I built an Apify actor that turns vet clinic lists into buyer-fit M&A targets

A generic clinic list is not a sourcing workflow.

For veterinary M&A, the expensive part starts after the scrape: removing chains, spotting likely independent practices, deciding which targets deserve review now, and mapping each record to a buyer thesis.

I built Vet Practice M&A Targets, an Apify actor that turns public veterinary practice records into a ranked, buyer-fit acquisition queue.

Watch the tutorial video

Run Vet Practice M&A Targets on Apify

Concept visual: the useful output is not a directory. It is a review queue.

The problem with generic clinic lists

If you source in a niche vertical, generic business databases create a strange kind of waste.

They can give you records, but they usually do not understand the vertical-specific exclusion rules.

In veterinary M&A, that matters because a clinic name can look like an independent target while actually being part of a known platform, brand family, specialty network, or corporate buyer ecosystem.

So a human still has to do the same cleanup loop:

• remove obvious corporate or chain-owned records;
• dedupe locations and names;
• inspect websites and market signals;
• decide whether the target is review-worthy now;
• guess which acquirer profile might fit;
• export the result into a CRM, spreadsheet, or internal review process.

That is not only a data problem.

It is a workflow problem.

What the actor does

The actor is built around the acquisition review workflow:

1. Select states.
2. Pull public veterinary practice records.
3. Exclude obvious chain-owned or corporate-owned records.
4. Score remaining practices on M&A readiness.
5. Map each target to a likely buyer profile.
6. Export structured records for review, outreach, or downstream automation.

The default tutorial run uses a focused five-state sample: Florida, Texas, California, New York, and Illinois.

That matters because you should not pay for a broad national run before checking whether the output matches your review process. A small sample is enough to inspect the shape of the data, the false positives, the scoring logic, and the export fields.

The actor is designed to move from extraction to decision support, not stop at scraping.

Output fields that matter

Core output fields include:

• practice name and location;
• website and phone when available;
• chain status;
• M&A readiness score;
• buyer match;
• buyer-match confidence;
• buyer-match reasoning;
• optional Google Maps and Yelp context;
• export-ready structured fields.

The scoring is not meant to be a valuation model.

It is a prioritization layer. It answers a narrower question: should this record be reviewed now, watched later, or ignored?

Why buyer-fit matters

Not every good veterinary practice fits the same acquirer.

A suburban general practice may look closer to a VetCor-style target. A specialty or emergency signal may point toward a Mars / VCA / BluePearl / Banfield-style profile. A dense metro clinic may map differently from a rural or mid-tier market clinic. A regional rollup thesis may care more about geography and consolidation density than brand prestige.

The useful output is not only a score.

The useful output is a score plus reasoning, so a reviewer can understand why a target landed in a given bucket.

That makes the model auditable. A human can disagree with it, but they can see what triggered the recommendation.

Google Maps and Yelp enrichment

The actor can add public review and location context when enrichment is enabled.

I treat Google Maps and Yelp as enrichment, not as the core product. The core product is the structured sourcing list: independent practice detection, readiness scoring, buyer-fit mapping, and exportable records.

For Yelp, the clean path is the official Yelp Fusion API when a key is provided. That is more reliable than brittle browser scraping and avoids making the product depend on anti-bot behavior.

Integration options

Once a run finishes, you can use the output in several ways:

• download CSV or Excel for analyst review;
• pull JSON from the Apify dataset API;
• call the actor from the Apify CLI;
• schedule recurring runs;
• expose the output through OpenAPI or MCP-style workflows;
• push records into a CRM or internal diligence process.

The point is not to replace judgment.

The point is to remove list cleaning from the critical path so judgment starts earlier.

A first test run

For a first pass, I would run:

• states: FL, TX, CA, NY, IL;
• max targets per state: 30;
• minimum readiness score: 7+ if you want a tighter review list;
• enrichment: off for a fast first pass, on when you want public review context.

Then inspect:

• whether the buyer-fit reasoning makes sense;
• whether the target names look independent;
• whether the exported fields match your CRM or review workflow;
• whether one or two states deserve a deeper run.

That is a better validation loop than starting with a giant spreadsheet and hoping the signal appears later.

Links

• Watch the tutorial video
• Run Vet Practice M&A Targets on Apify
• Try another KazKN actor: Vinted Smart Scraper

Disclosure: the Apify links above include my referral code.

If you work on acquisition sourcing, vertical data products, or Apify actors, I would be interested in feedback on the scoring model and output fields.