Vet Practice M&A Targets - acquisition review queues for veterinary practices

Build a state-level acquisition review queue for US veterinary practices:

state -> chain filter -> M&A readiness score -> buyer-fit reasoning -> CSV/Excel/API export

This Apify Actor helps corporate development, business development, PE, search funds, and healthcare M&A operators turn public-source veterinary practice data into a reviewable sourcing queue. It is built for first-pass market mapping and territory refreshes, not for seller-intent verification or legal/financial diligence.

What you get

• State-level scan - choose one state, a 5-state territory, or all 50 states + DC.
• Chain filter - obvious chain/corporate records are flagged so your team can avoid already-owned practices.
• M&A readiness score - public-source score based on signals such as website presence, opening hours, specialty/emergency language, multi-vet naming patterns, and metro tier.
• Buyer-fit reasoning - each target is routed to a likely buyer profile such as NVA, VetCor, BluePearl/Mars, Mars Direct, or Regional PE, with confidence and reasoning.
• Export workflow - download JSON, CSV, Excel, XML, HTML table, RSS, or JSONL, or push the dataset into your CRM through Apify integrations.

Static proof sample

Before running a paid territory scan, inspect the static 25-row proof sample:

• CSV sample
• JSON sample
• Sample summary

The sample was created from prior Apify datasets only. It includes 25 rows across 20 states: 7 qualified, 18 borderline_review, and buyer-fit coverage across BluePearl, VetCor, NVA, and Regional profiles.

Example row from the static sample:

Practice	State	Score	Tier	Buyer-fit	Reasoning
Animal AM-Emergency Clinic of Pasadena	CA	9	qualified	BluePearl	specialty signals, 24-hour/emergency language, top-25 metro

How the workflow maps to your sourcing process

Step	What the actor does	What your team reviews
1. State selection	Scans selected US states from public-source veterinary practice data	Which territories matter this week or month
2. Chain filter	Flags obvious chain/corporate records	Whether any excluded or borderline practice needs manual review
3. Readiness score	Scores public signals from 1-10	Which practices deserve first review
4. Buyer-fit	Maps each target to a buyer profile with reasoning	Whether the buyer thesis makes sense
5. Export	Sends results to Apify dataset, CSV, Excel, API, or CRM workflow	Which rows move into outreach, research, or a deeper diligence queue

Pricing and run sizes

Pricing is pay-per-event. Exact cost depends on the states scanned and the number of records emitted.

Run size	Good for	Typical setup	Estimated actor charge
1-state sanity check	Inspect output quality before scaling	1 state, maxTargetsPerState: 5	about $6 if 5 borderline records are emitted
5-state territory refresh	Weekly active market review	FL/TX/CA/NY/IL, 30 targets/state	about $55 if 150 borderline records are emitted
Full-USA market map	Monthly national review	50 states + DC, 30 targets/state	about $515 if about 1,300 borderline records are emitted

Event	Price	When it fires
State refresh	$5.00	One per state per run
Borderline target	$0.20	Practice scores 5-6.99 and is emitted for manual review
Qualified target	$1.00	Practice scores 7+ from stronger public-source signals

Start small if you are evaluating the dataset. A 1-state run with maxTargetsPerState: 5 is the lowest-risk way to inspect the format, scoring, and buyer-fit reasoning before running a larger territory refresh.

What this is not

This actor is a public-source first-pass review queue. It does not verify:

• ownership or seller intent
• financials, EBITDA, valuation, or deal readiness
• legal diligence or regulatory status
• exclusive access to a target
• that a buyer will pursue or win a practice

Use it to prioritize what to review first. Your team should still verify ownership, fit, financials, outreach readiness, and legal/compliance constraints before any acquisition process.

3-step tutorial

Step 1 - Pick your states

Default is FL/TX/CA/NY/IL. Add or remove states in the "States to scan" field. All 50 states + DC are supported via OpenStreetMap coverage, but coverage varies by state.

Step 2 - Set filters

Choose max targets per state, minimum readiness score, and optional buyer profile filters. Google Maps and Yelp enrichment are optional best-effort context fields; Yelp uses the official Yelp Fusion API when you supply a key.

Step 3 - Export to your review workflow

Results stream into the Apify dataset as the actor scans each state. You can download the dataset in JSON, CSV, Excel, XML, HTML table, RSS, or JSONL, or send it to HubSpot, Salesforce, Pipedrive, Google Sheets, Airtable, Slack, Make, Zapier, n8n, or a custom API workflow.

