Convlyze

Revenue reporting existed, but it was entirely retrospective. Leadership could see what happened last month, not where the business was headed. Decisions about staffing, inventory support, and merchant investment were made on stale information.

Every voice order flowing through the AI Order Taker was already structured and verified, which meant the raw material for a forecast already existed. The question was how to turn a stream of verified orders into a reliable rolling projection.

Rather than hand data science an open-ended 'build us a forecast' brief, defined the specific ML product requirements: inputs, update cadence, acceptable error bounds for a 30-day horizon. Stayed in the loop through model evaluation cycles.

Partnered with data science through iterative evaluation rounds, tuning which signals fed the model and how forecast confidence was surfaced to leadership.

Shipped the forecast as a rolling dashboard rather than a static report, so it stayed current as new verified orders arrived.

Improved forecast accuracy by 30%, giving leadership a materially more reliable 30-day revenue view for planning decisions.

The best AI features often aren't new products. They're a second use of data already being collected for something else. Convlyze only existed because the Order Taker pipeline made the input data trustworthy.