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OpenAI Software / SaaS United States

Ironclad

Document Processing

Ironclad needed to automate contract review without sacrificing control, precision, or privacy because legal teams were spending too much time on manual redlining and policy checks.

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An initial pass at contract redlining usually takes about 40 minutes
With AI Assist, we're seeing users complete them in two minutes

Reported impact

Business outcome

Ironclad cut the time needed for an initial contract-redlining pass from forty minutes to two, accelerated adoption of AI review, and gave legal teams a governed way to work faster without removing human judgment.

  • Compliance
  • Productivity Improvement

Solution

Ironclad embedded GPT-4 into AI Assist to automate the first pass of contract review inside its contract-lifecycle platform. The application identifies irregularities, proposes redlines, surfaces pre-approved clauses, and follows company-specific AI Playbooks so legal teams can work from their own policies rather than from generic drafting rules. Contract text enters Ironclad's editor, GPT-4 and the AI Assist workflow generate suggested changes and supporting language, and users then accept, reject, or disable those suggestions under human oversight. Ironclad also kept data-governance concerns front and center by using the API in a way that does not train OpenAI on customer-submitted data by default. That architecture makes AI a directly embedded legal-workflow feature rather than a detached assistant, which is why teams can move from manual review to governed automation in the same editor.

Data flow

Contract language enters Ironclad's editor, GPT-4 powers AI Assist to identify issues and draft redlines against AI Playbooks, and the resulting suggestions are surfaced to users who review, accept, reject, or turn off the AI output as needed.

Solution architecture

3 components · 3 layers
  1. Compute
    • GPT-4 Provides the instruction-following and language-generation capability behind AI Assist's review workflow.
  2. Serving
    • AI Assist™ Automates contract review by flagging issues, proposing redlines, and surfacing pre-approved language.
  3. Governance
    • AI Playbooks Supply company-specific clause guidance and policy rules for the review process.

Architecture clues

  • AI Assist was ported to GPT-4 as soon as it launched in April 2023
  • Human oversight is built into the workflow through accept, reject, and disable controls
  • OpenAI does not use data submitted via the API to train or improve its models by default

Evidence from the source

AI Assist empowers users to automate legal review by identifying and redlining irregularities in contracts
An initial pass at contract redlining usually takes about 40 minutes. With AI Assist, we're seeing users complete them in two minutes
Human oversight is baked into the workflow, giving users the ability to accept or reject all automated suggestions
The product also provides pre-approved clauses and language guidelines based on company-specific AI Playbooks
Transparency Evidence & methodology 90/100 reference value · 94% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    Ironclad customer story

    First-party
    https://openai.com/index/ironclad/
    Source type
    Vendor-published customer story
    Published
    Jan 4, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. LeadIQ

    Ironclad Company Overview, Contact Details & Competitors | LeadIQ

    Independent secondary
    https://leadiq.com/c/ironclad/5a1d88ca240000240062623d

    The profile markup includes "numberOfEmployees":{..."value":883}" and "addressCountry":"United States"."

    Source type
    Structured company profile
    Supports
    • Company size
    • Region

Date evidence

  • Published on source page Jan 4, 2024 · OpenAI · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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