AI document search that understands what you mean
Search across thousands of contracts, reports and files by meaning, not keywords — and get cited answers, not just a list of documents to open.
What this means for your business
When the answer is buried somewhere in thousands of documents, keyword search fails you — it needs the exact term, misses synonyms and returns a pile of files to open. The information is there; finding it is the bottleneck.
AI document search matches meaning. Ask a question in plain language and it retrieves the right passages across your whole corpus, then answers directly with a citation to the source file and section. It handles PDFs, contracts, scanned documents and tables.
We build it with access control and audit logging so sensitive files stay restricted and every search is traceable. The outcome is research and review that takes minutes instead of hours, grounded in your real documents rather than a model's guess.
What we build
Semantic search
Finds the right passages by meaning across your whole corpus, not just exact keywords.
Cited answers
Answers the question directly with a link to the exact source file and section.
Handles real files
PDFs, contracts, scanned documents and tables, with OCR where needed.
Cross-document insight
Surfaces and compares related passages across many documents at once.
Access control
Per-user and per-folder permissions so sensitive files stay restricted.
Audit logging
Every search and result is logged for a traceable, compliance-friendly record.
Implementation details
| Capability Parameter | System Specification |
|---|---|
| Ingestion | PDF, Office, scanned and image documents with OCR and table extraction |
| Retrieval | Hybrid vector + keyword search with cross-encoder reranking |
| Grounding | Cited answers with source file and section; abstention when absent |
| Access control | Per-user and per-folder permissions enforced in retrieval |
| Security | Audit logging, encryption in transit and at rest |
| Typical budget | ₹20L–₹45L / $20k–$55k depending on corpus size |