Conversational Search on Proprietary Data | superluminar
superluminar GmbH · Operations & Productivity
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
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.
Show the rest of the publisher’s description (9 more lines)
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.
Highlights
Highlighted by the publisher on AWS Marketplace.
Ask Your Platform Like an Expert: Users type a question in natural language and receive a concise answer with direct references to the most relevant content from your own data — no schema knowledge, no keywords, no guesswork.
Privacy-First by Design: Sensitive models (e.g., reranking) run on Amazon SageMaker inside your own AWS account — your proprietary content never leaves your environment, while query performance improves up to 5x.
Serverless & Team-Ready from Day One: The fully serverless architecture (Lambda, SQS, DynamoDB) eliminates infrastructure overhead, enabling your product team to adopt the solution and iterate immediately without ops burden.
Agent build and provenance
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