Enterprise LLM deployment and RAG architecture designed for regulated industries — where hallucination, bias, and compliance risk are production concerns, not afterthoughts. Our team has delivered GenAI systems for life and annuity underwriting, claims processing, document classification, and OCR-based workflows at top-tier U.S. insurance companies where model errors carry direct financial and regulatory consequences.
LLM Integration & Deployment — Enterprise LLM integration using Azure OpenAI, AWS Bedrock, and Hugging Face — including prompt engineering, system prompt design, fine-tuning, and LLM application development for regulated industry workflows.
RAG Architecture & Knowledge Systems — Retrieval-Augmented Generation pipelines with vector database integration (FAISS, ChromaDB, Pinecone), chunking strategy optimization, embedding model selection, and retrieval quality engineering validated for faithfulness and context precision.
Document AI & OCR Pipelines — AI-powered document classification, OCR-based data extraction, and intelligent document processing workflows for insurance policy documents, claims forms, and regulatory filings.
A top-tier U.S. life insurance company needed a GenAI platform to support underwriters in retrieving complex policy guidelines, analyzing applicant risk factors, and generating compliant recommendations — without hallucinating regulatory rules or producing biased outputs. RV Tech designed a RAG architecture on Azure OpenAI, optimized retrieval quality for policy document corpora, and embedded Responsible AI controls covering hallucination detection, demographic fairness evaluation, and compliance guardrails — reducing underwriting decision time by 40% with full audit traceability.
40% faster underwriting — hallucination & bias controls validated in productionMulti-agent AI systems and custom ML models built for enterprise reliability — with Responsible AI governance embedded throughout. From LangGraph-orchestrated agentic workflows to MLOps pipelines on Azure ML and SageMaker, we engineer AI systems that are accurate, explainable, fair, and compliant in regulated production environments.
Agentic AI & Multi-Agent Orchestration — Autonomous AI agents using LangChain, LangGraph, and AutoGen — with multi-agent workflow design, tool-calling, memory management, and human-in-the-loop safeguards for enterprise decision systems requiring full auditability.
ML Model Development & MLOps — Custom model design, training pipeline engineering, feature engineering, model versioning, and end-to-end MLOps on Azure ML, AWS SageMaker, and GCP Vertex AI — built for production reliability in regulated environments.
Responsible AI Governance — Bias and fairness evaluation across demographic slices, explainability frameworks (SHAP, LIME), PII/privacy validation, regulatory compliance controls, and AI safety guardrails — embedded across the full model lifecycle from development through production monitoring.
A leading U.S. specialty insurance group deploying AI across Property, Cyber Risk, and Professional Liability lines needed Agentic AI workflows for automated document analysis, risk scoring, and underwriting routing — with full governance across all lines. RV Tech designed multi-agent LangGraph orchestration with embedded Responsible AI controls: demographic bias evaluation, explainability validation, compliance monitoring, and model drift detection — ensuring AI-assisted decisions across thousands of policies were reliable, fair, and auditable under regulatory scrutiny.
Multi-line AI deployment — bias, explainability & compliance governance across all linesTell us about your program and we'll connect you with the right team within one business day.