Generally, when face with a new market or looking to identify potential buyers for a business, companies start with a brief that outlines the broad industry categories, geographic preferences, and rough financial parameters that either make up the market that they want to understand or encapsulate a potential acquirers area. But acting on these briefs require leaving analysts to manually construct search queries, filter results, and score hundreds of potential acquirers against unstated assumptions about strategic fit. This makes the process highly inconsistent and simultanously time intensive with different interpreations and coverage of data.
I built an automated sourcing pipeline that transforms natural language deal briefs into structured, scored company databases. To do so, the program parses briefs to identify criterion and then uses an LLM guided exploration to systematically search the market space using MECE principles. After using semantic search APIs to discover companies they are then scored against the derived criteria with a per company rationale.
This enables substantial time saving and reduces the ambiguity and variation that comes from human lead search. It has been utilized to source possible buyers on live deal teams guiding practice.