Call centers are overwhelmed: an operator spends 20 minutes collecting data for a single accident claim, and the client waits a day for a response. We solve this with an AI chatbot that handles FNOL in 3 minutes—5 times faster than an operator—and automatically creates a case in the CRM. Our AI chatbot insurance solution encompasses claims automation FNOL, insurance chatbot development with RAG for insurance, an LLM insurance bot, a chatbot for insurance broker, an OSAGO calculation bot, call center cost reduction, an AI assistant insurance, an FNOL chatbot, a policy calculation bot, and insurance claims AI. For a mid-sized insurer handling 50,000 claims per year, the chatbot saves approximately $1.5 million annually.
Chatbot implementation pays for itself within months by cutting time-to-resolution and FTE in the call center.
Why insurers need an AI chatbot
Manual claims processing is a bottleneck. The chatbot handles data collection, document verification, damage assessment, and transfer to CRM. The time from incident to case creation drops by 80%. The cost per inquiry is significantly lower than in the call center, and the bot works 24/7 with an average response time under a minute. For example, one client saved $500,000 annually after deploying our FNOL chatbot.
Chatbot automation scenarios
Main scenarios: policy selection and calculation (OSAGO, CASCO, health insurance) and claims settlement (FNOL). The bot collects VIN, vehicle registration, driving experience, and calculates premiums via RSA API. For FNOL, it follows a checklist, uploads damage photos, and creates a case with a claim number. Policy renewal with reminders at 30, 14, and 7 days is also implemented.
How the FNOL pipeline works
- Claim intake — The client writes 'I've had an accident.' The bot checks for injuries and calls an ambulance if needed.
- Data collection — Date, location, other party. Damage photos, diagram.
- Verification — OCR for passport, VIN via traffic police API, policy check with RSA.
- Damage assessment — Using photos and reference data (RAG), preliminary amount.
- Case creation — A claim in the CRM with a number, document attachments, operator notification.
Chatbot vs call center comparison
| Criteria | Chatbot | Call center |
|---|---|---|
| Time per FNOL | 2–4 minutes | 15–25 minutes |
| Availability | 24/7 | 9–20 weekdays |
| Cost per inquiry | $0.50 | $5.00 |
| Process NPS | 75 | 55 |
How RAG helps the insurance bot
RAG (Retrieval-Augmented Generation) enhances the LLM by searching a knowledge base of rules, tariffs, and regulations. This prevents hallucinations and ensures calculation accuracy. More on the technology at Retrieval-Augmented Generation. The vector database (pgvector) stores thousands of documents, with search in milliseconds.
To reduce p99 latency, we cache embeddings in Redis and parallelize queries with asyncio. Retrieval accuracy is measured by recall@k—we achieve 0.95 on our projects. Fine-tuning on corporate data (LoRA, INT4 quantization) reduces model size by 30% without quality loss.
Technical details on RAG implementation
We use LangChain for orchestration, ChromaDB as the vector store, and GPT-4 for generation. The ingestion pipeline extracts text from PDFs, splits into chunks, and creates embeddings via OpenAI's text-embedding-ada-002. Chunk size is 512 tokens with 128 overlap. Retrieval uses cosine similarity with a threshold of 0.7.Reliability technologies
Stack: LLM (GPT-4, LLaMA 3), LangChain for scenarios, ChromaDB for vectors, OCR (Tesseract, Azure Form Recognizer). Deployment on Kubernetes with auto-scaling. Monitoring: p99 latency, GPU utilization, answer accuracy. We guarantee 99.9% uptime and 100 requests per second throughput.
Scope of work
| Component | Description |
|---|---|
| Process audit | Analyze current scenarios, gather requirements, assess data |
| Bot prototype | MVP with 2-3 key scenarios (FNOL, policy calculation) |
| Integrations | Connect to CRM, RSA API, traffic police API, storage systems |
| Testing | A/B tests, historical log validation, accuracy checks |
| Documentation | API specs, administration guides, model cards |
| Training | Workshop for the client's team on scenario setup and monitoring |
| Support | 24/7 SLA, quarterly model retraining, integration updates |
We accompany the project from audit to production and ongoing support. Get a consultation—we'll assess your project in one business day. Contact us for a demonstration.
Implementation process
- Audit — Review current processes: which scenarios to automate, available data, APIs.
- Design — Define architecture: LLM, RAG, integrations. Create dialogue flows.
- Implementation — Write code in Python using LangChain, set up the vector database, fine-tune if needed.
- Testing — Run on historical data, measure accuracy (accuracy, recall), response time.
- Deployment — Deploy on the client's infrastructure or in the cloud (Kubernetes, GPU instances).
- Monitoring — Set up dashboards (p99 latency, GPU utilization, escalation counts).
Timeline: MVP with FNOL and policy calculation takes 6–8 weeks; full cycle takes 3–5 months. Pricing starts at $50,000 for MVP.
Quality guarantees and support
We provide an SLA: 99.9% uptime, 15-minute incident response. Quarterly model retraining on new data to maintain accuracy. Within support, we update integrations when the insurer's API changes. Training for the client's team on bot administration (basic admin, log review, scenario adjustment).
Request a consultation—get a demo on your insurance company's real data. Contact us to discuss your project.