HyperCounsel

HyperCounsel is a Microsoft Word add-in that provides AI-assisted drafting, redlining, review, and negotiation support for contracts, letters, and other legal documents, working directly within an open Word file rather than a separate web platform.

Key features and functions include:

Draft Mode
Originates new documents from a blank page or an uploaded precedent. The tool asks clarifying questions to fill gaps in the brief, produces a section-by-section framework for the lawyer to approve, and then drafts the document, incorporating jurisdiction-specific research before delivery into Word as an auto-numbered draft.

Native Tracked-Change Redlining
Proposed edits are inserted as native Word tracked changes for the lawyer to accept or reject, rather than delivered as a separate chat transcript to copy across manually.

Consequential-Change Detection
When a clause is edited, the tool identifies related knock-on changes elsewhere in the document, including affected defined terms, cross-references, reciprocal obligations, and survival clauses.

Severity-Graded Review Comments
Issues identified during review are inserted as margin comments graded by severity, which can be exported as a structured deviation schedule.

Opposing-Party Redline and Comment Negotiation
Provides a guided workflow for reviewing incoming redlines from opposing counsel and can draft threaded replies to incoming comments based on a stated negotiating posture.

Clause Library and Playbooks
Builds a searchable clause library from a firm's own past contracts and can run drafting or review against an uploaded firm playbook or reference agreement.

Formatting and Proofing Audits
Checks a document for issues such as orphaned defined terms, broken cross-references, duplicate numbering, and monetary inconsistencies.

Legal Research and Citation Verification
Can research a highlighted clause against current legislation and case law, and checks cited authorities against sources such as CourtListener, the eCFR, and state codes before presenting them.

Data Protection
A local model anonymizes personal information, such as client names and ID numbers, before any document text is sent to an external AI provider. If the anonymization step fails, no data is transmitted. Three configurable protection levels and custom protected term lists are available.