What wasn't working
A lithium-ion battery care company was relying on calendar-based service schedules and visual inspection to decide when client batteries needed maintenance or replacement. As the fleet of batteries under their care grew across very different use cases, that approach started missing pre-failure signals on some units while over-servicing perfectly healthy ones — wasting client time and eroding trust.
What we built
We built a tailored predictive analytics workflow that ingested their actual telemetry — cycle counts, discharge curves, temperature histories, and customer use patterns — and surfaced battery-health forecasts with confidence intervals. The technical team gets a dashboard, not a model: recommendations land as service in next 14 days, replace within 30 days, or healthy — defer, rather than raw scores. We tune the model behind the scenes.
What changed
-
Service scheduling moved from calendar-based to data-driven. The team now catches more pre-failure cases before they affect clients while extending the service window on healthy units. Customer conversations shifted from we think its time to specific, evidence-backed recommendations tied to each batterys actual usage profile.