Experience the Efficiency: AI Chatbot Handles FNOL in 3 Minutes

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

1. Claim intake — The client writes 'I've had an accident.' The bot checks for injuries and calls an ambulance if needed.
2. Data collection — Date, location, other party. Damage photos, diagram.
3. Verification — OCR for passport, VIN via traffic police API, policy check with RSA.
4. Damage assessment — Using photos and reference data (RAG), preliminary amount.
5. Case creation — A claim in the CRM with a number, document attachments, operator notification.

Chatbot vs call center comparison

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.

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

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

1. Audit — Review current processes: which scenarios to automate, available data, APIs.
2. Design — Define architecture: LLM, RAG, integrations. Create dialogue flows.
3. Implementation — Write code in Python using LangChain, set up the vector database, fine-tune if needed.
4. Testing — Run on historical data, measure accuracy (accuracy, recall), response time.
5. Deployment — Deploy on the client's infrastructure or in the cloud (Kubernetes, GPU instances).
6. 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.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.