We want to train an internal AI assistant on our proprietary estimating and pricing logic so junior account managers can draft quotes faster. How do we format our historical estimating spreadsheets and pricing calculators so the AI actually learns our logic instead of guessing?

Question

Tyler Smith · Accepted Answer

AI models are pattern matchers, not magic mind readers. If you feed them unstructured, messy spreadsheets with broken formulas and inconsistent tabs, they will output garbage pricing that destroys your gross margins. You must first standardize your estimating logic into a clean, structured database format. Start by extracting the core mathematical rules your senior estimators use to calculate quotes. Create a clean master reference template where every column represents a single variable, such as raw material costs, labor hours, machine setup fees, and margin multipliers. Format this data as a clean CSV file with clearly defined headers and no empty rows. Once your historical data is structured, you can use a retrieval-augmented generation tool to index these clean tables. This ensures the AI queries your actual logic rather than trying to guess formulas from a random PDF layout. Assign a high Follow Thru team member to oversee this data structuring project as a quarterly Rock. Having your proprietary pricing logic cleanly documented and AI-enabled dramatically increases your company enterprise value, making your business much more attractive to potential buyers during due diligence.