DataZoom

DataZoom is an AI-native document analysis platform designed for professionals who regularly work through dense stacks of legal and business documents — equity agreements, IP assignments, financial disclosures, acquisition contracts, and similar transactional materials. It combines vector-based semantic search with a retrieval-augmented generation pipeline so users can ask direct questions and receive cited, auditable answers.

The core workflow is straightforward: upload documents, let DataZoom chunk and embed them into a searchable index, then query across the full corpus in a conversational interface. Every response includes chunk-level citations pointing back to the source document. Specialized modules extend this foundation into structured workflows for cap table reconstruction, due diligence checklist automation, timeline visualization, and strategic advisory.

DataZoom is built for multi-tenant team use, with organization-scoped data isolation enforced at the database layer. It also supports fully local deployment via Ollama for teams operating in air-gapped or data-residency-constrained environments.

During due diligence, fundraising, or M&A review, professionals must read through hundreds of pages of contracts and agreements to extract specific facts, identify risks, and map relationships between parties. This process takes days to weeks, consumes senior attorney time, and is prone to missed clauses under deadline pressure.

Existing tools do not close this gap. Document storage platforms offer no semantic understanding. General-purpose AI assistants can summarize a single file but cannot reason across a corpus of 50 to 200 interrelated documents, maintain source citations, enforce organizational data isolation, or reconstruct equity ownership chains from fragmented transaction records.

• Semantic document search: Queries are embedded and matched against chunked document content using vector similarity search, returning the most relevant passages across the full uploaded corpus.
• Cited AI answers: Every response from the conversational interface includes traceable references to the specific document chunk that supports the answer. Uncited assertions are not permitted by design.
• Cap table reconstruction: An extraction pipeline parses equity agreements and transaction records to reconstruct ownership history. AI-extracted events enter a human review queue before being committed to the ledger.
• Due diligence checklists: Business-type-specific checklists surface document gaps and coverage status against standard review requirements for a given deal type.
• Timeline extraction: Date-anchored events are extracted from documents and organized into a structured timeline for rapid chronological review.
• Local deployment option: The full pipeline can run on-premises using Ollama with no external API calls, satisfying data residency requirements for regulated industries.

Transactional document review remains one of the most labor-intensive steps in deal execution. The segments DataZoom targets — private equity due diligence, corporate M&A, law firm deal support, and regulated-industry compliance — collectively represent substantial professional time spent on work that is systematic enough to be assisted by AI but complex enough that generic tools have not addressed it well.

The demand for on-premises AI processing is a concrete indicator of where the market is heading. Regulated buyers in healthcare and financial services cannot send documents to third-party cloud APIs, yet they face the same review bottlenecks as everyone else. DataZoom's local deployment path via Ollama directly addresses this constraint, which most cloud-only AI document tools do not.