Connect internal PDFs, Notion, Confluence, and SQL databases to custom Large Language Models with vector similarity search for instant, hyper-accurate answers.
// Ingestion Stream
📁 Q4_Financial_Audit.pdf (420 pages)
[-0.0124, 0.0842, 0.2311, ... 1536 dims]
Cosine Similarity Match: 0.942
"Net operating margin grew by 14.2% YoY under compliance protocol 8B."
Accuracy / Zero Hallucination
Vector Retrieval Speed
Document Types Supported
Compliant Data Isolation
We bridge the gap between static enterprise files and dynamic conversational AI.
Continuous sync pipelines extract data from Drive, Notion, Confluence, and SharePoint automatically.
Documents are split semantically into contextual blocks preserving tables, headings, and metadata.
Queries retrieve top-K relevant chunks using hybrid vector + keyword (BM25) search.
LLM generates precise answers citing exact source document page numbers and links.
Word, Excel, PowerPoint, OCR
Wikis, SOPs, Team Notes
PostgreSQL, MongoDB, Snowflake
Cloud File Sync
Your confidential IP never leaves your private cloud environment. Our RAG implementations respect existing Role-Based Access Controls (RBAC) so users only receive answers from documents they have permission to view.
We isolate your vector embeddings inside single-tenant VPC instances, wrapped in AES-256 encryption at rest and in transit.
✓ Security Protocol: Active
User: alex@company.com [Role: Tier-2 Analyst]
Access Scope: Filtered to /finance/2026/ restricted docs
Talk to our RAG architects to build a custom vector knowledge graph tailored to your organization's internal workflows.
Schedule RAG Consultation