LIMITED ENGAGEMENTS EACH QUARTER
Your team is already making decisions
on information nobody checked.
Manual review misses what's already going wrong. We install the discipline — forecasting, risk scoring, and honest verification — and teach your own team to run it. No dependency on us afterward.
Week 1
A real audit, not a pitch deck
Week 4
Your team runs it themselves
1 number
Real proof, not a testimonial
THE COST OF WAITING
Standing still isn't neutral.
It's a decision — and it compounds.
⏳
The gap compounds every week
Teams already catching risk early are pulling further ahead each cycle you spend still doing it by hand. This isn't a one-time gap to close later — it widens on its own.
💸
Late risk always costs more than early risk
The same problem caught in week one is a quick fix. Caught in week eight, it's a missed date, a client conversation you didn't want to have, and hours nobody budgeted for.
📈
Manual review doesn't scale with headcount
The people doing it today are already at capacity. Growth doesn't make this easier — it makes the blind spots bigger.
WHY THIS IS DIFFERENT FROM "JUST USE AI"
We don't trust AI either.
That's the whole point.
Won't the AI just make things up?
◈
That's the first thing we build in, not an afterthought: a fact-check gate that requires every number to be checked against real data before it's trusted, and an explicit rule that a missing input gets an honest “not enough data” state — never a guess. If a system can't tell you when it doesn't know, it shouldn't be making the call.
Our team isn't technical enough for this.
⊞
The curriculum is built for that. Nothing here requires anyone to write code — it's a set of habits (how to check a number, how to spot a gap, how to ask AI the right question) that a project manager or ops lead picks up the same way they'd learn any other process.
We don't want to be dependent on another vendor.
⚡
That's the actual goal, not a concession — the sprint ends with your team running the method themselves. You're not buying a subscription to us; you're buying the four weeks it takes to not need us for this anymore.
This feels like a fad we can wait out.
↗
Maybe. But the cost of waiting isn't zero while you find out — it's every week of manual review, every risk caught late, every competitor who didn't wait. “Wait and see” is itself a decision, and it's the one with the compounding downside.
THE CURRICULUM
This is the bread and butter.
Three phases. Twelve modules. Fully replicable.
Every module is taught against your own real data, not a toy example — and every module is something your team keeps running long after we're gone.
Phase 1
Foundations
Institutional AI Memory
A persistent instruction file so AI assistance compounds instead of resetting every session.
Data Pipeline Literacy
Source, process, and generated output kept cleanly separate — nothing hand-edited that a refresh would silently undo.
Debugging & Verification Discipline
Trace the full data path before touching code; never trust “looks right” without running it.
Process & Governance
Concrete triggers for when to pause and get sign-off, versus when to just proceed.
Phase 2
Analytical Core
Forecasting & Predictive Modeling
Project forward from the last real anchor, validated against real completed outcomes — not a guess dressed up as a model.
Risk Scoring Systems
Turn “someone has to remember to check this” into a deterministic, weighted, tiered score.
Entity/Vendor Scorecard Systems
Fair, sample-size-aware comparison across people, vendors, or projects.
Comment & Free-Text Rollup
Extract real structured signal from notes and comments nobody has time to re-read.
Reactivity & Cross-Output Integrity
Make sure a filter or segment actually changes the underlying numbers, not just the screen.
Phase 3
Communication & Scale
Charting & Visualization Conventions
Reports that read as one coherent system instead of a pile of one-off charts.
Document/Deck Generation
Leadership-ready output, verified against a real render — never just “the math says it fits.”
Review/Subagent Architecture
A repeatable quality-gate structure so good output doesn't depend on one person remembering to check.
Plus a close on Institutionalization — your own trimmed playbook, handed off, plus one real before/after number.
HOW WE WORK TOGETHER
A fixed sprint to prove it.
An optional retainer to keep going.
START HERE
The Sprint
$20Kone-time, ~4 weeks
The fast-track version of the curriculum — real audit, real installation, real proof, on your own data.
✓Institutional AI memory, built with your team
✓Forecasting & predictive modeling on one real workflow
✓Risk scoring rules built and tuned live
✓Comment/notes rollup on your real data
✓The sign-off discipline installed as a habit
✓One real before/after number, not a testimonial
The Retainer
$200/hr · capped at 80 hrs/mo
For teams who want to keep going past the sprint — ongoing support and the rest of the curriculum, as follow-on work. You only pay for hours actually used, up to the cap.
✓Billed hourly, never more than 80 hrs in a month
✓Direct access for questions and troubleshooting
✓Special projects, scoped month to month
✓Phase 2 & 3 curriculum modules as follow-on engagements
✓Cancel anytime — no lock-in
The retainer is billed hourly and capped at 80 hrs/month — you'll always know what's included, and what it costs.
PROOF, NOT THEORY
A "top-ranked" number, corrected once, fell out of the ranking entirely.
A headline metric everyone trusted turned out to be a data-entry artifact — a different answer was actually true once someone checked. That correction is the point: the sprint's Week 1 audit is built to find exactly this kind of thing on your own data, before it costs you something.
Your next quarter
shouldn't run on hope.
30 minutes. No pitch deck. Just whether this is a fit.
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