Conversational Search on Proprietary Data | superluminar

Your platform holds valuable knowledge — but users still rely on keyword search or know-how to find it. superluminar replaces that friction with a conversational interface: users ask questions in plain language, and the system responds with concise, cited answers drawn from your proprietary content.

Whether you run a data platform, a knowledge base, a product catalog, or a document archive — if users need to find information in your content, this solution applies.

How We Work:

• Sparring Partner Philosophy: We challenge assumptions and co-create optimal solutions tailored to your unique needs.
• Embedded Teams: Our certified engineers and architects work alongside your team, ensuring knowledge transfer and hands-on collaboration throughout the project.
• Risk-Aware Implementation: Leveraging AWS Well-Architected Framework best practices, we proactively identify and mitigate potential risks for smooth implementation.

Core Offerings:

• RAG Architecture Design & Implementation: Serverless pipeline using AWS Lambda, SQS, DynamoDB, and Aurora Postgres with pgvector — ingesting your content and making it queryable via natural language.
• LLM Integration via Amazon Bedrock: Connect your data to Anthropic Claude or other Bedrock-hosted models to generate accurate, cited responses grounded in your content.
• Reranking & Relevance Optimization: Integration of reranking models (e.g., Cohere on SageMaker) to improve answer precision — with measured 5x speed improvements in production deployments.
• Continuous Content Sync: Auxiliary Lambda-based pipeline that periodically re-indexes your content with vector embeddings, keeping search results fresh as your data evolves.