Technical Overview
High-level architecture, data flows, and performance profile. Components are modular and provider-agnostic (LLMs, embeddings, storage, and identity).
Highlights
- Provider-agnostic model & embedding layer (swap OpenAI/Claude, etc.).
- RAG with reranking and clause-level citations for explainability.
- Policy/guardrails enforce safety, masking, and compliance.
- Data stores are encrypted with KMS; access via least-privilege IAM.
- Observability: logs/metrics/traces with audit-grade retention.
Data Lifecycle
- Ingest: documents & cedent data land in an encrypted object store.
- Process: parsing, normalization, chunking; metadata to relational DB.
- Index: embeddings to vector DB; private namespace per tenant.
- Retrieve: scoped semantic + keyword + rerank.
- Generate: LLM answers grounded with clause citations.
- Log: prompts, retrievals, and outputs for audit/QA.
Performance & Scale
- RAG p95 retrieval ≈ ~500 ms at typical corpus sizes
- Horizontal scale: stateless APIs + autoscaling workers
- Batch ingestion throughput sized to dataset volume
- Latency budgets split across retrieval, reasoning, and post-filters
Integration Options
- REST APIs for ingestion, retrieval, and pricing runs
- Webhooks for long-running jobs (scenario sims)
- Service accounts & PATs for CI/CD
- Export: CSV/JSON, board-ready PDF reports
Environments
- Dev / Staging / Prod isolation
- IaC deployments and blue/green releases
- Feature flags for model/guardrail rollouts
Governance & Explainability
- Clause-grounded citations and term diffs for pricing/wordings.
- Decision logs capture inputs, retrievals, and final outputs.
- Human approvals configurable for underwriting events.
- Compliance reports mapped to NAIC/Solvency II/IFRS workflows.
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