Use cases / Sales & Estimating

Quote / Good-Better-Best Proposal Builder

In short: Reps build quotes inconsistently, slow follow-up after home visit kills momentum. This workflow uses Proposal tool (Leap/JobNimbus/etc) + LLM + price book export to fix it, and it is rated medium to set up.

Trades
HVAC, Solar, Roofing
Category
Sales & Estimating
Difficulty
Medium
ROI lever
Close rate / avg ticket
Tools
Proposal tool (Leap/JobNimbus/etc) + LLM + price book export

Workflow

  1. Tech inputs findings/photos/load notes.
  2. AI drafts Good/Better/Best with scope, efficiency, warranty in plain language.
  3. Rep reviews price (never auto-send pricing without human check).
  4. Send + follow-up trigger.

Starter prompt

You are a proposal writer for [Company]. Inputs: the tech's findings, photos notes, measurements or load calc, and the approved price book options. Write a Good, Better, Best proposal in plain language: for each option, the scope, what it fixes, expected efficiency or lifespan only if the manufacturer data is provided, and the warranty. Leave every price as [PRICE - rep to confirm]. Add a 3-sentence summary of the problem at the top. No pressure language. Never invent prices, availability, stats, or outcomes; if you don't know, say so and hand off to a human.

What to measure

Example: Same-day proposal vs next-day - test close rate by delivery time on your last 50 quotes.

Safety note: any customer-facing AI should hand gas smells, sparking, smoke, or active flooding to a human or emergency services right away. Last updated 2026-10-20. How we pick and check use cases.

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Financing Pre-Qualification Assistant

Homeowners stall at ticket size; financing mentioned too late or inconsistently.

HVAC, Roofing, Solar, Remodeling · ROI lever: Close rate on high tickets