Enterprise AI Search
Fueled by Advanced RAG
Standard databases miss context, and basic bots make up answers. Our state-of-the-art Retrieval-Augmented Generation (RAG) indices, chunks, and filters your private business documents to retrieve exact contextual answers containing clear citations.
How our secure RAG Pipeline works
We convert your private documents into numeric vectors, allowing semantic matching that reads intent rather than simple spelling.
Chunk & Ingest
Your documents are split into precise, overlapping segments (2,400 chars with 300 char overlap) so no vital context is sliced or lost.
Vector Embedding
Segments are mapped mathematically into a secure, single-tenant Qdrant database, translating words into multi-dimensional vectors.
Score & Answer
When asked, we score matching blocks (min threshold 0.35). Validated snippets are summarized by GPT, complete with direct URL citations.
RAG & Enterprise Search FAQ
Precise, direct information regarding Retrieval-Augmented Generation (RAG) systems.
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a technique that references an external knowledge base to feed reliable, contextual data directly to a Large Language Model (LLM) prompt before generating a response. This guarantees answers remain accurate, grounded, and specific to your proprietary data, avoiding generic AI hallucinations.
What is semantic similarity scoring, and why do you enforce it?
When a query is made, we query our Qdrant vector store and rate retrieved text fragments on a similarity scale. We enforce a minimum score threshold of 0.35. If no document meets this limit, the assistant flags a "knowledge gap" and informs the user rather than guessing—saving you money on LLM overhead and protecting accuracy.
How often are connected enterprise sources synchronized?
We support both real-time webhooks (for apps like Asana) and dynamic polling schedules (every 15 minutes for Notion and Google Drive). Synced documents are parsed and fully re-indexed using smart checksum guards, preventing redundant vectorizing fees.
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