AI & Automation
Regional FMCG Distributor
5,200 candidate alerts ranked down to the 300 that matter, by real financial impact — problems surfaced before the monthly review.
The Challenge
With reporting and coverage now live, the business had plenty of data but no way to know what needed attention today. Problems — a rep falling behind pace, an outlet quietly going lapsed, a return rate creeping up — were only discovered at the monthly review, weeks after they started, by which point the cost had already been incurred.
What We Built
How we delivered it
01
Build rule-based detection first
Encoded the patterns that actually mattered to the business — lapsed outlets, reps falling behind pace, elevated return rates — as rules running against the live data, generating candidate alerts automatically.
02
Rank by financial impact, not volume
Every candidate alert is scored by the real money at stake and ranked accordingly, so a manager's list leads with what actually matters instead of a wall of undifferentiated notifications.
03
Add AI narratives and self-resolution
Layered AI-generated regional insight narratives alongside the rule-based alerts, and made alerts auto-resolve once the underlying issue clears — so the list stays trustworthy without manual housekeeping.
5,200 → 300
Alerts ranked by impact
15,000
Stale alerts auto-resolved
Rule + AI
Alert sources, side by side
Part 3 of 3 — Same Client, One Ongoing Engagement
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