Submissions read by hand
Disclosures, statements, and exposure data are pulled and read manually. Every field is a keystroke, and the evidence behind the tier lives in an analyst's head.
Use cases · Underwriting
Agents pre-assess the submission, extract disclosures and financials, run risk, stress, and geo-exposure analysis, assign a risk tier, and hand the underwriter a decision-ready brief, so people price and decide instead of gather. Life and P&C to corporate and SME credit.
days to minutes on memosup to 90% lower cost per memo● in production
The work that has not moved is the work earlier automation could not reach: reading submissions, spreading financials, and building an evidenced view a risk owner can defend.
Disclosures, statements, and exposure data are pulled and read manually. Every field is a keystroke, and the evidence behind the tier lives in an analyst's head.
For corporate and SME credit, a single memo can run two to three analyst days of spreading and writing before anyone prices the risk.
Without a shared, evidenced view, risk tiers vary from desk to desk. The same submission can price two ways depending on who opens it.
One flow from submission to decision. Agents pre-assess the submission, extract disclosures and financials, run risk, stress, and geo-exposure analysis, assign a tier and score, and frame the call. Clean cases are decided straight through; the rest refer to an underwriter with the exception already framed. Hover a team to see where its agents sit.
Life and P&C underwriting leaders. The Head of Underwriting and the Chief Underwriting Officer own the loss ratio and the speed of the desk.
Corporate and SME credit teams. The Head of Credit Risk and the Chief Credit Officer approve the memo and answer for the decision.
The teams that need evidence behind every tier. They own the models and the justification, and they sign off with confidence.
Nine underwriting agents: what each one gathers, checks, and hands back to the risk desk.
Assigns a risk tier from the combined disclosures and data, with the drivers attached.
Builds an underwriter-ready summary with KPIs, historical performance, and focus areas before manual review.
Analyzes medical questionnaires for risk-relevant signals for life and health.
Validates disclosed conditions against medical history and flags the gaps.
Validates income and occupation on life applications against the disclosures.
Auto-builds structured, regulator-ready credit memos from the spread file.
Extracts and maps accounts from trial balances into standardized templates.
Uses geolocation and GIS layers to identify hazard zones, proximity risks, and exposure clustering.
Lets underwriters ask natural-language questions across claims, exposure, performance, and policy data, and get contextual, traceable answers
“…loss ratio is trending up on this account, here are the three claims driving it.”
Pre-composed sets of agents, each scoped to one underwriting value stream: deploy the whole stack or pick the agents you need.
Medical, health-history, income, and asset checks feed a risk tier, with borderline cases escalated to an underwriter with full context.
Balance-sheet extraction, KPI trends, market context, and auto-built credit memos turn raw statements into a defensible decision.
Income, title, appraisal, collateral, and debt checks assemble a complete, verified mortgage pack before an underwriter opens it.
Entity resolution, loss-ratio pattern analysis, and a pre-assessment brief give the underwriter an evidenced view before manual review.
Geo-exposure, proximity, and distance risk signals, wrapped in deterministic guardrails and human-in-the-loop validation.
Start from a stack or pick individual agents, they're built to work together on your value stream.
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