AI Buyer–Seller Match Engine
A proposed natural-language search, structured filters, transparent ranking, and user-approved outbound fallback would connect the matching workflow.
Implemented for The Law Practice Exchange
Before the implementation
The operating constraint
Buyer and seller fit must be assessed across disconnected CRM records and live marketplace listings.
Implementation
How the delivered system works
Translate the deal brief
Claude converts a user’s plain-language buyer or seller description into structured criteria for practice area, geography, revenue, earnings, and deal preferences.
Search and rank both sources
HubSpot contacts and Marketplace.law listings are queried together and ranked against configurable fit dimensions.
Escalate only with approval
When no match qualifies, the system pre-populates an Apollo prospect search for a user to review and approve before any outbound action.
Control boundaries
Safeguards and decision ownership
- Configured search criteria and rank weights
- Visible match attributes and confidence indicators
- User review of no-match escalation
- No automatic outbound prospecting
Published evidence
What this project supports us saying
Demonstrated
The brief proposes a controlled match-discovery workflow; it does not document completed delivery or operating outcomes.
Not established by this record
Integration availability, data coverage, match accuracy, deal velocity, and transaction outcomes are not established by this initial project description.
Business broker relevance
How this supports business-sale operations
The scope is directly relevant to M&A matching, but any implementation must validate data access, ranking policy, professional review, and outbound requirements.
Your brokerage
Build around the constraint—not the tool
We review the pipeline, data, handoffs, approvals, and operating risks before recommending a system.
Talk to a Broker Systems ExpertPast implementations do not guarantee the same results for another company.