Enterprise RAG &
Vector Search.
Turn your unstructured company documentation, PDFs, and SQL tables into instant, grounded answers. Zero hallucination risk with verified citations and enterprise role-based access control.
How we guarantee zero hallucinations.
Enterprise Data Extraction
Multi-format parsing across PDFs, Notion, Confluence, SQL schemas, and Slack with OCR table preservation.
Vector & Sparse Indexing
Generate dense embeddings and sparse SPLADE vectors with metadata payload filtering and role-based ACLs.
Neural Re-ranking
Cross-encoder scoring and contextual compression eliminate irrelevant tokens before reaching LLM context.
Grounded Synthesis
Deterministic citation linking and verifiable source footnotes delivered in sub-500ms streaming responses.
High-precision knowledge search.
Hybrid Dense + Sparse Search (BM25 + ColBERT)
Combine lexical keyword precision with multi-vector semantic representations so technical terms, SKU numbers, and colloquial questions are retrieved with 99%+ accuracy.
Contextual Document Chunking & Parent-Child Trees
Hierarchical chunking architectures that index small chunks for fast retrieval while serving full parent context to the LLM to prevent fragmented hallucinations.
Cross-Encoder Re-ranking & Context Compression
Two-stage retrieval pipelines where top-50 vector results are re-ranked with neural cross-encoders and filtered down to the top-5 most relevant context tokens.
Hallucination Defense & Source Grounding
Automated citation matching with strict attribution verification. The LLM only answers if verifiable factual ground truth exists in your indexed data.
Ready to ground your AI in your company data?
Get a working enterprise RAG prototype on your own documents in 5 days.