How we work
Most AI work never reaches production
It lives in a slide, a chatbot, or a pilot nobody owns. We build it into the work and measure it by what changes on the floor.
- Map. Watch how the work happens today.
- Propose. Pick the steps where AI creates the most value.
- Implement. Build it inside the workflow.
- Monitor. Measure it against the numbers that matter.
- Improve. Refine it, and run the loop again.
MapWatch how the work happens today.
In production
Case studies
Credit operations · B2B lender
$192,000
a year saved in review labor
The same team, with most of the review handled before a person opens a file.
- 13.47%Invalid or fraudulent files, closed
- 9.28%Wrong period, returned
- 32.51%Matched to a prior application
- 44.74%Reviewed by a person, with a report
Credit operations · B2B lender| Measure | Before | After |
|---|
| Review time, team of 12 | ≥40 h/week | ≤8 h/week |
|---|
| Labor cost of review | $240,000/yr | $48,000/yr |
|---|
| Annual saving | — | $192,000/yr |
|---|
We mapped how a case moved from intake to decision and interviewed the reviewers about what stops a review. Those red flags became stop checks. Files that failed a check left the queue before anyone opened a statement. For the rest, we extracted the figures and wrote a report. Reviewers open the source only when a number doesn't match.
Customer support · B2B software
38%
of requests answered with no agent
Three fewer support agents, with the easy requests handled by the agent.
- 38%Password, billing, and how-to, answered by the agent
- 62%Refunds, bugs, and escalations, sent to an agent with a summary
Customer support · B2B software| Measure | Before | After |
|---|
| Agents on support, team of 9 | 9 | 6 |
|---|
| Requests answered without a person | 0% | 38% |
|---|
The agent answers password, billing, and how-to questions from the help center and the customer's account, inside the support desk the team already uses. Refunds, bugs, and escalations go to an agent with a summary and the customer's history attached. Mapping took two weeks. The first version shipped in six weeks, and the team has added two new answer types since.
Commercial · B2B distributor
18 → 41
quotes per rep, per week
Reps now spend their time on the quotes that need judgment.
- 38.6%Standard items, quoted from the price list
- 21.5%Duplicates and existing orders, closed
- 39.9%Custom or discounted, drafted for a rep to approve
Commercial · B2B distributor| Measure | Before | After |
|---|
| Quotes sent per rep, per week | 18 | 41 |
|---|
| Rep time on quoting, team of 6 | ≈84 h/week | ≈24 h/week |
|---|
| Added quote revenue, per year | — | ≈$7.9M |
|---|
Quote requests came in by email and a web form. Each one sat in a rep's inbox until someone had time to check stock, pull the customer's price list, and work out freight. We mapped the path from request to quote and asked reps where the time went. Most of it was lookup, not judgment. The agent now reads each request, prices standard items from the current list with a margin floor, and drafts the quote in the CRM. Anything with a non-standard discount or custom spec goes to a rep with the reason and a draft attached.