Estimate drag becomes an approval loop.
This is the kind of first workflow FieldLayer is built to install: a home-service lead arrives with enough interest to matter, but not enough detail to quote cleanly. AI structures the mess. A human approves every customer-facing move.
The sample queue
No private customer data. These rows use sample details to show the operating pattern FieldLayer teaches in the Starter Kit.
Customer asked for “two rooms and trim” but gave no photos or measurements.
Customer sent photos, but timing, access, and active leak status are unclear.
Customer wants a ballpark but yard size and debris volume are unknown.
AI structures the messy lead.
- Pull the customer request into a short intake summary.
- Flag missing details: measurements, photos, product choices, access, timing, budget, urgency.
- Separate known facts from assumptions so the owner can review fast.
The owner approves before sending.
- Approve, edit, or reject the missing-info question.
- Confirm any pricing or labor assumptions before an estimate draft moves forward.
- Use corrections to make the next draft sharper.
Example owner-review card
AI draft for review
Summary: Customer wants an interior repaint for two rooms and trim. Missing: room dimensions, ceiling height, wall condition, trim type, paint preference, photos, and target timing.
Suggested customer question: “Could you send photos of each room and trim, plus approximate room dimensions and your ideal timing? Once we have that, we can give you a cleaner estimate range and next step.”
Human approval checklist
- Is the question specific enough to prevent another back-and-forth?
- Did the AI avoid promising a price before enough details exist?
- Should this lead be fast-tracked, scheduled, or held for more info?
- What correction should teach the next draft?
What this proves
FieldLayer is not trying to replace owner judgment. The first install makes the estimate leak visible, drafts the next move, and keeps the owner in control until the workflow is trusted.