent per follow-up, and close rate on followed-up estimates compared with the prior season.
Estimate logged
Customer, equipment options, price range, and the tech's notes on what matters to this homeowner.
Follow-up drafted
On day 2, the skill drafts a short, personal note in the company's voice that references the actual options quoted.
Office approves
The office manager reads, edits if needed, and sends. Nothing goes out automatically.
Second touch
On day 7, a different angle, such as financing, rebates, or scheduling availability, is drafted for approval.
Outcome recorded
Won, lost, or undecided, with the reason, so the pattern is visible next season.
Real Estate
Listing description drafts for a solo agent
Scenario. An agent lists three to five homes a month and spends 45 minutes to an hour on each description, plus social posts, usually late at night.
Diagnosis. The agent's own notes from the walkthrough already contain everything that matters. The gap is turning those notes into polished, compliant copy. That's an AI-assisted task with a clear trigger and output.
What we measure. Time from walkthrough to approved description, and the number of edits the agent makes to each draft over the first month.
Walkthrough notes in
Voice memo or quick notes, plus the property facts: beds, baths, square footage, updates, and lot.
Description drafted
MLS-length copy in the agent's voice, leading with what buyers in that price range care about.
Rules check
Standing rules steer the wording away from fair-housing problem phrases and unverifiable claims, and flag anything uncertain.
Agent approves
The agent edits and approves. Social post variations are drafted from the approved version.
Cross-industry
Inbound lead triage for a local service business
Scenario. Inquiries arrive by web form, email, text, and phone. Some are ready to buy, some are price shopping, and some are outside the service area. They all wait in the same pile.
Diagnosis. The business already knows what a good lead looks like. The rules are just in the owner's head. Writing those rules into the knowledge base lets the AI sort leads the way the owner would, and draft the first reply.
What we measure. Time from inquiry to first reply, and the share of good-fit leads that get a response the same day.
Inquiry received
Pasted, forwarded, or captured from a form, in whatever shape it arrives.
Qualified against your rules
Service area, job type, timeline, and budget signals, rated hot, warm, or not a fit, with the reasons shown.
Reply drafted
In your voice, answering what they asked, with a clear next step.
You approve and send
Every reply is reviewed. Out-of-bounds requests are flagged instead of answered.