How we source, structure, enrich, and score 6,071,556 acquisition-ready businesses across 45 industries and 6 countries.
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We are seeing active demand for companies like this.
Buyer names are not disclosed without permission; this proof uses anonymized demand category, size, geography, and criteria only.
Serava maps English-speaking markets where lower-middle-market acquisition activity is highest. Each country is sourced from multiple independent datasets to improve target-map coverage.
The 19 core verticals shown here (of 45 tracked) are chosen for their prevalence in PE and search fund deal flow: fragmented, recurring-revenue businesses with owner-operated characteristics. All industries are active in all 6 covered countries.
Records originate from public registries, active license sources, filings, open map data, and business websites. Sources are free or budget-gated, and fields should be treated as signals to review before outreach.
The Federal Motor Carrier Safety Administration publishes a census of every registered US motor carrier. Serava imports 1,048,269 active carrier records, 499,163 of which carry a named owner or principal on file, making trucking and logistics one of the deepest owner-named verticals in the database. Each record arrives with the carrier's operating address and phone straight from the federal filing.
The National Plan and Provider Enumeration System is the US government registry behind every NPI number. Serava imports 968,197 healthcare provider records across dental, veterinary, optometry, physical therapy, chiropractic, and more, and 99.7% of them carry a named owner or authorized official. Because the name comes from the provider's own federal enumeration filing, it is registry-grade rather than scraped.
State Secretary of State offices publish the corporate registrations behind every legally formed entity. Serava imports 659,451 registered US entities from these filings, 358,544 of them with a named owner, officer, or organizer on record. These registrations capture legal formation data — entity type, registration date, and the people behind the company — that never appears on a website.
State licensing boards publish the licensees behind regulated trades and practices. Serava imports 330,972 licensed contractors from state licence boards, including 88,638 in Washington and Oregon where the board file carries the owner's name, phone, and licence tenure, plus 55,309 dental practice owners matched from state dental board licensee records. Licence tenure is one of the strongest owner-tenure signals in the scoring model.
A baseline open-map layer for visible local operators. Serava uses targeted Overpass API queries where OSM tagging is dense enough to be useful, then supplements thin markets with official registries, licenses, filings, and demand-led source imports. OSM is not treated as a complete source for niche categories.
Companies House publishes a full bulk CSV export of all registered UK companies updated monthly. Serava streams this file, filters for active companies incorporated at least 3 years ago with a valid postcode, maps 32 SIC 2007 industry codes to our 19 industry types, and geocodes each company using the postcodes.io API. This adds 100,000 to 300,000 additional UK businesses per import cycle with legal registration data unavailable in OSM.
The New Zealand Companies Office publishes the official Companies Register as monthly bulk data. Serava imports 399,526 NZ companies from this register, 388,818 of them (97.3%) with a named director sourced from the register's director records — the founding director on file for each company — plus 62,048 registered websites. Coverage spans every NZ region from Auckland to Otago and refreshes automatically each month from the official feed.
The Open Database of Businesses (ODBus) is Statistics Canada's public extract of the National Business Register. It contains business names, addresses, NAICS industry codes, and employee size class for the majority of Canadian employer businesses. Serava imports this dataset and maps Canadian NAICS codes to our industry taxonomy, supplementing OSM coverage for Canadian provinces including Quebec and the Maritime provinces where OSM tagging density is lower.
The City of Montréal publishes a structured open-data file of all commercially registered businesses operating within the city limits. This registry captures small operators that may not appear in national datasets, adding granular coverage for one of Canada's largest commercial markets. The dataset is imported directly from Montréal's open data portal and geocoded to precise coordinates.
The US Small Business Administration releases historical 7(a) loan data under FOIA. Each record contains the borrower's business name, city, state, NAICS code, loan amount, and the number of jobs retained. Serava cross-references this against existing database entries to append employee estimates and revenue proxies. Because SBA loans skew toward established, owner-operated businesses, this dataset is a strong signal of acquisition-relevant companies.
SAM.gov is the US government's System for Award Management, containing businesses registered to receive federal contracts. These registrations can include detailed NAICS codes, employee counts, annual revenue, and owner demographic information. Serava cross-references SAM records to enrich matching companies with self-reported employee and revenue signals.
TDLR publishes daily CSV license files for Texas contractors, facilities, towing and vehicle storage operators, professional employer organizations, EV supply providers, mold companies, and related licensed businesses. Serava imports active Texas business-level records, maps them into the acquisition taxonomy, and uses ZIP or county-level coordinates depending on the detail available in each file.
Raw registry data tells you a business may exist and where it appears to operate. Enrichment adds contact signals, size clues, source context, and reputation data when available. Paid sources stay budget-gated and demand-led, with lower-cost public imports and website crawls used first.
Budget-gated machine extraction from public web signals. Diffbot is reserved for demand-led queues, paid target maps, or reviewed expansion work rather than broad database enrichment.
Optional paid contact and reputation signals from Google's business index. Matches are made by name and coordinates when budget allows; because this can be expensive, Serava runs it only on high-intent target maps rather than the whole dataset.
Free, unlimited SPARQL queries against Wikidata's structured knowledge graph. Useful for notable companies where founder, CEO, employee count, and revenue are recorded as structured facts. Serava queries Wikidata for any company whose name matches a known Wikidata entity.
IRS Form 990 data for US nonprofit organizations, associations, and foundations. ProPublica's API provides total revenue and total assets as reported on the most recent 990 filing. This is particularly useful for industry associations, healthcare nonprofits, and trade schools that appear in the home services and healthcare categories.
Every company is graded by how much verified registry data stands behind it. Verified datapoints count toward the grade; modeled estimates never inflate it.
Every mapped company receives a composite acquisition fit score between 0 and 100. The score is designed to surface businesses with traits that may matter to acquisition buyers. Higher scores rank to the top of every mandate search by default.
How many years the business has been under the same owner. Longer tenure correlates with accumulated equity, retirement motivation, and clean operational history.
Total operating age of the business. Older businesses have demonstrated survival through multiple economic cycles and carry lower go-forward risk.
When available from Diffbot, SBA, or SAM sources, revenue is scored against the mandate's target range. Businesses near the centre of the range score highest.
Businesses with available phone, website, contact-page, or Google Places signals score higher. This can correlate with active operations and reachability for outreach.
Google star rating and review count signal customer satisfaction and market presence. High-rating, high-volume businesses indicate a stable customer base.
Because multiple data sources cover overlapping geographies, deduplication is applied at the database layer. Each company is identified by a composite key of its normalized name, rounded latitude, and rounded longitude. When the same business appears in both OSM and a national registry, the record is merged rather than duplicated, and any additional fields from the second source are appended to the existing record.
The target map is not a static snapshot. Every company is continuously re-graded as verified data lands, registry re-imports follow each source's release schedule, and the enrichment layer is run against higher-intent companies first.
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