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Executive Summary
Our client, a leading insurance technology company, partnered with our technology team to modernise its agent search functionality using DeepQuery Navigator. The legacy search was slow, inaccurate, and challenging to maintain. The client significantly enhanced search speed, accuracy, and relevance for internal and external users by leveraging AI-driven search capabilities and AWS cloud infrastructure. This transformation resulted in a 60% faster query response time and a 40% improvement in user satisfaction scores.
Client Profile and Core Challenges
Customer Information
Client: Confidential
Industry: Insurance Technology
Location: Chicago, Illinois, USA
Company Size: 400+ employees
Business Challenges
The insurance provider needed to overhaul its outdated and inefficient agent search functionality. Users struggled with slow load times, incomplete search results, and limited filtering options. The legacy system was built on rigid architecture, making it hard to adapt to evolving business needs. Compliance with data privacy regulations and high availability requirements further complicated the process. As the company scaled operations, there was increasing pressure to provide seamless search capabilities for agents, support staff, and customers.
Technical Challenges
The existing infrastructure suffered from technical debt and lack of modularity. Legacy databases were difficult to scale and lacked support for real-time indexing. Integration with customer profiles, licensing data, and third-party verification services was complex and fragile. The client also needed to address search performance issues across different geographic regions, ensure high availability, and maintain compliance with SOC 2 and HIPAA requirements.
Our AI-Driven Solution Architecture on AWS
Solution Overview
The new architecture leverages a microservices-based multi-agent framework deployed on AWS EKS clusters. Key agents include a Supervisor Agent (React Agent), RAG Agent, and SQL Agent. The system supports unstructured and structured data sources by embedding pipelines and real-time indexing into Qdrant VectorDB.
AWS Services Used
- Amazon EKS: Hosted multi-agent services and frontend
- Amazon S3: For unstructured data
- Amazon RDS: For structured data
- Qdrant VectorDB: Vector database for semantic search
- AWS CodePipeline: Embedding and deployment pipeline
- Amazon CloudWatch: Monitoring and observability
- IAM: Access control and security
Architecture Diagram
Implementation Details
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Agile methodology with bi-weekly sprints
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Microservices hosted on EKS with CI/CD via CodePipeline
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Embedding pipeline populates Qdrant VectorDB from S3 (unstructured data)
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SQL Agent integrates with RDS for structured queries
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Supervisor Agent orchestrates RAG Agent and SQL Agent
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Real-time data sync with monitoring via CloudWatch
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Frontend enables agent chat and data configuration interface
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Data checkpoints and history stored securely for traceability
Innovation and Best Practices
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Applied AWS Well-Architected principles
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Used NLP and retrieval-augmented generation (RAG)
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Enabled real-time feedback loops
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Infrastructure as Code (IaC) for reproducible deployments
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Business-user-facing dashboards for configuration
Business Impact and Measurable Results
Business Outcomes and Success Metrics
The new search experience reduced query time from 3 seconds to 1.2 seconds on average, a 60% improvement. Agent engagement increased by 35%, and support tickets related to search dropped by 50%. Enhanced filtering and personalization capabilities led to a 40% boost in user satisfaction. The client reported improved agent onboarding speed and a noticeable uptick in conversion rates from search-generated leads. ROI was achieved within six months of deployment.
Technical Benefits
Search throughput increased by 4x, and uptime improved to 99.98%. The modular architecture simplified maintenance and accelerated the release cycle by 70%. Real-time indexing ensured that new agent records were searchable within seconds. The solution reduced technical debt and provided a future-ready platform capable of scaling with the company's growth.
Overcoming Implementation Hurdles
Challenges Overcome
The biggest challenges were integrating with fragmented legacy systems and ensuring real-time data sync. Midway through implementation, unexpected schema inconsistencies caused indexing failures. The team quickly adapted by introducing a validation layer and automated schema audits. Stakeholder alignment was also a hurdle, but regular demos and feedback loops helped maintain transparency and buy-in.
Best Practices Identified
Clear documentation, early stakeholder engagement, and incremental rollouts contributed to project success. The use of IaC and CI/CD pipelines ensured rapid, safe deployments. Continuous monitoring and A/B testing enabled data-driven improvements. The emphasis on user-centric design made the solution intuitive and highly adopted.
Roadmap for Future Innovation with DeepQuery
The client plans to expand DeepQuery capabilities with multilingual support and voice search. Future phases include integration with chatbot interfaces and predictive agent recommendations using machine learning. Additional AWS services like SageMaker and Comprehend are being evaluated for further innovation. The partnership will continue to evolve with quarterly roadmap reviews and co-innovation initiatives.
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