--- title: AI for Insurance: Connected Underwriting and Policy Workflows | Avinya Labs description: AI for insurance by Avinya Labs: a full-stack platform connecting policy documents, claims records and correspondence into one workflow. ~2.5x faster processing. url: /service-pages/industry-insurance.html --- Industries / Insurance # AI for insurance, on one connected platform. Policy documents, claims records and correspondence connected into one workflow across underwriting and policy operations, with an audit trail on every change. [Book a call](https://calendly.com/abbylester/30-mins-meeting) [Contact sales](https://calendly.com/abbylester/30-mins-meeting) [~2.5x Faster processing speed](/case-studies/ai-platform-insurance-broker.html) [100%Data fully connected](/case-studies/ai-platform-insurance-broker.html) [1 Unified platform, replacing siloed tools](/case-studies/ai-platform-insurance-broker.html) ## What we built for insurance. A full-stack AI platform for an insurance broker. ### Connected data Structured and unstructured insurance data brought into one system, replacing siloed tools. ### Underwriting and policy workflows Faster, structured workflows across underwriting and policy operations. ### Access and audit trail Role-based access, with an audit trail on every record change. ## What else we can build for insurance. Capabilities we offer. We have not delivered these in this industry yet. ### Claims intake Claims documents read and structured, with exceptions routed to handlers. ### Submission triage Incoming submissions classified and prioritised for underwriters. ### Policy document checks Policy wording compared against templates, with differences flagged. ### Customer and broker assistants Answers to routine policy questions, with a handover to staff. ## Match the autonomy to the task. Routine data work can run with supervision. Decisions stay with people. 1. L1**Workflow**A fixed, auditable path. 2. L2**Assistant**AI drafts. A person reviews. 3. L3**Supervised agent**AI acts and flags exceptions. 4. L4**Autonomous**Routine, low-risk, monitored. 5. Human-led**People decide**AI supports the analysis. ### Data ingestion and matching A fixed, auditable workflow, with AI reading the documents. L1 Workflow ### Claims processing A governed workflow, with an agent flagging exceptions. L1 Workflow L3 Supervised agent ### Underwriting and settlement decisions Stay with people. AI prepares the information. Human-led ## Our insurance work, and related systems. Each result comes from a delivered engagement. AI · Insurance ### Full-stack AI platform for insurance Fragmented policy, claims and correspondence data connected into one structured workflow. ~2.5x faster processing Link: /case-studies/ai-platform-insurance-broker.html AI · Document intelligence ### AI document intelligence Extraction and classification that sends only the exceptions to people. >80% less manual review Link: /case-studies/enterprise-document-intelligence.html AI · Agent platforms ### AI agent platform Agents retrieve, reason and act across systems, with human approval before high-stakes actions. ~40% less manual effort Link: /case-studies/enterprise-ai-agent-platform.html ## Is insurance data spread across tools that do not talk to each other? [Book a call](https://calendly.com/abbylester/30-mins-meeting) ## The technology behind our AI systems. The right stack depends on the model, the data, the architecture and where it runs. ### Generative AI and large language models Models, chosen per task - OpenAI - Anthropic Claude - Google Gemini - Meta Llama - Mistral AI - Cohere - Hugging Face - OpenRouter - LangChain - LlamaIndex ### AI agents and orchestration Tools, steps and handover - LangGraph - AutoGen - CrewAI - Semantic Kernel - Agentic workflows - Tool calling - Function calling - Multi-agent systems ### Vector databases and semantic search Grounded answers from your content - Pinecone - Weaviate - Milvus - Qdrant - Chroma - pgvector - Elasticsearch vector search ### AI integration and APIs Connecting to your systems - REST APIs - GraphQL - Webhooks - Microservices - API gateways - SDK integrations - Third-party AI APIs ### Cloud and AI infrastructure Where it runs - AWS - Microsoft Azure - Google Cloud - Amazon SageMaker - Azure Machine Learning - Google Vertex AI - Databricks ### AI evaluation and observability Quality and cost, measured - Model evaluation - Prompt testing - Hallucination detection - Latency monitoring - Cost tracking - Output quality assessment - AI observability - Langfuse Show the full stack 12 more layers Show fewer layers ### Programming languages What the code is written in - Python - Java - JavaScript - TypeScript - C++ - R - Go - SQL - Swift - Kotlin ### AI and machine learning frameworks Training and running models - TensorFlow - PyTorch - Scikit-learn - Keras - XGBoost - LightGBM - JAX - Hugging Face Transformers ### Natural language processing Understanding text - Text classification - Sentiment analysis - Named entity