AI Due Diligence Automation
Build a diligence workflow around your firm's playbook: classify VDR files, extract relevant terms, flag missing support, compare contracts and schedules, and prepare source-backed findings before senior review and IC pressure builds.
Workflow Value
Deal teams receive VDRs with inconsistent folder structures, duplicate files, scanned PDFs, late uploads, and disconnected schedules under compressed timelines. Analysts lose time classifying documents, extracting covenants and change-of-control clauses, checking employee liabilities, comparing customer concentration, and turning findings into a memo format a reviewer can trust.
We build pipelines that ingest CIMs, LPAs, MSAs, vendor contracts, customer schedules, board materials, financial schedules, and management files, then extract agreed data points into structured comparison matrices for analyst review.
Instead of open-ended chatting with documents, the workflow compares files against your firm's risk taxonomy and diligence checklist. It surfaces deviations, missing clauses, unusual obligations, inconsistent figures, and open questions with source references for human judgment.
M&A data is highly confidential and often subject to client, bidder, and counsel restrictions. We design the diligence workflow around the approved data boundary, including dedicated VPC, private cloud, tenant isolation, restricted retention, audit logs, and client-approved model endpoints where required.
Workflow Architecture
Every solution is implemented as a controlled workflow, not a loose chatbot. The system operates inside approved data and tool scopes, produces inspectable outputs, and routes judgment back to the right human owner.
Define the exact workflow, input sources, business rules, user roles, output format, and what the AI agent is allowed to do.
Pull only approved documents, records, ERP context, control libraries, or playbooks before the agent drafts or acts.
Generate findings, matrices, notes, SQL-backed answers, or queues with source references, exception reasons, and confidence signals.
Measure reviewer edits, pass/partial/fail outcomes, time saved, exception quality, and adoption before moving to adjacent workflows.
FAQ
Yes, within practical source-quality limits. OCR and table extraction are used first, and low-confidence pages or unreadable tables are flagged for human review.
No. The AI does the exhaustive parsing, extraction, and flagging. It provides exact citations to the source documents so the diligence team can verify the findings and focus on valuation and strategy rather than data entry.
Bring the workpaper, evidence review, diligence process, ERP answer queue, or reporting loop that consumes the most hours. We will map a practical workflow around your methodology.