The AI Hiring Doctrine

The M&A industry is broken for small sellers. Traditional intermediaries require months-long exclusivity agreements before taking you to market, and then take six to twelve months to find a single buyer. If you want institutional-grade advisory — the kind of work that actually moves deals — you're looking at $500/hr retainers that price out 95% of founders trying to exit.

We asked a simple question: what if every seller — regardless of deal size — got Goldman Sachs-level execution? What if a founder selling a $2M landscaping company in Ohio got the same quality of deal preparation, buyer matching, and negotiation support as a $200M SaaS company selling to private equity?

AI makes that possible. Not hypothetically. Right now.

This isn't a gimmick. This isn't a chatbot on a landing page. This is first principles applied to workforce design. We looked at every function in a traditional M&A advisory firm — deal sourcing, CIM preparation, buyer outreach, compliance review, financial modeling — and asked: can an AI do this at 90%+ accuracy, 24/7, at near-zero marginal cost? For most of those functions, the answer was yes.

So we hired them.

Each AI employee has been trained on tens of thousands — if not millions — of words of context. Deal structures, industry SIC codes, buyer profiles, negotiation frameworks, compliance regulations, financial modeling templates. The knowledge base isn't static; it grows with every deal we touch.

Millions of interconnected data points cycle through the organization daily. A seller uploads financials in the morning; by afternoon, the deal has been scored, matched to pre-qualified buyers, packaged into a preliminary CIM, and queued for outreach — all without a human touching it.

That said, humans touch everything that matters. Repeated feedback loops with human oversight refine performance continuously. The human founder and CEO, Matthew Liberto, personally oversees the training doctrine and performance standards. Every AI output of consequence — buyer communications, deal valuations, legal summaries — passes through human review before it reaches a client.

Models used: Claude (Anthropic) for reasoning and language, FLUX.1 for visual identity and synthetic portraits, proprietary Supabase/pgvector pipelines for deal matching and semantic search.

Let's address the elephant: 69 AI employees sounds like job-killing. It sounds like a founder replaced his staff with bots and wrote a manifesto about it. We get it. That's not what happened.

Here's what actually happened: the work that our AI employees do is the work that made M&A advisory unaffordable for small sellers. Nobody was doing this work for a $2M deal. The economics didn't support it. A human team of analysts, associates, and VPs running a full sell-side process costs $200K–$500K in labor. For a $2M deal at 10% success fee, you'd net $200K — barely breaking even before overhead.

AI didn't replace those humans. Those humans were never hired in the first place. The deals simply didn't get done. Founders sold at a discount, or didn't sell at all, or got taken advantage of by buyers who knew the seller had no representation.

Now we can serve 10x more sellers. And more deals means more advisors earning commissions on the deals we surface. More lawyers handling closings. More accountants running due diligence. More lenders financing acquisitions. The entire ecosystem grows when deal volume grows.

We're not replacing humans. We're creating the volume that makes human jobs viable in a market segment that was previously uneconomical to serve.

Integration, not replacement. That's the doctrine.