Defensible Deal Intelligence
For teams reviewing VDRs, contracts, financial models, and diligence files, we build the intelligence layer that evaluates files simultaneously, compares them against deal playbooks, flags liabilities, and keeps every finding tied to source evidence.
What changes for your team
The pain is rarely just reading. It is intake, file classification, duplicate checking, table extraction, term comparison, missing-document flags, source verification, reviewer questions, and converting findings into the team's workpaper, memo, issue list, or report format.
We prepare entity and clause extraction, multi-document comparison, checklist completion, policy-control mapping, exception lists, issue trackers, reviewer notes, and export-ready packets so experts start from a source-backed draft instead of a blank sheet.
1. Intake: documents are synced from VDRs, SharePoint, drives, or secure upload paths. 2. Parsing: OCR and table extraction normalize text, tables, screenshots, and attachments. 3. Analysis: workflow logic extracts defined schemas and compares them against your playbook. 4. Review: findings appear in a queue with source highlights, exception reasons, and export paths.
For confidential work, we define the deployment boundary before model use. The workflow can run inside a VPC or approved private environment with RBAC, masking where needed, audit logs, and reviewer history.
How it works
Agree on the work, information, business rules, user roles, desired output, and what the AI is allowed to do.
Bring in only the approved documents, records, business data, control libraries, or playbooks needed for the task.
Give your team findings, matrices, notes, data-backed answers, or work queues with sources and clear reasons for exceptions.
Track edits, accuracy, time saved, exception quality, and adoption before applying the system to more work.
FAQ
No. It prepares the first-pass extraction, comparison, and source pack. Human experts review, validate, edit, and make the final decision.
Yes, when the source quality is workable. We use OCR and table extraction before analysis, then flag low-confidence or unreadable areas for human review.
Technology partnership
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