Due Diligence Assistant

Integrates heterogeneous data sources (financial databases, regulatory filings, corporate records, web sources) into a unified document store accessible via MCP protocol. Uses a source-agnostic indexing layer that normalizes metadata and content formats, enabling cross-source search and retrieval without requiring clients to manage individual API connections or authentication.

Parses unstructured documents (PDFs, Word files, regulatory filings) to extract key entities, financial metrics, risk factors, and contractual terms into structured formats (JSON, tables). Uses pattern matching, NLP-based entity recognition, and domain-specific parsers for financial statements and legal clauses to normalize heterogeneous document formats into queryable data structures.

Analyzes organizational documents (org charts, board minutes, shareholder records, management bios) to extract stakeholder information, identify key decision-makers, and map organizational structure. Implements relationship mapping to identify conflicts of interest, related-party transactions, and governance issues. Flags unusual ownership structures or control mechanisms requiring legal review.

Analyzes multiple documents (e.g., target company financials vs. industry benchmarks, current contracts vs. proposed amendments) to identify discrepancies, inconsistencies, and missing information. Uses semantic comparison and structured data diffing to highlight gaps in due diligence coverage and flag material differences that require investigation.

Scans documents and extracted data for predefined risk categories (financial, legal, operational, regulatory, reputational) and assigns severity scores based on materiality, frequency, and business impact. Uses rule-based detection, keyword matching, and LLM-based reasoning to identify issues and contextualize them within the deal scope.

Generates structured due diligence reports by combining extracted data, comparative analyses, risk assessments, and LLM-generated insights into customizable templates (executive summary, detailed findings, risk matrix, recommendation). Uses template engines to format output and supports multiple output formats (PDF, Word, HTML) for stakeholder distribution.

Enables LLM clients to ask natural language questions about due diligence documents and receive answers grounded in extracted data and document content. Uses retrieval-augmented generation (RAG) to fetch relevant document excerpts and structured data, then uses LLM reasoning to synthesize answers with citations and confidence levels.

Coordinates multi-step due diligence workflows (document collection → extraction → analysis → risk assessment → reporting) via MCP, managing state, dependencies, and error handling across steps. Enables definition of custom workflows as sequences of MCP tool calls with conditional logic and parallel execution where applicable.

Connects to external data providers (SEC EDGAR, Bloomberg, Crunchbase, company registries, legal databases) via standardized API adapters exposed as MCP tools. Handles authentication, rate limiting, and data normalization to present a unified interface for accessing diverse external data sources.

Records all due diligence activities (documents accessed, analyses performed, decisions made, reports generated) in an immutable audit log with timestamps, user attribution, and data lineage. Enables compliance teams to demonstrate that due diligence was conducted systematically and to trace findings back to source documents.

Extracts key contractual terms and legal clauses (liability limitations, indemnification, termination rights, payment terms, non-compete) from contracts and legal documents using LLM-based extraction with legal taxonomy. Implements clause categorization and comparison across multiple contracts (e.g., comparing customer contracts to identify outlier terms). Flags unusual or unfavorable terms requiring legal review. Maintains clause library for pattern matching and anomaly detection.