90% Faster Risk Insight: AI-Powered Claims Analysis for a Global Insurance Provider

The client engaged Binariks to deliver a custom AI-powered service that transforms unstructured insurance documents into structured, traceable, and actionable insights. The primary objective was to enable risk identification and analysis at scale using retrieval-augmented generation (RAG) pipelines without compromising on transparency or auditability.

To achieve these goals, Binariks assembled a cross-functional team led by a Project Manager and staffed with ML engineers, prompt engineers, QA specialists, and backend developers.

We collaborated with the client to define implementation objectives and architecture requirements:

• Transform unstructured documents into structured metadata and risk profiles
• Provide citation-backed answers grounded in real document context
• Securely deploy in Azure, with full traceability of AI outputs via LangSmith

The work was structured into Agile sprints, following 2-week Scrum cycles with regular demos and feedback loops. This allowed us to quickly iterate on LangChain-based prompts, RAG behavior, and model evaluation strategies. Feature reviews with client-side risk analysts ensured technical progress aligned with the domain-specific expectations of compliance and precision.

Throughout development, the project evolved through several key phases aligned with team scaling and project needs:

• Initial phase: A core team of three Backend Engineers, an ML Engineer, QA, BA, DevOps, and a PM established the foundation for OCR pipelines, RAG architecture, and early prompt strategies.
• Scale-up phase: The team expanded with an additional ML/NLP Engineer, a QA Automation Engineer, and a Prompt Engineer to improve Chain-of-Thought (CoT) prompting and optimize RAG evaluation.
• Stabilization phase: A lean team of two Backend Developers, an ML Engineer, and QA finalized the MVP, refined service stability, and supported deployment testing, preparing the platform for secure enterprise rollout.

Binariks implemented a scalable, AI-powered pipeline that transforms document ingestion into structured, explainable, and auditable risk insights – meeting strict security and compliance requirements set by a global commercial insurer.

The architecture was shaped by key drivers such as high document complexity, the need for transparent AI decisions, and enterprise constraints, including encryption policies, Azure-only deployments, and fast MVP timelines.

Key Drivers and Constraints Addressed

• Data complexity: Required structured extraction from a high volume of unstructured claim and risk documents
• Auditability & compliance: Needed citation-backed outputs for regulatory traceability and internal audits
• Enterprise constraints:
• Azure-mandated cloud usage
• In-transit and at-rest encryption
• Only whitelisted services (e.g., Azure Blob, Key Vault) permitted
• No external LLM hosting allowed
• Time-to-value: A fast MVP delivery with scalable infrastructure was required

Key Architecture & Implementation Components

1. Document ingestion & OCR layer

• Converted scanned and digital claim documents into machine-readable text using Azure OCR
• Served as the foundation for downstream processing
• Implemented as a Dockerized FastAPI microservice with integration to Azure Blob Storage

2. RAG-based insight generation

• Designed a Retrieval-Augmented Generation pipeline using LangChain and LangGraph
• Model responses were grounded in source documents, delivering traceable, citation-backed outputs
• Complied with audit expectations for high-stakes decision support

3. Prompt engineering with chain-of-thought (CoT)

• Developed advanced prompting logic to guide LLMs through multi-step insurance reasoning
• Improved the accuracy and interpretability of extracted risk factors and potential losses

4. Reflection agents

• Integrated to self-evaluate LLM outputs by running multiple reasoning paths and selecting the most consistent result
• Enhanced reliability while minimizing the need for manual review

5. Evaluation & observability layer

• Deployed LangSmith to trace model responses, evaluate output quality, and monitor chain performance
• Used both automated tests and user feedback from business analysts to fine-tune prompt strategies

6. Secure, scalable infrastructure

• Fully containerized and deployed in Azure, aligning with the client’s enterprise security and compliance policies
• Used Azure Key Vault for secret management and enforced encryption in-transit and at-rest
• Integrated CI/CD pipelines, with automated deployments and scheduled refresh logic
• Supported environment separation (test, QA, production) for controlled rollout

7. QA & testing framework

• Built an end-to-end test suite using Pytest and automation tools
• Covered all workflow elements including:
• OCR edge cases
• LLM fallbacks
• Failure handling
• Integration flow between services

Technology Stack

• Cloud & infrastructure: Azure, Docker, Azure Key Vault, Azure Blob Storage
• Backend: Python, FastAPI
• AI/ML: OpenAI APIs, LangChain, LangGraph, LangSmith
• Core techniques: OCR, RAG, Prompt Engineering (CoT), Reflection Agents
• Monitoring & testing: LangSmith, Pytest, test automation, CI/CD pipe