Real-time inventory visibility across 400+ stores
BluePeak Retail
$1.4M
in reduced stockout losses (year one)
BluePeak's replenishment decisions were running on data that was, at best, twelve hours old — long enough that popular items were routinely out of stock by the time a restock order went out.
We designed a streaming ingestion layer on Databricks that captures point-of-sale events in near real time, feeding a unified inventory model that both store associates and the replenishment system read from.
Within the first year, faster visibility into stockouts and overstock reduced estimated lost sales by $1.4M, and the replenishment team moved from reactive, spreadsheet-driven ordering to a model-assisted workflow.
More success stories
Predicting equipment failures before they cause downtime
Ferro Industrial's maintenance teams were reactive by necessity — sensor data existed but was never unified. We built the feature pipelines and model-serving layer that made predictive maintenance possible.
23%
reduction in unplanned downtime
Modernizing a decade-old data warehouse without downtime
Northgate Capital's legacy warehouse couldn't keep pace with regulatory reporting deadlines. We re-platformed onto Snowflake with zero downtime and cut pipeline maintenance overhead by more than half.
60%
reduction in ETL maintenance time
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