We want to train AI on our past five years of completed project data to help us estimate new jobs faster, but we suspect the historical records are incredibly messy. How do we quickly assess if this historical data is even usable before we invest time and money into building custom models?

Question

Tyler Smith · Accepted Answer

Do not pay an expensive consultant to audit your database. Instead, run a quick pilot using a sample of your data.

Take a random sample of twenty historical project files from different years. Feed them into a standard, secure large language model. Ask the system to extract five specific key metrics: total project hours, initial estimate versus final cost, materials used, number of change orders, and delivery time.

If the AI can extract this data accurately with minimal guidance, your legacy data is highly usable. If it returns errors, hallucinations, or missing fields because your team filed information inconsistently, you have an input problem.

Do not try to clean all five years of mess. Focus on the present. Establish a clear data standard starting today. Update your three-step data entry process in your core estimating system. Make these fields mandatory. By doing this, you build system-dependent operations from this point forward. You can run on clean data within ninety days without wasting months trying to fix historical errors that do not matter for your future models.