to a measured baseline
How it works · the method
A working system you can measure in two weeks.
Not a slide deck. Not a sandbox demo. A high-touch ladder built for skeptical owner-operators, where each rung de-risks the next — and our deliverables read like a diligence memo, not a pitch.
01The ladder
Five rungs. Each one earns the next.
1
AI Audit / Data-Readiness Assessment
We map the workflow and order/document channels, scan your data quality (duplicate SKUs/customers, null fields, pricing staleness), and snapshot your baseline KPIs.
Deliverable: a prioritized use-case backlog on an impact/effort matrix, a scored data-readiness scorecard, and a baseline KPI snapshot — written like a diligence memo. This is the paid front door, not a loss-leader.
$15–40K1–3 weeks
2
Use-case prioritization & ROI model
We build the financial bridge: a hard-dollar baseline (cost-per-order, cost-per-invoice, DSO, hours), a target state, and a payback with sensitivity.
Deliverable: the ROI model that turns "we think this helps" into a defensible number. Every figure shows its work. This is what competitors can't do.
the signatureincluded
3
Build Sprint
We deploy the wedge on a contained scope — one channel or segment — reselling a proven engine (Conexiom/Esker) and owning the integration, the exception design, and the last-mile workflow.
Deliverable: working automation in production, with a before/after measurement harness instrumenting the result. Fixed fee, and we put a portion of it at risk against the number.
$40–120K2–6 weeks
4
Deployment & change management
Your reps move from order-takers to order-makers. We set human-in-the-loop confidence thresholds, train the team, and socialize the early wins.
Deliverable: adoption metrics and exception-handling SOPs. Framed as "do more with the staff you can't hire" — never headcount cuts.
—with the sprint
5
Run / fractional retainer
We measure realized ROI against the baseline, then either expand the ladder (AP → AR → forecasting) or hand off cleanly.
Deliverable: a realized-ROI report, plus an optional retainer to expand — with outcome-based pricing we can credibly underwrite.
$10–30K/moongoing
Prices are bands; the audit scopes your exact figure. The resold engine license is separate and excluded from fee-at-risk.
Proof of value
In about two weeks,
In about two weeks,
you'll have a number
you can defend.
The audit replaces every assumption with a measured baseline from your own ERP and a real order sample. Then we prove the wedge on one workflow with a before/after harness. You don't have to take ROI on faith — you watch it get measured.
~2 wks
~30 days
to live, zero ERP changes
1
workflow proven before you scale
$0
audit fee if there's no fit
02We underwrite the outcome
The most ownable line we have:
we tie our fee to the result.
Competitors charge flat fees and never underwrite the outcome — engineers can't model a financial result. We can. We qualify hard up front, prove it on one workflow, and put a defined portion of the build fee at risk against a measured target.
If we miss the number, you don't pay for missing it. No fit, no fee.
For PE sponsors, this becomes a portfolio lever →
The guarantee, in one breath
What's promised: a measured cost-per-order cut (or touchless-rate target) against a signed baseline.
How it's measured: the before/after harness, on annualized volume.
What's at risk: a defined portion of the build fee — or, for PE, gain-share on realized savings.
The guardrail: data-readiness preconditions, so dirty data never forces a payout on a system that worked.
"We'll show you the dollar number in a two-week audit, prove it on one workflow, and tie our build fee to hitting it."
03Honest about the hard parts
What breaks in the first weeks —
and how we handle it.
Burned-once owners don't trust vendors who pretend nothing goes wrong. Here's what actually does.
The data is messier than anyone admitted
Duplicate SKUs, inconsistent UOM, stale prices — the universal distribution problem.
→ We find it in the audit before we build, and human-in-the-loop thresholds stop dirty data from auto-propagating.
"Send the usual" orders that defy any template
Real customers don't send clean POs.
→ The system learns each customer's shorthand from their order history; the genuinely ambiguous ones route to a person, not your ERP.
Staff worry it's about replacing them
Change resistance from long-tenured, valuable people.
→ We involve them in the design, frame it as capacity not cuts, and show a quick win in the first two weeks.
The baseline is hard to pin down
No clean cost-per-order number to measure against.
→ If we can't establish a real baseline, we say so — and pivot the wedge (often to AP, which has cleaner math) rather than guess.
See the number first.
Run the calculator for a bottom-up estimate, then we'll verify it in the audit and tie our fee to hitting it.