A multi-agent AI workflow that turns each private-equity client's investment thesis into a tailored, scored list of acquisition targets — and remembers every company it touches.
I'm designing and building an AI-assisted origination system for an M&A advisory firm — a boutique that finds privately-held companies open to being sold and matches them to the right private-equity buyer. Origination is the top of the deal funnel, and it has traditionally been slow, memory-dependent, and hard to repeat: every campaign started from a blank page.
The system is a coordinated team of specialized AI agents built on top of a durable data layer. Agents do the heavy lifting — research, list-building, scoring, formatting — but the design philosophy is deliberate: the AI produces drafts, humans make the decisions, and the compounding value lives in the data the firm accumulates, not in any single generation.
Boutique origination runs on human memory and one-off effort. A client explains what they want on a call; an analyst holds that thesis in their head and builds a list keyed to a single theme — usually just "industry." Companies already owned or already contacted slip back into lists. When a company finally replies weeks later, someone has to remember which client it was ever meant for. Nothing compounds — the next campaign begins from zero.
The specific failure points I set out to solve:
The spine of the system is not the agents — it's two living data stores that give the firm memory. Everything else reads from and writes back into them:
The agents are the workforce that operate on those stores. Tooling can change; the theses and the tracker come with the firm. On top of that spine, I designed three core AI workflows.
Before the firm approaches a company or briefs a client on a sector, it needs research it can trust. I built this as a four-stage relay of specialized agents rather than a single "go research this" prompt — because a lone model produces confident, plausible, and sometimes wrong output. Separating generation from verification is what makes the result defensible.
The output feeds both sides of the system: sector intelligence sharpens a client's thesis, and company-level research informs how a target is approached.
This is the source-of-truth workflow — everything else feeds from it. Instead of a thesis living in an analyst's head, the Thesis Architect turns raw client material — intake-call notes, their portfolio, their website, quarterly updates — into a structured, living thesis document. Two design choices matter here:
The List Builder then reads that thesis and translates its firmographic criteria — business model, revenue band, geography, ownership type, seller-intent signals — into a ready-to-run search against a private-company intelligence platform. Two deliberate behaviors:
A key architectural nuance: the List Builder keeps metrics the data platform can't filter (EBITDA, margin, growth) out of the search entirely, deferring them to the scoring stage — matching each tool to what it's actually good at.
The Matcher scores a company against a client thesis and returns tiers — Strong / Possible / Weak / Review / Excluded — each with a plain-English rationale. It's the judgment layer that turns a raw candidate list into a ranked, defensible one. Two things make it more than a similarity score:
Scored companies flow to a CSV export agent that maps each tier to a 0–100 match score, attaches the verified owner contact, and produces an import-ready file for the firm's CRM — with every score and every blank field explained, and nothing fabricated.
End to end, a client thesis becomes an import-ready, scored target list — with a human review gate at every step, so no expensive action happens without approval.
Thesis Architect turns client material into a structured living thesis.
AI DraftList Builder translates the thesis into a firmographic query, excluding already-owned companies.
AI DraftThe human orchestrator runs the search and pulls firmographics + verified owner contacts.
Human-RunThe Matcher tiers each company against the thesis with rationale.
AI Draft0–100 match scores + owner contacts become an import-ready CRM file.
AI DraftEvery company lands in the Target Tracker and the exclusion list, so the next build skips it.
Human-ApprovedThe interesting problems in this build were less about prompting and more about systems design:
In one worked campaign, the system turned a single client's add-on thesis into 25 fully-scored acquisition targets — each tiered, rationale-attached, paired with a verified owner contact, and formatted for direct CRM import. What used to be a multi-day analyst grind became a reviewed, repeatable pipeline.
More importantly, the firm now compounds. Every company it touches is remembered, attributed, and automatically excluded from future searches — so each campaign starts smarter than the last.
| Old Process | This System | |
|---|---|---|
| List basis | One theme — usually just "industry" | The client's full thesis, scored and tiered |
| Already-owned | Caught by memory, ad hoc | Excluded automatically at search time |
| Attribution | Recalled from memory when a reply lands | Recorded up front, instantly reverse-lookupable |
| Contacts | Manual hunt, deal by deal | Owner name + verified email pulled in-flow |
| Memory | Every campaign starts from zero | A compounding, versioned data asset |
The core origination pipeline is built and producing real deliverables. What's next: