I built a $510 vet-vertical M&A data tool that replaces $50K-150K/yr databases — 30 days, 14K targets, full methodology

Fantastic execution and clear product-market fit — love how you turned a narrow problem into a fast, low-cost alternative to expensive generic databases. A few quick thoughts:

The 17-year runway stat is a powerful selling point for PE buyers — lead with that in sales collateral.
Your pricing and per-state refresh model feels very defensible versus annual platform fees.
Consider packaging vertical playbooks (why a practice is a fit for X buyer) as upsell content for mid-market buyers.
Other verticals with similar fragmentation and repeat buyers: dental, optometry, physical therapy, independent urgent care, and funeral homes. Curious how you’re handling buyer outreach and trial conversion — outbound list + cold email, marketplace, or direct partnerships with regional brokers

the verticals where this wedge works best share three signals: (1) PE pressure visible in trade press but consolidation still under ~50%, (2) at least one $50K+/yr incumbent data tool covering the space generically, (3) public source-of-truth data (state license boards, USDA, OSM) so you avoid commercial-site scraping risk. dental hits all three (~70K independents, ASD acquiring aggressively, ADA license data public). funeral homes too (~19K, SCI + StoneMor consolidating, state regulator DBs). optometry is borderline — public licensing exists but the buy-side market is thinner. disqualifier worth flagging: if rollups are already 60%+ done, runway is short and target count usually under 5K.

Vertical M&A databases are a great wedge. The reason PitchBook can charge $50K is that they cover everything. Cover one vertical at 100x cheaper and you'll find a long list of buy-side searchers who can't justify the enterprise SKU. Coming out of a merger myself I can confirm the bottleneck on most lower-mid-market deal sourcing isn't capital, it's clean target data. The question is whether you scale by adding verticals or by going deeper in vet (operator-level contacts, EBITDA estimates, multiples paid). The second is way more defensible.

The vertical-native vs generic database tension you're describing also shows up in policy/regulatory data. Bloomberg Government and CQ Roll Call charge $20-50k/year for legislative coverage that's 90% irrelevant to any specific use case. Most of it is fire-hose alerting on every bill introduced -- which for a compliance team tracking one or two specific regulatory areas is mostly noise.

The 'boring vertical' framing at the end is right. Funeral homes, optometry, dental -- but also narrow regulatory domains. A law firm focused on telecom regulatory work doesn't need a general legislative database. They need something that watches the specific 8-12 rule-makings that affect their clients and nothing else.

The acquirer-fit mapping is the killer feature here. The equivalent in regulatory contexts is mapping rule changes to the specific statutes they amend -- sounds simple but isn't in any of the generic tools.

Interesting angle!

The part that stands out to me is not just “cheaper database”, but that it’s built around the actual workflow of someone looking at one specific vertical. Generic databases have a lot of data, but the painful part is usually filtering it into something you can actually act on.

I would be curious how you think about false negatives here though. Like if a practice does not have obvious public signals, but is still a strong acquisition target because of revenue, owner age, local reputation, or succession issues, how would that show up?

The acquirer-fit mapping is probably the most valuable part if the reasoning is good. That turns it from a list into more of an actual deal sourcing tool.

Also agree dental and optometry feel obvious, but I wonder if the better opportunities are in less crowded boring verticals where there is fragmentation but less obvious software/data coverage :-)

Best regards /A

This is one sharp build, the cost arbitrage against Grata is a genuinely compelling pitch to anyone who's ever had to justify that invoice.

The natural MVP progression from here feels like three layers: what you built is Layer 1 — discovery and scoring, which is already valuable. Layer 2 is enrichment — attaching owner name, direct contact info, tenure signals, and revenue proxies to each qualified target so a PE associate can actually pick up the phone the next day rather than go do more research. Layer 3 is a lightweight deal-tracking CRM for this workflow with templated outreach sequences tuned for owner-operator conversations.

Each layer has a meaningfully higher price ceiling than the last. Also worth noting — the same methodology maps cleanly onto Canada.

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.