Contract Review

Automation handles the routine part of contract review — reading, comparing against benchmarks, identifying deviations. The lawyer receives a preliminary report and works only with the clauses that require expert judgment.

What automation does

1. Accepts the contract from file storage — PDF, DOCX, scans. Supports text recognition (OCR) for scanned documents.
2. Classifies the document type — NDA, MSA, SoW, DPA, licensing, employment, supplier agreement. The rubric applied depends on the type.
3. Extracts key fields — parties, dates, amounts, currency, jurisdiction, governing law, term, automatic renewal, termination conditions.
4. Checks clauses against the company rubric — liability limits, indemnification, SLA, confidentiality wording, disclaimer of warranties, arbitration clause.
5. Flags deviations by category: critical (blocker), material (require negotiation), minor (informational).
6. Generates a report with quotes from the contract, references to the relevant rubric clause, and suggested edits.
7. Delivers the report to the responsible lawyer via Slack, Teams, email, or the corporate DMS.

Typical configuration options

• Review against the legal rubric only (compliance-only).
• Full analysis with commercial metrics — pricing terms, rebates, volume commitments.
• Two-way review: your version vs the counterparty's version with a diff report.
• Batch mode: weekly review of active contracts for hidden obligations and upcoming renewal dates.

What automation does not do

1. Does not make legal decisions — the final judgment on the acceptability of terms remains with the lawyer.
2. Does not negotiate with the counterparty — automation prepares the material, communication with the other party goes through a human.
3. Does not replace due diligence for M&A — complex deals with multiple exhibits and cross-references require a comprehensive review from a law firm.

Technically, this is a Grow2.ai AI agent on top of an AI model and a specialized vertical-SaaS CLM platform. The model reads a contract like a junior lawyer, but without fatigue and with precision down to the wording.

Flow architecture

1. Trigger — a new file in a dedicated folder of the file storage (SharePoint, Google Drive, Dropbox, S3, corporate DMS) or drag-and-drop into the web interface.
2. Pre-processing — text extraction, OCR for scans, segmentation into sections: preamble, definitions, obligations, payment terms, IP, liability, termination, governing law, signatures.
3. Classification — the model identifies the document type and language. The type determines which rubric will be applied.
4. Entity extraction — parties, dates, amounts, percentages, cross-references between sections. The result is saved to a structured JSON.
5. Review by rubric — each contract clause is compared against the standard in the company's rubric. The LLM assesses the degree of compliance and formulates an explanation in natural language.
6. Deviation ranking — priorities are assigned according to the "risk probability × financial impact" matrix.
7. Report generation — Markdown or PDF with an executive summary, a deviations table, and suggested edits.
8. Delivery — the report is published to the legal team's Slack channel, attached to the ticket in the DMS, and sent to the responsible lawyer's email.

Implementation steps

1. Audit of the current review process — interviews with lawyers, review of 30-50 contracts of different types, a catalog of critical clauses.
2. Rubric collection and digitization — converting implicit rules ("we never accept unlimited liability") into structured checks.
3. Test sample annotation — 50-100 contracts annotated by lawyers to calibrate model accuracy.
4. Storage integration setup — service account, read permissions, webhook for new files.
5. Pilot on real contracts — 2-4 weeks of parallel work: a lawyer and the AI agent review the same contracts, comparing results.
6. Rubric calibration — adjusting thresholds, refining standard wording, adding edge-cases.
7. Rollout to the full flow — switching to automatic mode, the lawyer works only with the final report.

Solution components

Security and compliance

Grow2.ai configures the environment so that contracts do not leave the client's perimeter without explicit consent. Vertical-SaaS platforms offer on-premise or private-cloud deployment for regulated industries. LLM requests go through enterprise versions with contractual obligations on data non-disclosure — neither for model training nor for content logging.

To implement contract review automation, you need an organized library of reference clauses and access to the incoming contract flow.

What the client needs to have in place

• File storage with incoming contracts — SharePoint, Google Drive, Dropbox, S3, or a corporate DMS. Access via a service account with read permissions on the designated folder.
• Rubric review in explicit form — a list of items the legal department checks in every contract. If the rubric exists only in the lawyers' heads, the first project phase is digitizing it.
• Reference library of clauses — acceptable versions of clauses for each category (liability, indemnification, termination). 20-50 examples are sufficient to start.
• Historical contracts with review results — 50-100 contracts with flagged issues for calibration and validation.
• Legal project sponsor — the person who makes decisions on the rubric and participates in calibration.

Team readiness

• The legal department is ready to revise the review process: what automation handles, what remains with the human.
• The operations manager agrees on the review SLA — for example, 24 hours to deliver a report.
• IT provides access to the file storage and configures corporate SSO for the vertical-SaaS platform.

Implementation timeline

The project takes 6-10 weeks: 2 weeks for the audit and rubric collection, 2-3 weeks for configuration and calibration, 2-4 weeks for a pilot in parallel mode, 1 week for rollout. The timeline grows if the rubric is not digitized or on-premise deployment is required in a regulated industry.