AI Audit & Roadmap
I map where AI actually pays off in your stack — every opportunity ranked, with a baseline number attached and the first build scoped.
Start with an audit →FIG. 01 — WHAT I BUILD FOR MARKETING TEAMS
From dream to deployed system.
Your team is dreaming about what AI could do for marketing. I build the system that does it — agents, lifecycle automation, team adoption — and measure the lift.
▼ A real engagement, end to end — click any node for the artifact behind it
ARTIFACT — THE INPUT DATA
segment: lapsed_90d · source: ESP + billing export
accounts: 4,180 · fields: last_active, plan, top_feature, seats
// no new tooling — the data was already there, unjoined
SYSTEM
Draft a winback email using the account facts below. Never invent usage data.
RULES
- one specific fact in line one
- one plain-text CTA
- 90 words max
Draft a winback email using the account facts below. Never invent usage data.
RULES
- one specific fact in line one
- one plain-text CTA
- 90 words max
the underlined guardrail is what stopped hallucinated stats in QA
qa-agent rubric: factuality · tone · one-CTA · length
4,180 drafts scored → 212 low-confidence routed to human review
✓ humans stay in the loop where the model is unsure
09:15:40 dispatch — batch → ESP, 3,968 sends + 212 approved edits
09:15:41 holdout — 10% control group withheld for measurement
// the holdout is the point — no baseline, no claim
FIG. 02–04 — THREE WAYS IN
Each offer is a subset of the pipeline
Implementation Sprint
We pick one workflow and ship it: agent built and QA'd, live in your ESP, measured against a holdout. Like the run above.
Scope a sprint →Team Enablement
Working sessions inside your tools — not a webinar — until AI use is a habit. Tracked as weekly active AI users on your team.
Enable the team →FIG. 05 — FEATURED RUN
The winback agent, documented
The full write-up of the run above: the prompt with its guardrails, the QA rubric, the send log, and the holdout math behind +31%.
Read the field note →[ before/after dashboard screenshot ]