We want to train an AI model to draft accurate job estimates from historical scopes, but our past project folders contain messy spreadsheets with outdated pricing and undocumented discounts. What specific data hygiene steps must we take before connecting AI to our historical estimating files?

Question

Tyler Smith · Accepted Answer

Plugging an AI agent into five years of disordered project folders will generate wildly inconsistent quotes because the model cannot distinguish between a discounted deal for a friend and a high-margin commercial contract. Before you connect any machine learning tool to your estimating files, you must clean and isolate your source material.

Start by creating an approved historical baseline. Do not ingest every spreadsheet on your server. Have your lead estimator select twenty to thirty representative, profitable projects completed within the last eighteen months. These jobs must reflect current labor rates, valid material costs, and standard operating margins.

Next, sanitize the files. Convert messy, multi-tab workbooks into standardized CSV or text documents containing uniform fields: project scope, square footage or production units, bill of materials, labor hours, and final gross margin. Strip out handwritten field notes, non-standard line-item discounts, and internal side comments that do not follow your Core Process.

Finally, lock this curated data into a read-only repository. This becomes the authoritative knowledge store for your AI estimating assistant. When the tool references historical scopes, it will pull exclusively from verified, profitable precedents rather than bad historical habits. Data hygiene is not about organizing every forgotten archive; it is about providing the algorithm with a pure operational standard to emulate.