How One Firm Found 47 Qualified Targets in 48 Hours

This is a case study. The details have been generalized to protect confidentiality, but the numbers are real and the process is exactly how it happened.

The Problem

A mid-market private equity firm was evaluating a roll-up strategy in a fragmented services sector. Think the kind of industry where the typical company does $3M–$15M in revenue, the owner is the founder, and the "corporate website" was built in 2012 and hasn't been updated since.

Their investment thesis was sound: the sector had strong fundamentals, recession-resistant revenue, and highly fragmented ownership. Hundreds of small operators, no dominant national player, classic PE roll-up dynamics.

There was just one problem: they couldn't find the companies.

Their analyst team had spent three weeks on the sourcing effort using the standard playbook:

• PitchBook: Returned 31 results. Mostly larger companies or those with prior transaction history. The long tail of small operators was almost entirely absent.
• Grata: Found 58 companies matching sector keywords. Better coverage of smaller firms, but still biased toward companies with substantive web presence.
• Capital IQ: Similar to PitchBook. Strong on financials for the companies it covered, but thin coverage in this sector below $20M revenue.
• Manual web research: The analyst found another 40+ companies through Google, industry association directories, and state licensing databases. But each company took 30–45 minutes to research and profile.

After three weeks and approximately 200 hours of analyst time, they had a target list of 112 companies. The managing director reviewed it and flagged a core issue: "This is a good list of the companies everyone can find. Where are the ones nobody's looking at?"

What We Did

The firm engaged Banker Buddy for a full-sector sourcing engagement. Here's the process:

Hours 0–4: Criteria Definition

We worked with the deal team to define precise acquisition criteria:

• Revenue range: $3M–$25M
• Geography: Continental US, with priority weighting for Sun Belt and Midwest markets
• Service mix: Minimum 60% recurring revenue from core services
• Ownership: Founder-owned or family-owned preferred; PE-backed excluded
• Employee count: 15–200 (as a revenue proxy for companies without disclosed financials)

Hours 4–24: Discovery Pipeline

Our AI pipeline searched across multiple data layers simultaneously:

• State business registrations across all 50 states, filtered by SIC/NAICS codes and active status
• Industry licensing databases — in this sector, most operators require state or local licenses, creating a registration trail that databases miss
• Web presence analysis — not just finding websites, but extracting signals like service area descriptions, team pages, fleet/equipment lists, and customer testimonials that indicate company scale
• LinkedIn signals — employee count trends, hiring patterns, executive tenure
• Google Maps and local directory listings — identifying operators by service area and customer review volume
• Association memberships — industry trade groups often have member directories that include companies invisible to financial databases

Hours 24–40: Profiling and Qualification

For every company identified in the discovery phase, the pipeline built a structured profile:

• Estimated revenue (based on employee count, service area, fleet size, and industry benchmarks)
• Employee count (LinkedIn, website team pages, state filings)
• Service line breakdown (extracted from website and marketing materials)
• Geographic footprint (service area mapping from website and directory listings)
• Ownership intelligence (state filings, LinkedIn, local business records)
• Acquisition fit score (weighted composite against the client's criteria)

Hours 40–48: Quality Review and Delivery

Human review of the AI-generated output. This step catches errors, resolves ambiguities, and applies the kind of judgment that AI can't:

• Is this company actually in the target sector, or does it just use similar keywords?
• Does the estimated revenue make sense given the employee count and service area?
• Are there red flags in the ownership structure or business history?

The Results

247 companies identified in the initial discovery sweep.

After qualification against the client's criteria, 47 companies met all thresholds and were delivered as Tier 1 targets — fully profiled, scored, and ready for outreach.

Another 89 companies were classified as Tier 2 — meeting most criteria but with one or more data gaps requiring additional verification.

How the AI List Compared to the Manual List

We mapped our output against the firm's existing 112-company list:

• 67 companies appeared on both lists (60% overlap with the firm's manual research)
• 45 companies on the firm's list were not on ours (mostly because they didn't meet the refined criteria — too large, PE-backed, or outside the service mix parameters)
• 180 companies on our list were not on the firm's list

Of those 180 new companies:

• 112 had no PitchBook profile
• 94 had no LinkedIn company page
• 68 had websites that a human researcher would likely deprioritize due to poor design or minimal information
• 31 were identified primarily through state licensing records — a data source the analyst team hadn't searched

What Happened Next

The firm's deal team took our Tier 1 list of 47 targets and began outreach. Within six weeks:

• 38 companies were successfully contacted (81% contact rate — significantly above the firm's typical 50–60% rate, because our profiles included direct owner contact information rather than generic company emails)
• 14 owners expressed preliminary interest in a conversation about their business and future plans
• 6 companies progressed to management meetings
• 2 LOIs were issued within 90 days of initial outreach

The firm's principal told us something that stuck: "We would never have found at least 30 of these 47 companies through our normal process. And two of our six management meetings came from that group."

The Economics

Cost of the AI sourcing engagement: Under $5,000.

Cost of the firm's prior manual sourcing effort: Approximately $15,000 in analyst time (200 hours at $75/hour fully loaded) plus $8,000 in amortized database subscription costs. Total: ~$23,000.

The AI engagement cost 78% less and produced 2.2x more qualified targets in 48 hours instead of three weeks.

Lessons Learned

1. Database coverage in fragmented sectors is worse than most firms realize. Financial databases are built for companies that have touched institutional capital. In sectors dominated by founder-owned small businesses, coverage can be as low as 20–30% of the total market.

2. The best targets are often the hardest to find. The companies with no database profile, no LinkedIn page, and a website from 2012 are frequently the most attractive acquisition targets. They're profitable, stable, and overlooked — which means less competition when you approach them.

3. Speed creates optionality. The firm's original three-week sourcing effort locked their team into a single sector for nearly a month. Our 48-hour turnaround let them evaluate the opportunity quickly and commit resources to outreach while competitors were still building target lists.

4. AI sourcing and human judgment are complementary, not competing. The AI found companies that humans couldn't. The humans evaluated strategic fit that AI couldn't. The combination produced better outcomes than either approach alone.

5. The ROI math is straightforward. Two LOIs resulted from this engagement. If either converts to a closed transaction, the firm's return on the sourcing investment will exceed 100x.

The Takeaway

Forty-seven qualified targets. Forty-eight hours. Under $5,000.

This isn't a theoretical capability. It's what happened. And it's repeatable across any sector where the target universe consists of small, private, founder-owned businesses — which is to say, most of the M&A market.

The firms that figure this out first don't just save money. They see deals that nobody else sees. And in M&A, proprietary deal flow is the only sustainable competitive advantage.