Using GenAI to Automate Customer Complaint Resolution for a Global Insurance Provider

Quick Summary

Challenge

A global insurance firm was dealing with rising complaint volumes and long resolution cycles due to manual triaging and limited visibility into customer intent.

Solution

Tatras built a GenAI-powered complaint resolution engine using a Retrieval-Augmented Generation (RAG) system and sentiment-aware classifiers to prioritize, summarize, and suggest resolution paths.

Result

70% faster complaint resolution time and 40% drop in escalation volume.

Tech Stack

AI & ML: Open-source LLMs Sentiment classifiers Retrieval-Augmented Generation (RAG) LangChain | Data & ETL: CRM databases Vector stores (e.g., FAISS or Chroma) Regulatory policy ingestion pipelines | Infra & Deployment: On-prem Kubernetes REST APIs for CRM integration Private inference servers | Tooling: Prompt engineering frameworks Multi-agent coordination layer Human-in-the-loop feedback system

The Challenge

One of the world’s largest insurance providers was receiving thousands of daily support tickets across multiple lines of business ranging from delayed claim processing to ambiguous policy terms. The support team was overwhelmed by:

Manually reading and classifying every complaint
Cross-referencing prior tickets or policies
Routing issues to the correct team
Writing resolution responses that aligned with regulatory frameworks

The company wanted to automate these workflows without sacrificing accuracy or compliance — and reduce the time it took to get back to customers.

A Day in the Life: Before Our Solution

Support agents would log in each morning to hundreds of complaints from policyholders. Many were long and emotionally charged, buried in complex language. Agents had to scan through multiple systems to understand the customer's policy, prior communication, and status of any claims. Meanwhile, SLAs were frequently breached and team morale was slipping. Escalations were rising — often over simple misunderstandings.

Solution

Tatras designed a multi-layered complaint handling engine using open-source LLMs and a Retrieval-Augmented Generation (RAG) framework. The system automates the full lifecycle of a customer complaint. To address these challenges, we designed a system that blends predictive modeling with on-the-ground reality.

1. Core Innovation

At the heart of the system lies a multi-layered GenAI engine designed to understand both what the customer is saying and how they’re saying it. A sentiment and intent detection module reads emotional cues and flags high-risk or urgent complaints early in the process.

Behind the scenes, a dynamic RAG (Retrieval-Augmented Generation) framework pulls in data from CRM history, policy documents, and regulatory guidelines to ensure the system generates grounded, reliable responses.

To handle the complexity of insurance workflows, a multi-agent architecture routes each query to specialized agents — whether it's billing, legal, or policy interpretation — and composes a unified response. When a case is particularly nuanced or sensitive, the system flags it for early human review, providing a pre-written summary to accelerate triage without losing context.

2. Key Features

Automated prioritization based on SLA and risk profile
Context-aware response drafts with links to source documents
Dashboards for supervisors to track response quality and trends
On-prem deployment with complete data privacy and compliance

3. Workflow Integration

The GenAI engine is embedded into the company’s existing CRM system. Agents now receive summarized complaint context, recommended actions, and editable response drafts — all within the same dashboard. No more switching between tabs or wasting time on repetitive issues.

Outcomes

70% faster complaint triage and response time

40% reduction in escalations to management

Improved customer satisfaction scores

Better agent productivity and morale

Reduced regulatory risk with consistent, traceable responses