Icertis acquired Dioptra, an AI company focused on playbook-driven, pre-signature contract review and accuracy. Icertis positions this as a step toward more autonomous contract review using its contract intelligence platform and data.
Background of the companies
Icertis
- Founded in 2009.
- Provides Contract Lifecycle Management and what it calls Contract Intelligence software.
- Positions its platform as centralizing contracts, integrating with systems like CRM and ERP, and applying AI over unified contract data.
Dioptra
- Focuses on AI for legal and contract workflows.
- Builds tools for automated redlining based on customer playbooks and for tracking and improving AI accuracy for legal teams.
Who buys their product
- Large and upper mid-market enterprises.
- Main buyer and influencer groups:
- General counsel, legal, and legal operations.
- Procurement and sourcing leadership.
- Sales operations and revenue operations.
- CIOs and CTOs when CLM is treated as a core application and data platform.
- Common industries include technology, manufacturing, life sciences, financial services, telecom, and other contract-heavy sectors.
What is the benefit of this news
According to Icertis, the Dioptra acquisition enables:
- AI-driven redlining aligned with each customer’s legal playbooks.
- Automated first-line review of third-party contracts before signature.
- Faster creation and refinement of playbooks using existing executed contracts and history.
- Use of Icertis’s unified contract data as context to improve reliability and explainability of AI behavior.
Icertis presents the outcome as more contract volume processed by AI agents, with legal teams focusing on non-standard and higher-risk work.
Who is the competition
Vendors that often show up in the same or adjacent evaluations:
-Conga, including legacy Apttus
- Suite covering CPQ, CLM, and revenue lifecycle.
- Frequently positioned around Salesforce-centric quote-to-cash and revenue processes.
- Ironclad
- CLM focused on collaboration and workflows across business and legal teams.
- Often evaluated where front-office usability and deal workflows are a priority.
- DocuSign CLM and Insight
- E-signature plus CLM and contract analytics.
- Often considered where organizations already standardize on DocuSign for agreements.
- Agiloft
- CLM platform with a strong configurability message.
- Considered where buyers want to tailor workflows and data structures extensively.
- Evisort and other AI-first tools
- Emphasize AI for ingestion, classification, and search over large volumes of existing contracts.
- Often used for contract discovery and analytics, with or without a full CLM rollout.
Zoho Contracts and Zoho ecosystem
- Zoho offers Zoho Contracts as part of its broader business application suite.
- Positions Zoho Contracts for organizations that want CLM integrated with other Zoho apps such as CRM, finance, and collaboration tools.
- Often evaluated by mid-market and smaller enterprises that already use Zoho or prefer a suite approach over multiple point solutions.
What do people think about when buying this category of software
Typical questions and evaluation themes:
- Data and integrations
- How does the platform model contract data, clauses, and obligations.
- Which systems it integrates with out of the box, such as CRM, ERP, P2P, and e-signature.
- How legacy contracts are ingested, normalized, and made searchable.
- AI behavior and trust
- How vendors measure and report accuracy for extraction and redlining.
- How legal and legal operations can configure and govern playbooks and AI rules.
- How decisions, suggestions, and overrides are logged and made auditable.
- Workflows and ownership
- How business users request, generate, and negotiate contracts within the tool.
- How sales, procurement, finance, and legal processes are supported in one platform.
- Whether configuration is primarily handled by admins, vendor services, or IT.
- Change management and time to value
- Implementation effort and timelines for basic CLM and then AI-enabled review.
- Recommended rollout path by contract type, such as NDA first, then sales, then procurement.
- Risk, compliance, and audit
- How the system records approvals, deviations, and exception handling.
- How reports support compliance, internal audit, and external regulatory needs.
Advice to CIOs and CTOs
1. Decide the role of CLM
- Clarify whether CLM is a system of record and intelligence for contracts or mainly a workflow layer.
- If it is a system of record, prioritize platforms with a clear data model, strong integration roadmap, and governance story. Icertis explicitly positions itself in this direction.
2. Treat AI as part of risk and governance
- Ask each vendor, including Icertis, to share concrete accuracy metrics, evaluation methods, and examples.
- Ensure legal can control playbooks, thresholds, and what is fully automated versus advisory.
- Confirm that every AI action and recommendation is traceable for audit.
3. Start with narrow, high-volume use cases
- Begin with contract types like NDAs, low-complexity sales agreements, or standard vendor forms.
- Use these to test pre-signature AI review, routing, and auditability before expanding.
4. Align with your existing ecosystem
- Heavy Salesforce and CPQ plus revenue lifecycle needs often bring Conga into scope.
- Broader enterprise contract footprint across sales, procurement, and supply chain often brings Icertis and similar platforms into scope.
- Strong need for discovery across scattered legacy contracts can justify AI-first tools alongside or before a CLM change.
5. Plan for data portability
- Require clear export options for structured contract data, AI-enriched metadata, and playbooks.
- This supports future migrations, consolidations, or parallel use with other systems.
Comments