AI Quoting & Estimates for Restoration Companies

AI estimates at a restoration company work in three places: sorting photo requests by service and urgency, drafting the scope from your technician's notes, photos and moisture readings, and giving rough ranges on small direct-pay jobs. Insurance-billed losses still need someone on site with a meter, because what decides the price is the water nobody can see.

How photo quoting works

In restoration, photo quoting is mostly photo triage: the pictures decide who you send and what they bring, and the price comes later. A typical flow:

• The homeowner sends photos or a short video. A link in your text or web form asks for the damaged area, the source if they can see it, and a wide shot of the room, plus three questions: what happened, when, and which floors.
• AI sorts the request. It tags the likely service (water, sewage, fire and smoke, mold, storm), flags urgent signs such as standing water, a sagging ceiling or visible growth across a wall, and marks anything it cannot read.
• It drafts a dispatch suggestion. Crew size, extraction gear, a rough count of air movers and dehumidifiers to load, or "inspection first". The coordinator accepts or changes it.
• A person calls back. Active water is dispatched; mold and smaller losses are booked for an inspection. The call is where trust is won, so it stays human.
• On small direct-pay jobs, it drafts a range. A patch of mold on a bathroom ceiling or a small closet leak can get a rough range from your own price rules, marked clearly as subject to inspection.
• After the visit, it drafts the scope. From the technician's photos, moisture map and readings, AI drafts the scope notes and the estimate narrative. Your estimator builds the final estimate in the estimating platform your carriers expect and approves every line.

What you need first

You need clean inputs before any tool is worth switching on. Put them in place in this order:

1. Write the approval rule. Decide what may never reach a customer without a person's sign-off. For most restoration companies, that is any number at all.
2. Write price rules for direct-pay work. Minimums, how you price small mold jobs, and what triggers an inspection-only answer.
3. Standardize field documentation. Room-by-room photos, moisture maps and daily readings, captured the same way on every job, so a draft scope has something reliable to work from.
4. Build a library of past jobs. Photos and final invoices, tagged by service, so you can compare each AI draft with what really happened.
5. Set up a photo upload link. It has to work on a phone in a dark basement, with no app to install.
6. Give every estimate a status. Sent, viewed, accepted or lost, in your restoration job management software or field service CRM, so follow-up has something to trigger on.

Accuracy and human review

Accuracy depends on how much of the damage the camera can see, which in restoration is often very little. Use this as the rule of thumb for what AI may produce:

Two rules hold for every row. AI never edits or fills in readings or photos, because they are the evidence a claim rests on. And no AI number goes out as a binding quote.

Following up open quotes

Most open restoration quotes are direct-pay work that stalled: mold remediation the homeowner is still weighing, a crawlspace they hope will dry on its own, or the rebuild after your crew finished mitigation. Each needs a different nudge, and AI can draft and time them.

• Mold, day 2: a short text offering to walk through the scope again, with two times to call.
• Mold, day 7: an email explaining what happens if moisture is left in place, in plain terms and without health claims.
• Rebuild after mitigation, day 3: a call from the estimator, since the homeowner is still choosing who puts the house back together.
• Any quote, day 14: one last check-in, then close it as lost with a reason.

Texts need the customer's consent and must honor STOP (TCPA rules, CTIA messaging principles). The sequence stops the moment they reply or book. For the call side of this, see the AI receptionist page for restoration companies, and for where quoting fits among other uses, the guide to using AI in a restoration company.

How VETS Growth Marketing helps

We build restoration estimating help around your own price rules and field documentation: photo triage, scope drafts and open-quote follow-up, with an estimator approving every number. It is a project with a finish date, built in the software and accounts you own, and we report quotes sent, quotes accepted and jobs booked. Bring a few recent estimates to a growth consultation and we will show you where AI would save your estimator time.