I built a $510 vet-vertical M&A data tool that replaces $50K-150K/yr databases — 30 days, 14K targets, full methodology
Spent 30 days building a vertical-specific M&A target database for vet-vertical PE rollups.
The premise: ~25% of US vet practices are now PE-owned (up from 10% in 2017). Five buyer groups (NVA, VetCor, BluePearl/Mars, Mars Direct, regional rollups like Pathway/Thrive/Innovetive) drive 80% of dealflow. PE associates and search funds chasing this vertical pay Grata $50K-$150K/yr for a generic database — 90% features they don't use.
I built a vertical-native alternative as an Apify pay-per-event actor:
- Pulls every US vet practice from OpenStreetMap (~14K across 50 states + DC)
- Excludes the 12 chain-owned ones already acquired (VCA, Banfield, NVA, VetCor, BluePearl, Ethos, MedVet, Innovetive, Pathway, Thrive, Compassion First, Vetnique)
- Scores each on M&A readiness (1-10) using public signals: 24-hr operation, specialty name patterns, multi-vet hints, metro tier
- Maps each independent to its most likely PE acquirer with confidence + reasoning
- Pricing: $0.20 borderline / $1.00 qualified / $5 per state refresh. Full 50-state scan = ~$510 in 8 minutes
A 5-state validation run (FL/TX/CA/NY/IL, 400 records) hit 14 qualified targets at 100% precision after manual review.
Three findings that surprised me:
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14K independents remaining = 17-year acquisition runway at current PE buying pace (~500-800/yr). The consolidation is nowhere near done.
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Regional rollups (Pathway, Thrive, Innovetive, Compassion First, MedVet) closed MORE deals last 24 months than NVA or VetCor individually. Less press, same volume.
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Geographic supply is uneven. FL/TX oversupplied with VetCor-fit practices. CA has more BluePearl-fit specialty. Pacific Northwest + Mountain West = whitespace with fewer corporate buyers active.
Stack: TypeScript + Crawlee + zod + OSM Overpass + Apify pay-per-event.
→ https://apify.com/kazkn/vet-mna-actor
Looking for feedback from indie hackers who've built vertical-specific data tools or search-fund infrastructure. The vertical-native vs generic-platform tradeoff has been interesting to navigate at this small scale. Also curious about other fragmented professional-services verticals worth this treatment — dental? optometry? funeral homes?
The fragmented professional-services verticals you mentioned at the end -- dental, optometry, funeral homes -- all share a feature that generic M&A databases ignore completely: they're heavily regulated at the federal level, and that regulation directly affects acquisition timing and valuation.
Dental is the clearest example. Medicare dental expansion has been in and out of reconciliation bills for three cycles. Medicaid reimbursement rates for dental, HSA/FSA rules for dental expenses, and federal provider licensing requirements all move practice revenue in ways that change the deal math. A PE associate looking at a 20-practice dental acquisition right now needs to know what's moving in committee before they close -- not just who's already sold.
I've been building something that covers this layer from the other direction: a tool that monitors Congress.gov for federal bills affecting specific industries and alerts when something relevant moves. Public API (free), same principle as your OSM approach -- specific signal from public data instead of expensive generic databases.
Different audience from you -- I'm targeting the practice owners who want to know what's coming before they negotiate, refinance, or expand. But if you go into dental or optometry, the legislative exposure layer is real and worth thinking about early. It's the part that shows up last in any database and first in a bad acquisition.
billwatch-landing.vercel.app
This is strong because you are not just building a cheaper database. You are turning a generic search problem into a vertical-specific deal intelligence product.
The sharpest positioning is the PE/search-fund angle: generic databases are broad, expensive, and full of unused features, while this is purpose-built for one acquisition workflow. That makes the product feel much more valuable than “vet data from OSM.”
The one thing I’d pressure-test early is the naming/brand layer. “vet-mna-actor” works as an Apify listing, but it makes the product feel like a technical script instead of a serious vertical M&A intelligence tool. If you expand into dental, optometry, funeral homes, or other fragmented rollup markets, the name needs to carry more trust and buyer seriousness.
Beryxa.com would fit this direction much better as a data/intelligence brand for vertical M&A targeting. It keeps the tool on the buyer’s side: sharper deal sourcing, cleaner market maps, and lower-cost intelligence compared with generic platforms.
I’d think about that before more verticals and methodology pages get built around the current naming frame, because this already feels closer to a PE data product than a one-off Apify actor.