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📥 Input

Vet Practice M&A Targets has the following input options. Click on the input tab for the auto-generated UI:

• States to scan (required) — array of US state codes
• Max targets per state — default 30, max 5000
• Minimum readiness score — default 5 (captures paid borderline + qualified targets)
• Include borderline targets — default true
• Buyer filter — optional, restrict to NVA / VetCor / BluePearl / Mars / Regional
• Refresh mode — full scan; delta is reserved for v0.2
• Google Maps + Yelp enrichment — optional review context for top candidates; Google is direct best-effort, Yelp uses the official Fusion API when yelpApiKey is supplied
• Yelp Fusion API key — optional encrypted input for reliable Yelp enrichment
• Proxy configuration — used by Google enrichment and fallback lookup debugging

Input example

{
  "states": ["FL", "TX", "CA", "NY", "IL"],
  "maxTargetsPerState": 30,
  "minReadinessScore": 5,
  "includeBorderline": true,
  "enableEnrichment": false,
  "refreshMode": "full",
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

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📤 Output

Each dataset record is a single scored vet practice. Here is a representative target record:

{
  "id": "552df40acd0ce8d0",
  "practice_name": "Hyde Park Veterinary Clinic",
  "practice_address": "1111 W Swann Ave, Tampa, FL, 33606",
  "practice_city": "Tampa",
  "practice_state": "FL",
  "practice_zip": "33606",
  "is_in_chain": false,
  "phone": "+1 813-555-0100",
  "website": "https://www.hydeparkvet.com/",
  "opening_hours": "Mo-Fr 08:00-18:00",
  "lat": 27.9374785,
  "lon": -82.4715167,
  "ma_readiness_score": 6.5,
  "ma_readiness_reasoning": "OSM-mapped practice; has website; opening_hours listed",
  "buyer_match": {
    "buyer_match_primary": "NVA",
    "buyer_match_primary_confidence": 0.78,
    "buyer_match_reasoning": "top25 metro, OSM-confirmed practice with website and opening hours — NVA-fit candidate"
  },
  "is_qualified": false,
  "is_borderline": true,
  "scraped_at": "2026-05-21T05:43:25Z"
}

Key-value store records (run analytics)

• RUN_SUMMARY — totals: states scanned, practices found, qualified vs borderline counts, duration, PPE cost
• BUYER_DISTRIBUTION — records per buyer category
• STATE_COVERAGE — per-state breakdown (verify all 50 states returned data)
• EXCLUDE_STATS — chain-exclude counts per chain

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🔌 Integrations - drop records into your pipeline

This actor is first-class on the Apify ecosystem — 165 integration options out of the box, no glue code required.

For automation teams

• n8n / Make / Zapier — trigger CRM updates, Slack alerts, email digests on every run
• Gmail / Google Drive — auto-email CSV digests or push to a shared Drive folder
• HTTP webhook — fire a POST to your CRM (HubSpot, Salesforce, Pipedrive) per qualified or borderline target

For AI agents (LLM teams)

The actor is exposed as an MCP server tool — drop it into Claude, Cursor, or any GPT in seconds:

https://mcp.apify.com/?tools=actors,docs,kazkn/vet-mna-actor

Plus a ready-to-use OpenAPI 3.0.1 spec for OpenAI Custom GPTs (paste the schema, GPT can call the actor as a tool).

For developers

• REST API — POST /v2/acts/kazkn~vet-mna-actor/runs with your Apify API authentication
• CLI — apify call kazkn/vet-mna-actor from your terminal
• Node / Python SDK — apify-client package on npm and PyPI

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⚙️ Tips and advanced options

• Start small to test scoring. Run on 3-5 states with maxTargetsPerState: 30 to see real records before committing to a 50-state run.
• Filter by buyer to focus your pipeline. A PE associate at NVA can set buyerFilter: ["NVA"] to receive only NVA-fit records.
• Use schedules for refreshes. Delta mode is reserved for v0.2, so current scheduled runs should use refreshMode: "full".
• Combine with Apify integrations. Pipe results straight into HubSpot, Salesforce, Google Sheets, or Slack via the 165 integration options.
• Schedule full USA runs monthly. Vet practice churn is slow — monthly refresh captures 95% of new acquisitions.