recognition - Text summarization - Semantic search - Question answering - Embeddings ### Computer vision Understanding images and video - OpenCV - YOLO - Detectron2 - Image classification - Object detection - OCR - Image segmentation - Facial recognition - Video analytics ### Speech and voice AI Listening and speaking - Speech-to-text - Text-to-speech - Voice recognition - Voice assistants - Conversational voice AI - Audio processing ### Data engineering and processing Moving and preparing data - Apache Spark - Pandas - NumPy - Apache Kafka - Apache Airflow - Databricks - dbt ### Databases and data storage Where the data lives - PostgreSQL - MySQL - MongoDB - Redis - Microsoft SQL Server - Amazon DynamoDB - Elasticsearch - Snowflake ### Containers and orchestration Deploying and scaling - Docker - Kubernetes - Amazon EKS - Azure Kubernetes Service - Google Kubernetes Engine - Terraform ### MLOps and model operations Keeping models healthy - MLflow - Kubeflow - DVC - Model versioning - Model monitoring - CI/CD pipelines - Automated retraining - Performance tracking ### Backend development Services and business logic - Node.js - Django - FastAPI - Flask - Spring Boot - .NET - Express.js ### Frontend and application development What people use - React - Next.js - Angular - Vue.js - React Native - Flutter - Android - iOS ### Development and collaboration tools How we ship - Git - GitHub - GitLab - Bitbucket - Jenkins - Jira - Postman Used in our delivered platforms ## From first use case to production. Four stages, with a working version reviewed at each one. ### Discovery Map the current workflow, the systems and data involved, and what good looks like. Discovery notes - The systems and documents involved - Where reconciliation happens today - Access and audit rules ### Scope & architecture Agree the architecture, success measures and delivery milestones. Solution design - Platform architecture - Access and audit design - Success measures ### Build Build against real data, with a working version reviewed at each milestone. Working build - Working platform on real data - Team review - Quality checks ### Ship & support Roll out to production with monitoring and a defined support window. Production rollout - Production rollout - Monitoring - Support window ## Why Avinya for insurance. We have connected insurance data in production. ### Delivered, not theoretical The work on this page is in production, with results from the engagement. ### People stay in control Human review and approval checkpoints are part of the design. ### Traceable by design Sources, audit trails and monitoring planned from the start. ### We stay after launch Monitoring, tuning and a defined support window. ## Engagement models. Pick the shape that fits where you are. Ongoing ### Dedicated team A dedicated team that works as an extension of yours, from first release to scale. Fixed scope ### Project-based A defined scope, milestones and delivery date for a specific product or workflow. Advisory ### Consulting & advisory Architecture and readiness advice before you commit to a build. ## What our clients say. AI platform for an insurance broker > “Avinya Labs built our full-stack AI solution, delivering scalable infrastructure, optimized AI workflows, and a seamless user experience. Their expertise accelerated our launch and significantly reduced execution risk.” SandraProject lead, Insurance broker Result~2.5x Faster processing speed [Read case study →](/case-studies/ai-platform-insurance-broker.html) AI SaaS for construction > “We needed a platform that could handle complex construction workflows without breaking down every week. Avinya Labs delivered exactly that.” JohnCEO, Craft X [Read case study →](/case-studies/construction-fitout-ai-platform.html) AI chatbot for a travel platform > “Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.” LauraCo-founder, Travel platform [Read case study →](/case-studies/ai-travel-chatbot.html) ## AI for insurance FAQs. ### What did you build for an insurance broker? A full-stack AI platform that connects policy documents, claims records and correspondence into one workflow, with about 2.5x faster processing. ### Is every change tracked? Yes. The platform has role-based access and an audit trail on every record change. ### Does AI make underwriting decisions? No. It connects and prepares the information. Underwriting and settlement decisions stay with people. ### Where should we start? With one task where the data exists and the risk is contained. We map it, agree what good looks like, and build a working version you can review before anything scales. ### How do you handle our data? Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery. ## Ready to turn your vision into reality? Tell us the problem, the data, and where manual effort sits today. We'll map the path from MVP to scale. [Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the work](/work/index.html) Or email [contact@avinyalabs.co](mailto:contact@avinyalabs.co)