AEGIS AI — Insurance Risk & Underwriting Intelligence Engine
Assess. Underwrite. Defend.
An insurance risk and underwriting intelligence architecture for commercial insurers and reinsurers. It ingests submission data, enriches it with hazard, geospatial, and CAT model intelligence, scores risk using ML models, and generates structured underwriting recommendations with cited rationale — designed to help underwriters review evidence more efficiently while retaining decision authority.
−70%
Submission turnaround target
+28%
Risk score lift target
< 2 min
Memo draft time target
Figures labelled target or est. are design targets or estimates, not measured results.
Processing pipeline
Submission
Ingest policy documents, SOV, and structured submission data.
Enrichment
Geospatial hazard scoring and exposure attribute derivation.
CAT Modeling
Pull and calibrate CAT model outputs across perils.
Risk Scoring
ML ensemble produces expected loss and risk tier.
Reasoning
LangGraph routes signals to LLM for narrative synthesis.
Memo
Structured underwriting memo with citations and recommendations.
Review
Underwriter reviews, modifies, and finalizes the decision.
Problem & use case
Underwriters spend the majority of submission time on data gathering, CAT model interpretation, and narrative writing — not on risk judgment. Legacy tools produce scores without reasoning, leaving underwriters unable to explain or defend pricing decisions to clients, regulators, or reinsurers.
Solution & engineering approach
AEGIS AI pairs structured ML risk models (XGBoost and other gradient-boosting ensembles) with geospatial hazard enrichment and a LangGraph orchestration layer that routes each submission through the right combination of CAT model outputs, exposure signals, and LLM reasoning to produce a fully cited underwriting memo.
Built around three principles
01
Risk Intelligence First
Every underwriting recommendation is grounded in hazard data, CAT model intelligence, and exposure signals — not just document text. The system enriches before it reasons.
02
Explainable by Design
Underwriters must be able to explain pricing to brokers, clients, and regulators. AEGIS AI generates cited, auditable rationale for every decision — not a black-box score.
03
Augmentation, Not Automation
AEGIS AI accelerates the submission workflow and surfaces risk signals. Final underwriting authority remains with the underwriter — the system earns trust through demonstrated accuracy.
Architecture & data flow
- 01
Submission ingestion: parse policy documents, SOV, and structured fields via document intelligence pipeline
- 02
Geospatial enrichment: hazard scoring, flood / wildfire / earthquake exposure via PostGIS + satellite imagery
- 03
CAT model integration: pull RMS / AIR model outputs and calibrate against internal loss experience
- 04
ML risk scoring: XGBoost ensemble produces expected loss, risk tier, and key driver attribution
- 05
LangGraph orchestration: routes signals to LLM for narrative synthesis and underwriting memo generation
Technology stack
Key capabilities
- Designed to connect submission data, hazard intelligence, risk scoring, and cited underwriting memo generation in one workflow
- Geospatial hazard enrichment across flood, wildfire, earthquake, and storm surge perils
- CAT model output integration with internal calibration layer for sharper portfolio views
- Explainable pricing: every recommendation includes cited risk drivers and model confidence
- Designed for US, UK, and APAC commercial property and specialty lines
- Human-in-the-loop review for edge cases — AI accelerates, underwriter decides
Implementation status
Architecture & Design Complete — full system design, workflow, and component specifications defined
A Saffronyx AI project — Designing Intelligent Systems That Last.
Insurance risk decisions are only as good as the intelligence behind them. AEGIS AI brings together risk engineering, geospatial science, and enterprise AI to give underwriters the evidence, context, and cited rationale they need to make faster, more defensible decisions.
Bring the idea. We forge the system. — Saffronyx.in