Real-Time Claims and Incident Intelligence Accelerator

VigourSoft builds a real-time semantic search pipeline that captures claims, support tickets, or operational incidents as they happen, generates a vector embedding for each one, and stores it in Amazon Aurora PostgreSQL using the pgvector extension. When a new case comes in, the system finds the most similar past cases by meaning rather than exact keyword match, and can optionally use Amazon Bedrock to generate a suggested root cause and fix grounded in those matches. We assess your event volume and team structure up front to recommend the right ingestion pattern, Amazon MSK for high-volume, multi-consumer, or shared event streams, or a simpler queue-based pattern where a full streaming platform is not yet justified.

Scope of Work:

• Data source and event-volume assessment to confirm whether Amazon MSK or a simpler queue-based pattern fits your workload
• Embedding pipeline design and deployment (event consumer, embedding generation, crash-safe processing so no event is lost)
• Amazon Aurora PostgreSQL with pgvector configuration for similarity search, including index strategy as data volume grows
• Search API deployment for finding similar past cases by meaning
• Optional Amazon Bedrock integration to generate a grounded root-cause and fix suggestion from the top matches
• Seeding historical claims, tickets, or incident data so the system has useful matches from day one

Deliverables:

• Deployed embedding and ingestion pipeline with crash-safe, at-least-once processing
• Aurora PostgreSQL cluster with pgvector configured for similarity search
• Search API returning similar historical cases by meaning
• Event volume assessment report with an ingestion pattern recommendation (Amazon MSK versus a simpler queue-based approach)
• Optional Amazon Bedrock-grounded root-cause and fix recommendation feature

Target Audience: Banking, financial services, and insurance companies whose claims desks, support teams, or IT operations teams re-diagnose the same class of problem repeatedly because past resolutions are not searchable by meaning, and who want a semantic search and root-cause suggestion capability without adopting a separate vector database service.

Outcome Summary: Support and claims teams can describe a new case in plain language and immediately see similar past cases along with how they were resolved, instead of starting every diagnosis from scratch. The system runs on infrastructure most teams already have, Aurora PostgreSQL, and comes with a clear, honest recommendation on whether the event volume justifies a dedicated streaming platform like Amazon MSK.