01
Build the scenario
Capture the borrower, asset, leverage, timeline, and exit plan.
Private credit intelligence
The right lender.
The right fit. Every time.
Structured lender matching for investor-loan brokers. Qualify the field with hard rules, then make every submission with conviction.
Product-level guidelines Broker-controlled decisions
Match Engine
DEAL #DM-1024 · READY
Scenario
Phoenix, AZ · SFR Fix & Flip
- Loan request
- $400,000
- Closing
- 14 DAYS
- LTC
- 84.2%
- LTARV
- 66.7%
Top match
Summit Capital
Fix & Flip 75
92
MATCH SCORE / 100
Arizona allowed
Guidelines passed
10–14 day close
The DealMap system
Structured data first. Better decisions next.
AI helps compare eligible options. It never replaces the guideline filter or broker judgment.
02
Filter the field
Eliminate products that fail product, geography, credit, or leverage rules.
03
Score the fit
Rank eligible lender products by speed, pricing, confidence, and performance.
04
Control submission
Review recommendations, select a lender, and generate a lender-ready package.
Decision sequence
Deal data
Metrics
Hard filter
Match score
AI analysis
Human approval
Every recommendation remains explainable: see the passing guidelines, potential conflicts, pricing, leverage, and historical context before selecting a lender.
Outcome intelligence
Execution data compounds.
Capture approvals, decline reasons, time to close, final leverage, and funded volume to build a more reliable lending desk.
Internal decision support
Clarity for your desk. Control for every decision.
Lender matches are based on entered information and available guidelines. Final eligibility, pricing, terms, and approval are determined by the lender and underwriting.