How Salmon turned sparse CRM data into verified commercial business intelligence for a leading home services platform.
A national home services marketplace had millions of contractor and business records in their CRM. Most were barely more than an address and a phone number. No business name. No website. No way to know if the address was a residence or a commercial location. They needed the full picture — and their existing data vendors couldn't get them there.
The client's CRM had over three million records representing contractors and service businesses across the U.S. But the data was skeletal — the kind of records that make your ops team wince. Over 60% of records had no business name. Fewer than 15% had a website. Email addresses were almost nonexistent.
Most records had no business name. Many had only a first name like "Pro" — a placeholder from self-service signups. No way to know who was actually at that address.
The most complete field was often just a street address and zip code. No website, no email, no employee count, no industry classification beyond a broad category like "HVAC."
Many addresses were residential — a sole proprietor working from home. Others were commercial locations. The CRM couldn't distinguish between the two, making segmentation impossible.
They'd tried this before. ZoomInfo couldn't match — records were too sparse for their database to find a hit. D&B returned patchy results at best. Internal solutions required manual research that didn't scale and wasn't economical across millions of records. Every approach assumed you had enough data to match against. They didn't.
Static data vendors couldn't solve this. The records were too sparse for a simple match — there was nothing to match against. Salmon's AI research agents went to work the way a human analyst would, but at scale.
A typical record from the CRM: a phone number, a broad industry tag like "Electrical," a partial address, and a placeholder name. That's it. No business identity, no website, no way to segment or prioritize.
Salmon confirms the address is real, standardizes it to USPS format, and classifies it as commercial. The geo-coordinates are resolved. We now know exactly where this record lives.
AI agents search Google, Yelp, state licensing databases, and trade directories. A licensed electrical contractor is found at that address. The business name, phone, and reviews are pulled.
Website, owner name, employee count, years in operation, license number, service area, and Google rating are all verified and attached. The skeleton record is now a fully qualified business profile.
A complete, confidence-scored record ready for CRM sync. Every field has a source and a score. The ops team can segment, prioritize, and act — no Googling required.
This ran across millions of records. Not a sample. Not a pilot. The full CRM — enriched, classified, and scored in days, not the months a human team would need. Every record that could be matched was matched. The ones that couldn't were flagged with a reason.
In four weeks, the client went from a CRM full of anonymous addresses to a segmented, enriched database of verified businesses. Their ops team could finally act on the data instead of working around it — and for the first time, actually quantify the pipeline sitting inside their own CRM.
Even starting from just an address and phone, Salmon identified the commercial business at that location with north of 85% accuracy. Records that couldn't be matched were flagged, not guessed at.
Every address was classified. The client could instantly filter sole proprietors working from home vs. established commercial locations — a segmentation that was previously impossible.
Matched records came back with verified business name, website, owner, employee count, license status, and service area. Duplicates and overlapping entries were surfaced and merged. 40+ fields where there used to be 4 — and a cleaner database to work from.
The alternative: hundreds of analyst-hours. At 3M+ records, manual research would require a team of analysts working for months — conservatively 5-10 minutes per record for the kind of multi-source verification Salmon runs automatically. That's tens of thousands of hours of work, replaced by a four-week engagement. And every enriched, verified business record represents potential top-of-funnel pipeline that was previously invisible.
If your CRM has addresses without context — residential vs. commercial, who's there, what kind of business — you're looking at the same problem. Different industry, same data gap.
Whether you're qualifying service areas, segmenting install bases, or routing field teams — the starting point is always the same question: what's actually at this address?
The methodology is the same. Salmon's AI agents research every address the way an analyst would — Google, Maps, directories, licensing databases, property records — but across millions of records, in days.
Share a few hundred addresses from your CRM and see what Salmon finds. No commitment, no contract — just proof.
kevin@salmonrun.aisalmonrun.ai