We want to use language models to generate customer project quotes, but incoming scope details arrive as a mess of forwarded emails, text messages, and handwritten site notes. What data hygiene workflow must we implement before letting AI touch customer estimates?

Question

Tyler Smith · Accepted Answer

Feeding unstructured, sloppy inputs into a language model produces hallucinated estimates and costly pricing errors. You cannot automate quoting until you fix the hygiene of how customer scope data enters your business. The rule is simple: standardize the intake before you automate the output.

First, define a strict Scope of Work data schema. Identify the non-negotiable data fields required to calculate a quote. This typically includes square footage, equipment models, site access restrictions, utility connections, and requested completion dates. Anything outside these required fields is considered noise.

Second, create an automated normalization layer. When a customer sends a rambling email or a technician uploads a voice memo from the field, route that raw text to an AI agent whose only job is intake parsing. The agent extracts the mandatory schema fields and highlights any missing variables. If a critical field is missing, the workflow automatically alerts the project estimator to gather the missing detail before any calculation runs.

Third, maintain a verified pricing master table in your operational database or ERP that is strictly separated from the AI. The AI should never guess material costs or labor rates. Instead, the AI parses the clean project scope, matches the requested items to your verified SKU database, and produces a structured quote draft.

By enforcing data hygiene at the front door, you protect your gross margins and eliminate quote turnaround bottlenecks. This makes your estimating seat system-dependent rather than reliant on the memory of a veteran estimator, which significantly raises your valuation during exit preparation.