DealScope — Buyer Intelligence Engine
The Problem
On any sell-side mandate, the buyer list is the single most important deliverable, and it's still built manually. Analysts spend days combing through databases, cross-referencing deal histories, checking sector fit, and guessing at appetite. The output is a static Excel sheet that's inaccurate. There is no way of measuring the likelihood that a buyer actually intends to transact versus just wasting the team's time.
The real cost is not only the analyst hours, but the deals that slip because the right buyer was ranked 40th on a list of 200 and never got a call. M&A advisors fight over the same deal. The first to make the call wins.
What We Built
A deal-scoring engine that takes a live mandate — target company profile, sector, financials, deal parameters — and ranks potential buyers against multiple fit signals automatically.
The core is a machine learning model trained on the client's own acquisition history. It learned the patterns that predict which buyers actually transact versus which ones take meetings and disappear, so the system can tell an analyst whether a name on the list is worth the call.
For each buyer, the system generates:
- •A composite fit score based on sector alignment, check-size fit, deal history, strategic rationale, and execution complexity
- •A plain-language explanation of why the buyer ranks where it does. Not a black-box number. A narrative an MD can read and challenge.
- •Flagged watchouts: gaps in financial data, valuation mismatches, or execution risks that would surface during diligence anyway
- •A recommended next action: prioritize outreach, hold until more data is available, or deprioritize
- •A tailored outreach angle built from the target's actual financials, growth profile, and the buyer's known acquisition preferences
- •Full deal history with prior transactions, deal values, multiples paid, and deal types, pulled and structured automatically
The system is built for the sell-side workflow. It does not replace the banker's judgment. It gives them a ranked, reasoned starting point instead of a blank spreadsheet.
Outcome
Analyst time on buyer list construction drops from days to hours. The top of the ranked list reflects buyers who actually transact, not buyers who look good on paper. Over a quarter, the team makes earlier calls to the right buyers on more mandates. That speed advantage compounds into closed deals.
See It in Action
Scored buyer shortlist for an India-based pharma sell-side mandate, with Everstone Capital ranked as a top buyer — including the full reasoning chain, watchouts, outreach angle, and historical deal comps at ~12.66x EV/EBITDA.