In day-to-day operations, formatting differences can obscure substantive changes. When the old and new contracts, clauses, definitions and version metadata are spread across different screens, documents or team records, the issue may not be noticed in time.
This fragmented data delays preparation of the clause-level difference report and forces staff to search for supporting records again by hand. Before deciding, the contract lawyer must complete missing fields and verify each inconsistency individually.
A common exception is that a change in numbering or formatting may not reflect a change in content. If this context is not checked, an incomplete or incorrect automation could treat a minor difference as a risk or miss a substantive change, so the output cannot be acted on directly.
The system stops after preparing the clause-level difference report. The contract lawyer reviews the sources and the exception, then approves, corrects or rejects the output. Direct execution remains disabled because a minor difference could be treated as a risk or a substantive change could be missed; the system does not replace professional judgement.
Access to the old and new contracts, clauses, definitions and version metadata is limited to the minimum permissions needed for the task. Identifiers and sensitive fields are masked where possible, and only the necessary section is sent to the model rather than the full raw file. Access, output and the contract lawyer's decision are logged; production use does not begin until the organisation approves the retention period and data location.
A good fit
Not a good fit
The old and new contracts, clauses, definitions and version metadata are used. If a required field is missing, the system does not produce a definitive result and flags the missing source for review by the contract lawyer.
The workflow stops when the clause-level difference report is ready and waits for the contract lawyer's approval. The lawyer can correct, defer or reject the output with reasons.
The design retains the source identifier, applied rule, exception, output and human decision together. This makes it possible to review how each source record led to a recommendation.
Project led by:DijitalPi
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