Who it's for
Built for lending teams and the agents that work for them.
Loan Intelligence fits wherever loan packages need to be reviewed consistently, quickly, and against real credit policy.
Audience list
Lenders
Built for private lenders.
Loan Intelligence gives private lending teams a repeatable review workflow: package intake, policy evaluation, deterministic metrics, and clear status outputs.
Brokers
Mortgage brokers move faster with a package engine.
Submit complete or partial borrower packages and get back a missing-item list, extracted facts, and policy alignment in minutes.
Investor loans
DSCR and investor-loan teams.
Calculate DSCR, LTV, and payment metrics from rent rolls, operating statements, and property facts — with citations back to source pages.
Operations
Loan operations teams.
Centralize package intake, track missing items, and produce consistent review outputs across a pipeline.
Agents & platforms
AI agents and partner products.
Give agents a specialized loan-package backend they can quote, submit, and retrieve through MCP or HTTP API.
FAQ
Fit questions before a team starts.
Fit is not just industry. It is package volume, repeatable policy, agent operation, intake discipline, and whether the team needs source-linked decision support instead of another document store.
The strongest fit is a team that repeatedly reviews document-heavy private credit, DSCR, investor, or commercial loan packages and needs faster completeness checks, repeatable policy alignment, source-linked outputs, or agent-operated intake. It is less useful for one-off consumer files with no repeatable package pattern.
Both, but for different reasons. Executives get a controlled package-review system with quoted credit economics and repeatable proof. Reviewers get the working outputs: extracted facts, missing-item lists, deterministic calculations, and citations they can inspect before moving the file forward.
Yes. Brokers can still process a package for extracted facts, document classification, missing items, and review-ready summaries. Uploaded lender policy makes the output stronger because the same package can then be checked against the lender's own thresholds and required documents.
Use Loan Intelligence as the specialized backend for loan package work. Let the agent collect or receive files, ask the engine for a quote, submit source documents through MCP or API, poll durable status, then return a concise answer with source references. The agent stays small; the engine handles package state.
Prepare a few representative anonymized packages, your current credit policy or checklist, expected monthly volume, intake channels, reviewer roles, credit-control requirements, and any data-retention or integration constraints. That gives the workspace and MCP setup a concrete target instead of a generic demo.
It does not approve loans, decline loans, set lending terms, replace compliance counsel, or make autonomous credit decisions. It produces package intelligence and decision-support outputs so qualified humans and controlled workflows can move faster with better evidence.
Not sure where to start?
See what the engine does, then bring it back to your workflow.