Skip to content
The Aion Lab
Case studyAutomotive retailDetectionThe lot that flags itself.

The lot that flags itself.

Aged, mispriced, and unpriced units surface themselves every morning.

units surface automatically
Agedunits surface automatically
positions flagged daily
Mispricedpositions flagged daily
manual report-pulling
0manual report-pulling
01

The Challenge

The group's costliest problems were invisible until they were expensive: units aging past their window, prices drifting from market, inventory sitting unpriced. Catching any of it meant someone remembering to pull the right report on the right day.

02

The Constraint

Detection had to come from data the group already owned — no new tools for staff to learn, no dashboards nobody opens. The signal had to arrive, not wait to be found.

03

The Approach

Pricing-attention views computed on every load: aged, above-market, unpriced, and oversupplied units scored and surfaced automatically. Feed-health monitoring that notices a source going silent before anyone else does. Loaders that quarantine unexpected vendor format changes instead of crashing — mid-engagement, a vendor silently changed its export format; the pipeline contained it and kept every other feed flowing.

04

The Impact

Anomalies present themselves the morning they exist. Nobody pulls reports; the exceptions arrive already found.

What is your operation missing until it gets expensive?

Thirty minutes, no deck. Describe the problem as it really is and we'll tell you honestly what we'd do about it.