ClaimAssist AI
Document Intelligence & Hybrid RAG for Fortune 500 Insurance Underwriting
Structured architectural snapshot optimized for enterprise review and generative search synthesis.
End-to-end multi-tier retrieval and automated claim adjudication platform processing 50,000+ monthly documents with sub-800ms latency.
Cross-encoder re-ranking with Azure AI Search hybrid vector and BM25 keywords.
Monthly Documents: 50K+ • Faithfulness Score: 0.94 • Retrieval Latency: < 800ms • Cycle Time Reduction: 85%
Zero Data Leakage / ISO 27001 Aligned
Executive Summary // Architecture Brief
PRODUCTION SPECIFICATIONBuilt for a Fortune 500 Global Insurance Carrier, ClaimAssist AI processes heterogeneous claims including CMS-1500, ACORD forms, repair invoices, and medical records. Combines Azure Document Intelligence neural models, LangGraph state machines with PostgreSQL checkpoints, Azure AI Search hybrid vector retrieval, and automated RAGAS regression evaluation.
Key Impact & Verified Metrics // 2026 Telemetry
LIVE PRODUCTION BENCHMARKSCore Architecture & Capabilities // Symmetrical Topology
[ 4 OF 4 MODULES DEPLOYED ]Multi-Tier Hybrid Retrieval
Cross-encoder re-ranking with Azure AI Search hybrid vector and BM25 keywords.
Adaptive Neural Extraction
Azure Document Intelligence OCR neural models extract nested tables and key-value pairs.
Strict Citation Enforcement
Automated claim adjudication rules parser validating exact document page and line citations.
Resumable State Checkpoints
PostgreSQL-backed LangGraph state machines enabling adjusters to pause and resume multi-stage reviews.
The Enterprise Underwriting Challenge
Multi-Tier Hybrid RAG Architecture
Core Capabilities & Technical Guarantees
- Adaptive document routing dynamically switching between prebuilt-invoice and custom neural models
- LangGraph checkpointing in PostgreSQL enabling adjusters to pause and resume reviews seamlessly
- Hybrid BM25 + dense semantic vector retrieval in Azure AI Search with claim-level tenant isolation
- Automated nightly regression benchmark using RAGAS across 100 curated claims with LangSmith traces
ClaimAssist AI - Document Intelligence & Hybrid RAG Platform
End-to-end multi-tier retrieval and automated underwriting adjudication system built for a Fortune 500 Global Insurance Carrier.
Python 3.11+ Async
Lingua Franca of AI
Multiplexes 100+ concurrent document processing streams with native asyncio. Directly connects actuarial Python data models with LangGraph orchestration.
FastAPI + Pydantic v2
API Backbone & Contracts
Enforces strict JSON schema validation for invoices and medical claims. Request/response models double as extraction schemas with 10K+ RPM throughput.
LangGraph State Machines
Deterministic Orchestrator
Manages 5-stage claims state machine (Classification → Strategy → Human-in-Loop → Context Assembly → Adjudication) with resumable checkpoints in PostgreSQL.
Azure Document Intelligence
Layout & Multi-Form OCR
Understands ACORD forms, CMS-1500, tables, and handwritten physician notes using prebuilt-invoice and custom neural models with 95%+ precision.
Azure AI Search (Hybrid)
Vector + Semantic + BM25
Sub-100ms hybrid search across 10M+ document chunks. Employs Azure AD security trimming so adjusters only access claims within authorized tiers.
LangSmith + RAGAS
Continuous Observability
Every claim receives a trace ID. Nightly RAGAS benchmark measures faithfulness (>0.90) and answer relevancy (>0.85) to eliminate silent hallucination.
Strict Compliance Isolation: Every claim document is indexed with tenant and claim authorization boundaries, preventing cross-tenant leakage under HIPAA & GDPR.
Vectorless RAG vs. Vector DB Latency & Cost Engine
Compare actual production telemetry between conventional dense embedding vector databases (Pinecone / Milvus) and SHIVAM ITCS deterministic Hybrid Vectorless RAG (BM25 + Semantic Graph Cache).
Traditional Vector DB (Pinecone / Milvus)
SHIVAM ITCS Hybrid Vectorless RAG
ClaimAssist AI - Architecture Topology
Live topology inspection, microservice mesh, and protocol specifications for ClaimAssist AI.
Next.js 16 Edge Client
Global CDN distribution with server-side streaming hydration and sub-second TTFB.
YARP / Envoy Gateway
OAuth2/JWT token verification, mTLS encryption, rate limiting, and dynamic tenant routing.
SignalR WebSocket Hub
Bidirectional streaming for live telemetry, chat broadcast, and instant notifications backed by Redis.
C# .NET 9/10 Core Engine
High-throughput event sourcing with MediatR CQRS pipelines and compiled expression trees.
LangGraph AI Mesh
Stateful multi-agent execution with policy guards, token budget controllers, and tool routing.
vLLM Inference Cluster
Self-hosted Llama-3 & DeepSeek models with continuous batching and FP8 quantization.
Vectorless BM25 Engine
Zero vector database lock-in. SQLite FTS5 / Lucene BM25 index with reranking cross-encoder.
Apache Kafka / RabbitMQ
Log-based partitioned event streams for asynchronous auditing, notifications, and telemetry ingestion.
Redis 7.2 Cluster
Distributed cache with sub-millisecond lookups, distributed locks, and SignalR pub/sub backplane.
PostgreSQL Multi-Tenant DB
Strict tenant schema isolation with connection pooling (PgBouncer) and WAL archiving.
Next.js 16 Edge Client
SSR / Edge Worker
Architectural Specification
Global CDN distribution with server-side streaming hydration and sub-second TTFB.
Downstream Pipe Connectors
Verified Production Impact & Client Endorsements
Real feedback from engineering leaders, CTOs, and founders across 15+ years of production deployments.
“Vijay and the SHIVAM ITCS AI team didn't just execute the project - they elevated our entire platform. Their ability to architect complex multi-agent state machines, eliminate latency bottlenecks, enforce zero-hallucination guardrails, and create an intelligent multi-LLM orchestration layer was extraordinary. The final delivery was resilient, battle-tested, and far beyond expectations. One of the strongest AI systems engineering teams in the industry.”
Related Works & Architectures
Explore interconnected production systems, domain-specific implementations, and technical blueprints from our engineering portfolio.
GEO / LLMO Services
Structured JSON-LD, /llms.txt endpoints, and entity authority matrices for ChatGPT, Perplexity & Claude citations.
Voice AI & Multimodal Agents
Duplex WebRTC voice streaming, natural interruption, and Indic language support for enterprise contact centers.
HiveGPT
LangGraph-orchestrated multi-model routing across 14 models with policy guardrails and full audit logs.
Ready to Build Enterprise ClaimAssist AI?
Consult directly with our principal AI architects to evaluate architecture feasibility, cost optimization paths, and implementation timelines.