DOCUMENT OPS

Inbox Redline Reviewer: Diff Emailed Contract Drafts Against Your Liability Playbook

When a contract draft arrives as an email attachment, it diffs the incoming indemnity and liability terms against your standard playbook positions and replies with a summary…

CategoryDocument Ops
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerContract draft arrives as email attachmentGmailGmail
  • ActionExtract attachment text
  • ActionCompare clauses against liability playbook positionsOpenAI
  • LogicAssemble off-policy and marginal clauses into a summary
  • OutputReply to reviewer with clause-by-clause assessmentGmailGmail

What it does

Watches a shared legal inbox for incoming contract attachments, extracts the indemnity and liability terms, and compares them not just to the prior version but to your team's standard playbook positions and approved fallbacks. It then drafts a reply summarizing which clauses are inside policy and which need negotiation, so reviewers start from an assessment instead of a blank page.

When to use it

Use it when counterparty drafts arrive by email and you want an instant read on how far each risk clause deviates from your acceptable range. Best for legal teams with a documented playbook who want consistent first-pass triage across every incoming draft.

How it works

  1. 1An email with a contract attachment arrives in the monitored inbox and triggers the run.
  2. 2The workflow extracts the attachment text.
  3. 3An OpenAI step compares the incoming indemnity and liability clauses against the playbook positions and flags each as within fallback, marginal, or off-policy.
  4. 4A logic step assembles the off-policy and marginal clauses into a prioritized review summary.
  5. 5The workflow sends a reply email to the reviewer with the clause-by-clause assessment.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GmailRead, draft, send, label.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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