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.
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.
- L1WorkflowA fixed, auditable path.
- L2AssistantAI drafts. A person reviews.
- L3Supervised agentAI acts and flags exceptions.
- L4AutonomousRoutine, low-risk, monitored.
- Human-ledPeople decideAI supports the analysis.
Data ingestion and matching
A fixed, auditable workflow, with AI reading the documents.
L1 WorkflowClaims processing
A governed workflow, with an agent flagging exceptions.
L1 WorkflowL3 Supervised agentUnderwriting 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.
Full-stack AI platform for insurance
Fragmented policy, claims and correspondence data connected into one structured workflow.
~2.5x faster processing AI · Document intelligenceAI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · Agent platformsAI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effortIs insurance data spread across tools that do not talk to each other?
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 layersShow 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.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
“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.”
“We needed a platform that could handle complex construction workflows without breaking down every week. Avinya Labs delivered exactly that.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
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.
Or email contact@avinyalabs.co