Built to scale

A real 50-state production run: 1,302 records in 27m35s for $0.159 in Apify compute (then PPE on top).

Transparent sourcing

Every run logs its data sources line by line — OSM Overpass element counts per state, chain exclusion stats, refresh charges. No black box.

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❓ FAQ

Who are the largest private equity buyers of veterinary practices?

The actor uses five buyer profiles for routing: NVA style general-practice rollups, VetCor style buy-and-maintain groups, BluePearl/Mars specialty and emergency profiles, Mars Direct multi-location profiles, and regional PE rollups. These are buyer-fit categories for review, not verified acquirer intent.

Does this estimate valuation or sale price?

No. The actor does not estimate EBITDA, valuation, sale price, or deal probability. It only flags public-source signals that can help prioritize which practices deserve manual review.

How do I find independent veterinary practices to acquire?

Run this actor with your target states selected. It pulls public-source veterinary practice records, flags obvious chain/corporate records, scores public M&A-readiness signals, and routes each emitted row to a buyer-fit profile for manual review. Use the 1-state setup first if you want to inspect output before a larger territory scan.

What private equity firms own veterinary clinics?

Examples of corporate and PE-backed veterinary groups include NVA, VetCor, Mars Veterinary Health brands such as BluePearl, VCA, and Banfield, and regional rollups such as Innovetive Petcare, Pathway Vet Alliance, Thrive Pet Healthcare, Compassion First Pet Hospitals, MedVet, and Vetnique. The actor uses these buyer profiles to organize review queues; it does not verify that any group is actively pursuing a specific clinic.

Is this compliant to use?

The actor uses public-source business/location data such as OpenStreetMap records and public chain signals. It does not collect private inbox data, personal account data, or seller-intent data. You are still responsible for your own outreach practices, jurisdiction-specific rules, and compliance review.

How is this different from generic company databases?

Capability	Generic database	Vet M&A Targets
Scope	Broad company search	Veterinary practice review queue
Pricing	Usually subscription or seat-based	Pay-per-event
Buyer-fit reasoning	Usually generic tags	NVA/VetCor/BluePearl/Mars/Regional profile with reasoning
Chain filtering	Often manual	Built into the emitted review workflow
Refresh workflow	Export and clean manually	Re-run by state, schedule, or API

Where does the data come from?

Primary source: OpenStreetMap Overpass API (amenity=veterinary tag). OSM is a crowd-sourced map database, so coverage varies by state and market. When enableEnrichment is on, the actor also performs Google Maps lookups and, when yelpApiKey is supplied, official Yelp Fusion API lookups for the top candidate pool.

How accurate is the buyer mapping?

Buyer-fit is inferred from public-source signals such as specialty/emergency language, metro tier, website presence, opening hours, and multi-vet naming patterns. Treat it as a prioritization aid, not a verified buyer recommendation. Your team should review the reasoning before moving a row into outreach or diligence.

Why are many rows borderline instead of qualified?

Most records are borderline because the default run prioritizes fast OSM coverage. Some practices can still score 7+ from strong public OSM signals such as specialty keywords, 24-hour/emergency positioning, full address data, website presence, and multi-vet naming patterns. Turning on Google/Yelp enrichment can raise scores when review/rating signals are available.

Can I use this in Claude / Cursor / a custom GPT?

Yes. The actor is exposed via Apify's MCP server (https://mcp.apify.com/?tools=actors,docs,kazkn/vet-mna-actor) and ships with an OpenAPI 3.0.1 spec. Drop it into Claude Desktop, Cursor, n8n, or paste the spec into an OpenAI Custom GPT — the LLM can call it as a tool and return scored records inline.

How do I get support or troubleshoot a run?

Failed run? Check the run log in Apify Console — the actor logs a one-line cause for every failure (Overpass timeout, OSM 429, invalid state code, etc.). No results for a state? Verify the state code is uppercase ISO (e.g., FL not Florida). Pricing surprise? Set maxTargetsPerState: 5 for a low-volume sanity check before scaling. For all other issues, open a question on the Apify Actor page or contact @kazkn — usual reply time < 24h. Custom buyer mapping for specific PE firms available on request.

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🔗 Other Actors by KazKN

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📄 License

Apify Actor source is closed-source for the scoring + buyer-mapping algorithm. Custom integrations available on request.