Retrieval-based AI that connects directly to your structured knowledge sources. Provide accurate, context-aware responses to your teams and customers with zero hallucinations.
Traditional chatbots are static and generic AI tools hallucinate. When your team needs documentation or your customers need support, "almost right" is a failure.
Static bots fail when faced with specific internal policies or niche technical questions.
Generic LLMs often make up facts when they don't have the specific context, creating liability.
Valuable intelligence scattered across PDFs, Slack, and Wikis remains inaccessible to teams.
How RAG ensures your assistant stays grounded in your unique knowledge.
We vectorize your documents, databases, and wikis, turning them into a searchable mathematical space.
When a question is asked, the system instantly pulls the top relevant snippets from your knowledge base.
The AI combines retrieved facts with language logic to provide an accurate, cited, and safe answer.
Connects directly to your repositories, indexed data, and secure knowledge bases for precise context retrieval.
Seamlessly works with PDFs, SQL databases, and internal documentation to ground AI responses in reality.
Cloud-native infrastructure designed with strict data isolation and private access controls.
High-performance vector storage ensuring sub-second retrieval times even with million-page documentation.
Embed your assistant in Slack, Discord, your website, or mobile app via our robust universal API.
Built-in fact-checking layer that prevents the AI from answering if the retrieved context is insufficient.
Transform how your organization accesses its collective intelligence.
Users find answers themselves through self-serve AI.
Teams save hours searching through internal wikis.
Support tickets
Expert Availability
Hallucinations
Search Speed
Technical clarity on our RAG architecture.