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Ad-Crafters -
“For local service businesses that depend on the phone — turn transcripts into clarity on strong leads, dropped opportunities, and patterns that quietly affect your booking rates. No call listening needed.”
Currently In Beta - No cost, no sign-up. Just results.
About The Project
About The Project
Most local service businesses spend thousands each month on Google Ads but rarely review what actually happens on the phone.
I wanted to automate that process using GPT-4 — turning call transcripts into structured data that shows true booking rates and missed opportunities.
💡 Why I Built It
💡 Why I Built It
Local-service companies like restoration, pest control, and plumbing often judge success by number of leads, not quality of conversations.
But the real money is made (or lost) after the phone rings.
I wanted to build a tool that goes deeper — one that reveals what happens after the click.
It tracks who booked, who didn’t, and what your team did that made the difference.
🔄 How It Helps Businesses
🔄 How It Helps Businesses
Each call is automatically analyzed and tagged so you can see performance at a glance instead of guessing.
Here’s what that means for real companies:
🎯 Pain Point
🎯 Pain Point
You’re getting leads but don’t know which ones were real opportunities.
💡 How the AI Auditor Helps
💡 How the AI Auditor Helps
Flags which calls were actual prospects vs. wrong numbers, job inquiries, or vendors — giving clarity on true lead quality.
🎯Your team handles dozens of calls, but you don’t see where good leads slip through. .
💡Identifies calls where the customer wanted service but didn’t end up booking — revealing lost opportunities for follow-up.
💡Identifies calls where the customer wanted service but didn’t end up booking — revealing lost opportunities for follow-up.
🎯You rely on gut feeling to judge ad performance.
💡Shows patterns across calls (booked vs. not booked) so you can understand which services or campaigns attract the best-quality leads.
🎯You don’t have time to listen to every recording..
💡Auto-summarizes each transcript in seconds with a clear outcome tag: Booked, Callback, Not a Fit, General Inquiry, or Voicemail.
SAMPLE DATA
Call
Classification
Call
Classification
Automatic tagging helps distinguish real opportunities from unqualified calls.
Dashboard
Overview
Dashboard
Overview
Dashboard showing which calls booked, which didn’t, and where follow-ups are needed.
Unqualified Leads Breakdown
Unqualified Leads Breakdown
Unqualified leads view — shows valid inquiries that didn’t meet service criteria or weren’t a good fit.
📬 Contact / Collaborate
📬 Contact / Collaborate
💬 Let’s Build Smarter Lead Insights Together
I’m testing this AI auditor with real businesses — reach out if you’d like to collaborate or get an early demo.
Recent Case Studies (Updated Weekly)
Recent Case Studies (Updated Weekly)
💡Clear Split Between 5-Star and 1-Star Clients (Google LSAs) - Anonymous Restoration company
💡Clear Split Between 5-Star and 1-Star Clients (Google LSAs) - Anonymous Restoration company
The audit revealed a clear distinction between high-rated and low-rated leads in the company’s Google LSA account.
Since we personally rate each LSA call from 1 to 5 stars, our AI audit and call categorization system helps identify which conversations deserve top ratings — booked jobs, qualified leads, or strong inquiries — versus those that were unqualified or mishandled.
By cross-referencing this data, we can now rate calls more accurately inside the Google LSA dashboard, signaling to Google which leads are valuable.
Over time, this feedback loop should help Google’s algorithm deliver better-quality leads and increased impression share.
🕒 In progress.
💡15% Scheduling Conflicts Found — Estimator Role Proposed - Anonymous Restoration company
💡15% Scheduling Conflicts Found — Estimator Role Proposed - Anonymous Restoration company
The audit revealed that 15% of potential leads resulted in scheduling conflicts between the caller and the company.
Many callers were looking for earlier appointments, but available slots didn’t align with their needs — turning qualified leads into missed opportunities.
These missed calls represent thousands of dollars in potential revenue, especially considering the marketing costs already spent through LSAs to generate them.
To address this, we proposed adding an on-call estimator for large or high-priority jobs, enabling faster scheduling and reducing lost leads.
Awaiting implementation results.
💡Found where 25% of calls were slipping through – anonymous construction company
💡Found where 25% of calls were slipping through – anonymous construction company
The audit revealed that roughly 25% of incoming lead calls required a follow-up with a team manager, online estimator, or another step before booking.
We found many of these leads were being lost during handoff, so we implemented a more structured intake process before ending each call.
Agents now collect the caller’s name, address, callback number, and best time for follow-up, ensuring no opportunity slips through.
Reconnecting quickly—ideally the same day—significantly increases the chance of closing the job.
Awaiting implementation results.
💡New Renters-Help Approval Method Found - (Anonymous Construction Company)
💡New Renters-Help Approval Method Found - (Anonymous Construction Company)
We introduced a new step in the process — when a renter calls in, agents now ask for permission to contact the property owner on their behalf.
This ensures the lead doesn’t stall or get lost during the approval stage.
Awaiting implementation results.
💡Saved 25% of Ad Spend on LSAs - Anonymous Restoration company
💡Saved 25% of Ad Spend on LSAs - Anonymous Restoration company
The audit identified wasted ad spend from low-intent LSA calls being marked as leads.
After filtering out unqualified categories and reallocating budget toward higher-converting call types, the company reduced wasted spend by 25% while maintaining volume.
✅ Implemented, results achieved.
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