Case Studies  /  AI-Powered Due Diligence

Case Study

AI-Powered Due Diligence

Family Office / Private Capital

A family office was wasting months on people who shouldn't have made it past the first call

We built an AI-powered vetting system that checks 47+ public data sources and delivers a comprehensive verification report — before a single meeting gets booked.

IndustryFamily Office / Private Capital
ChallengeCounterparty Vetting at Scale
AI RoleAutomated Research & Report Generation
ResultLive System — 47+ Sources per Report

The Problem

The client runs a family office that evaluates dozens of potential deals, funding partners, and investment opportunities every month. The flow never stops — people come through referrals, networking events, conferences, cold outreach. Every one of them has a pitch deck and a compelling story.

The problem wasn't finding deal flow. It was filtering it. The client's team was spending weeks — sometimes months — in conversations with potential partners, conducting meetings, reviewing proposals, and advancing relationships, only to discover deep into the process that someone's credentials didn't check out, their claimed track record was fabricated, or their entities didn't actually exist.

With active transactions including a $106 million data center funding in Canada, the cost of letting the wrong person into the process isn't theoretical — it's nine figures of exposure. The existing vetting process — manual Google searches, gut feel, and hoping referrals were solid — wasn't catching the issues early enough.

The Solution

We built an AI-powered verification system that runs a comprehensive public records check on any individual or entity before the client commits meaningful time or resources. The system cross-references 47+ independent data sources and produces a structured report that categorizes every claim as Supported, Unverified, or Inconsistent — with links to the original sources so the client can verify everything independently.

Every finding is tagged by source type so the client knows exactly how much weight to give it — whether it came from a government database, an independent third-party platform, or the subject's own self-reported information.

What the Report Covers

Regulatory & Compliance Screening

OFAC / Treasury sanctions list
SEC enforcement actions
FINRA BrokerCheck history
SEC Form D filings (fundraising)
SEC Form ADV (investment advisor registration)
FEC political contribution records

Entity Verification

State Secretary of State business filings
Registered agent and address verification
Domain registration history (RDAP)
Website analytics and technology fingerprinting
International business registries where applicable
Product / app store listings and developer records

Claims Verification

Education and enrollment records
Professional affiliations and claimed roles
Accelerator and incubator participation
Published media, speaker bios, and interviews
Crunchbase, PitchBook, and venture databases
Social media and LinkedIn cross-referencing

Report Structure

Every claim categorized: Supported / Unverified / Inconsistent
Source tagging: Government, Platform, Third-Party, or Self-Reported
Full entity map with independent data for each organization
Investigation timeline with verified dates
Clickable source links for independent verification
Actionable next steps with specific follow-up questions

Source types used in every report:

GOV — Government / InstitutionalPLATFORM — Data subject can't modify3RD PARTY — Independent sourceSELF — Subject's own statements

The Results

47+
Independent sources checked per report
Hours
Not months — to vet a potential partner
3
Claim categories: Supported, Unverified, Inconsistent
100%
Verifiable — every finding links to the original source

The first report the system produced identified multiple inconsistencies in a potential partner's credentials — claims that were false per government records, affiliations that didn't exist in published portfolios, and entities with zero digital footprint beyond a freshly built website. Without this system, those issues would have surfaced weeks or months into the relationship — if they surfaced at all.


Why This Matters

Family offices, investment firms, and anyone evaluating deal flow at scale face the same fundamental problem: the people who are best at presenting themselves are not always the people with the strongest track records. Embellishing credentials is widespread, and the traditional vetting process of manual searches and referral trust doesn't catch it fast enough.

This system doesn't replace human judgment. It gives the humans better data to judge with — before they've invested time, reputation, or capital. The report is a starting point for smarter conversations, not a verdict. And because every finding links back to the original source, the client never has to take the report's word for it.

For family offices, fund managers, commercial real estate operators, and anyone who evaluates people before writing checks — this is the kind of operational intelligence that pays for itself the first time it catches something the old process would have missed.

How much is one bad meeting costing you?

If your team is spending weeks vetting people who should have been flagged on day one, I can show you how AI changes that equation. Free 45-minute audit, no pitch.