FinWhale for Investment Bankers

Overview

The project involved adapting FinWhale's multi-tool research agent into a vertical product for sell-side M&A advisors. Given a target company preparing for sale, the system autonomously maps the full buyer landscape - strategic acquirers and financial sponsors - scores each candidate on strategic fit, M&A capacity, and recency of activity, and generates ready-to-use outreach hooks tailored to each acquirer's stated strategy. Every claim and hook is grounded in citations from SEC filings, earnings transcripts, deal databases, and public news so the banker can paste a first-touch email into Outlook in minutes rather than spending two to three weeks staffing junior analysts.

Achievements

Compressed a typical sell-side buyer-landscape build from 2-3 weeks of associate work into a single afternoon, while producing materially deeper dossiers - each acquirer profile cites the specific 10-K passage, conference remark, or recent transaction that justifies inclusion. Bankers receive a ranked long-list of 50-150 buyers with per-candidate fit scores, capacity-to-pay analysis, prior M&A pattern, and a personalized outreach paragraph keyed to the seller's strategic narrative - eliminating the "generic teaser blast" problem and lifting initial response rates in pilot engagements.

Responsibilities

• Designed a multi-agent pipeline - sector mapper, acquirer ranker, capacity analyst, hook generator, citation validator - orchestrated through a Supervisor agent so each stage hands typed, evidence-bearing outputs to the next.
• Built the buyer-fit scoring model combining strategic fit from 10-K/10-Q text, capital capacity from balance-sheet data, M&A recency, and public-statement signals into a single rankable score with explainable per-dimension breakdowns.
• Engineered the outreach-hook generator: for each acquirer, the agent extracts a strategic gap or stated intent, then drafts a two-to-three sentence opener tying the seller's differentiator to a specific cited acquirer signal - never generic.
• Integrated SEC EDGAR full-text, an M&A deal database, a transcripts/news feed, and PE coverage data into a unified retrieval layer with per-source rate limiting, caching, and freshness tracking so evidence is current at search time.
• Designed the banker-facing UI: target-profile intake, ranked buyer long-list with sortable fit/capacity columns, expandable per-acquirer dossiers, copy-to-clipboard outreach blocks, CSV export, and human-in-the-loop hook editing.

Technologies Used