90% faster risk insight: AI-powered claims analysis for a global insurance provider

InsurtechInsurance

TECHNOLOGIES

SERVICES

Data Science and AI/ML Services

EXPERTISES

Cloud Development Services

Project Context

Solution

Outcome

About Client
Our client is a global commercial insurance provider with a presence in major markets worldwide. Employing over 50,000 professionals and generating an annual turnover of $45–50 billion, the company plays a pivotal role in the workers' compensation space – processing large volumes of claims, legal documents, and risk assessments daily.
Like many large insurers, the client operates under mounting pressure to meet regulatory standards, reduce operational costs, and deliver faster turnaround times for claims processing. Recognizing the increasing inefficiency and manual overhead in their existing document-heavy workflows, the company initiated a transformation strategy to introduce AI agents that could scale with their global operations and unlock measurable improvements in speed and accuracy.
Business challenge
For a company operating at the scale of our client – processing thousands of documents tied to insurance claims, legal evaluations, and risk assessments – the existing manual workflows had become a serious bottleneck.
Insurance agents were relying on SharePoint-based document review, spending countless hours extracting critical insights from unstructured text. This not only slowed operations but also introduced human error, inconsistency, and a lack of auditability, risking both compliance and efficiency.
The turning point came when leadership recognized the need to modernize. They aimed to automate insight extraction, speed up risk evaluation, and ensure consistent decision-making across all document types. But transforming this vision into reality presented a series of challenges:
- Designing OCR pipelines to handle complex, multi-format insurance documents
- Implementing retrieval-augmented generation (RAG) pipelines to enable contextual search and insight extraction
- Structuring risk-related metadata into a usable schema
- Securing integration with Azure and OpenAI within strict enterprise security requirements
- Creating evaluation frameworks using LangSmith to ensure traceable, high-quality LLM outputs
In an industry under heavy regulatory scrutiny, the client required a partner with in-depth AI integration expertise, rigorous quality standards, and a proven track record in compliance-first environments. They chose Binariks based on strong referrals and our ability to bring structured thinking and measurable results to complex data problems.

Approach
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.
Implementation
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:
- 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:
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

Value Delivered

The collaboration with Binariks allowed the client to move from manual, time-intensive document processing to an AI-driven system that delivers consistent, reliable insights at scale. By combining OCR, LLMs, RAG pipelines, and advanced prompt engineering, we significantly reduced operational overhead, improved accuracy, and enabled faster, more confident decision-making.
Key Outcomes:
- 90% reduction in time required to extract and analyze risk-related data from documents
- 80-90% fewer manual review cycles, thanks to citation-backed insights from LLMs
- 20-30% improvement in model output quality and decision confidence using Reflection Agents and Chain-of-Thought prompting
- Faster triage of high-risk claims through real-time risk identification
- Improved SLA compliance, reducing delays and avoiding regulatory penalties
- Reduced reliance on domain specialists for document triage, freeing senior experts for higher-value tasks
- Decreased onboarding time for engineering teams due to a modern, well-documented tech stack (FastAPI, Docker, Python, Azure)
- Laid the foundation for potential 5x scalability through modular microservice architecture
Strategic Business Impact:
- Delivered a fully functional MVP that met all core project goals: performance, transparency, and auditability
- Enabled business users to rely on AI for real-time claims evaluation and structured data extraction
- Strengthened internal collaboration between engineering, ML, and risk analysis teams
- Opened opportunities for future use cases in fraud detection, compliance monitoring, and contract analysis
The system is now actively used in production, providing real-time support to claims professionals and empowering the organization to make smarter, faster, and more defensible decisions at scale.

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