A Texas market-research firm sold reports built on 15+ person-days of manual data collection every quarter. We automated the collection — the same product now assembles in hours, with more data than before.
Our client is a Texas market-research firm whose core product is quarterly pricing reports on new-home floor plans — prices, square footage, beds, baths, and more, tracked across four major Texas markets and dozens of homebuilders.
Every quarter, that data was collected by hand: a person visiting builder websites one by one, copying numbers into spreadsheets. Fifteen-plus person-days per quarter — and growing, because builders keep adding communities. The data was the product, and the data was the bottleneck.
The question: Could automation match the accuracy of a careful human — on dozens of differently-structured sources — reliably enough to bet the firm's flagship product on it?
The system runs as an automated pipeline with deliberate human checkpoints:
Each data source is handled by its own isolated collector, so a change in one can never break another. The pipeline discovers new communities on its own and flags ones that disappear.
The client has 40 years of their own naming conventions. An AI matching stage maps every collected plan and community to the client's established names — their data stays the source of truth, and a review tool lets a human settle every uncertain match.
Every quarter is diffed against the last accepted quarter (catches drops and renames), checked against the counts each source publicly displays (catches misses), and guarded against silent failures — nothing is allowed to quietly become zero.
Before delivery, a second AI independently re-reads sources and audits the extracted data. The client receives an HTML health report with every delivery: match rates, warnings, and flagged items — nothing suspicious is ever silently skipped.
Plenty of firms sell reports, lists, or datasets assembled by hand. That manual assembly caps growth and eats margin. The pattern here transfers directly: automated collection, AI reconciliation against your own historical naming, layered validation, and a human review step that's designed in — not bolted on.
The client's team now spends its time on analysis and client relationships instead of data entry — the parts of the business customers actually pay for.
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