AI-Powered Quote Intake for a Remodeling Company

The Client

Anton owns RMC, a remodeling and renovations company that works with homeowners on projects ranging from flooring and tile to plumbing, electrical, demolition, permitting, and full renovation scopes. Like many contractors, Anton was spending too much time chasing early-stage leads, asking the same qualifying questions, and trying to gather enough information before deciding whether a project was worth an on-site visit.

The Problem

RMC's quote process depended heavily on manual back-and-forth. A potential customer would submit a basic inquiry, but the information was often incomplete. Anton or someone on his team would then need to follow up to understand the scope of work, project size, materials, timeline, budget, site conditions, and whether permits or subcontractors might be needed.

This created several recurring issues:

• Too much time spent qualifying leads manually
• Incomplete information before site visits were scheduled
• Difficulty preparing accurate preliminary estimates
• Missed follow-up opportunities when the team was busy
• No consistent structure for capturing project details
• Limited visibility across the team once a lead came in
• Repetitive questions that could be answered automatically

The Solution

The work started not with building the chatbot, but with understanding the business process behind the quote. RMC's renovation categories were mapped and the cost structure for common project types was broken down, including flooring, tile, wood flooring, demolition, plumbing, electrical, permitting, material selection, labor assumptions, square footage-based pricing, scope complexity, and site visit requirements.

From there, a structured estimating framework was created that the chatbot could use during conversations with website visitors. The chatbot was designed to ask the right questions based on the type of project — branching into different question flows depending on whether the lead was asking about flooring, a full remodel, or something else entirely.

The system could then generate an initial quote range, explain how the estimate was calculated, and collect the information Anton needed before scheduling an on-site visit.

The Workflow

• A potential customer visits the RMC website
• The AI chatbot starts a conversation and asks project-specific questions
• The chatbot collects scope, size, materials, timeline, and other key details
• The system calculates an initial estimate based on RMC's cost structure
• The lead receives a preliminary quote or estimate range
• Anton is notified when a qualified lead comes in
• The lead information is pushed into the CRM
• A detailed internal report is created showing project scope, assumptions, and how the estimate was calculated
• The team can follow up with better context and less manual discovery

Knowledge Base

In addition to the quoting logic, a knowledge base was built for frequently asked questions. This allowed the chatbot to answer common questions about RMC's process, timelines, project types, service area, materials, permitting, scheduling, and what customers should expect before and during a renovation. As new questions came in, they were added to the knowledge base, making the system more useful over time.

Tools and Systems

• Wix website chatbot
• AI-assisted lead qualification
• Custom renovation cost structure
• Project-specific question flows
• CRM integration
• Automated lead notifications
• Internal estimate breakdown report
• Frequently asked questions knowledge base

Outcome

The system helped RMC turn the website from a passive brochure into an active lead qualification and quoting tool. Instead of waiting for Anton to manually collect details, the chatbot could begin the discovery process immediately, gather structured project information, provide an initial quote range, and route the opportunity into the CRM. Anton gained better visibility into incoming leads and could follow up with more context. Through this process, RMC improved its lead conversion process and began seeing a stronger return from website-generated leads.

Key Value

This project showed how a construction company can use AI without losing the human element of the sales process. The chatbot did not replace Anton's expertise — it helped capture better information earlier, prepare more accurate conversations, and make follow-up faster and more effective. The result was a more professional intake experience for customers and a more efficient sales process for the business.