Argonaut is a multi-agent AI platform that processes commercial insurance submissions and lending applications end-to-end — from document intake to final decision, with a full audit trail at every step.
When a reviewer changes an AI recommendation, the override and its stated reason are saved to an immutable audit log — searchable, attributable, and available for compliance, E&O, or reinsurer review.
Override patterns are extracted by the AI engine into structured signals. Admins review calibration recommendations and publish improved scoring prompts — with full version history per line of business.
AI-graded packages, carrier matching, and an 8-step submission copilot.
Appetite engine, automated bordereaux, and real-time portfolio intelligence.
STP for clean risks and a prioritized queue for everything else.
DSCR, risk flags, and a structured credit memo — from documents to draft.
Argonaut processes every risk or loan application end-to-end — with a full audit trail at every step.
Email, PDF, ACORD, loan application, or API. The Intake Agent normalizes it regardless of format and creates a structured case record.
The Enrichment Agent pulls external signals in parallel — business records, safety data, financial signals, sanctions screening, and industry benchmarks.
Risk, Fraud, Appetite (or Credit), and Decision agents analyze the enriched record simultaneously. Each produces a scored output with full reasoning.
The Decision Agent synthesizes all signals into a recommendation — with a logged rationale ready for the underwriter or loan officer, or routed via STP.
Individual practitioners get standardized self-serve tools. Institutions get configured pipelines wired into their workflow.
For individual brokers, loan officers, and small teams who need AI-powered tools without a long procurement cycle. Sign up, get access, start working.
For carriers, institutional lenders, and MGAs that need a configured AI pipeline — with per-vertical STP thresholds, multi-tenant org management, and a full audit trail.
Most platforms are static. Argonaut captures override patterns, surfaces calibration gaps, and keeps every recommendation traceable — across insurance and lending.
Every agent action — intake, enrichment, scoring, decision — is logged with a timestamp, inputs, outputs, and confidence level. Underwriters, compliance teams, and reinsurers can trace any decision back to its source.
When a reviewer changes an AI recommendation, the override and stated reason are stored and classified by the AI engine into structured signals. Override patterns surface as calibration recommendations — admins publish improved scoring prompts with full version history per line.
Ask Argo questions across your entire book — hit rates by class, override trends by underwriter, which brokers are submitting clean business, or which deals are approaching DSCR thresholds. Every answer is cited to live record data.