Legal AI startup Ivo raises $55 million to scale its platform
Ivo has secured $55 million in a Series B funding round to accelerate its legal services platform and expand sales. The round was led by existing backer Blackbird and values the company at around $355 million, according to a person familiar with the deal.
New investors include Costanoa Ventures, Uncork Capital, Fika Ventures, GD1, and Icehouse Ventures. The message is clear: buyers in legal are spending, and vendors with capital will move faster on product, security, and enterprise support.
Why it matters for legal teams
More funding means faster product cycles-expect sharper tools for contract work, research assistance, and intake workflows. It also means more sales outreach. If you've been evaluating AI vendors, the timing favors structured pilots and stronger pricing negotiations.
For partners and GCs, the question isn't "if" but "how" to implement with control: data security, auditability, and attorney oversight must sit at the center.
Practical implications you can act on
- Expect upgrades to privacy controls, redaction, and on-prem/private cloud options.
- Push for clearer performance metrics (accuracy by task, failure modes, and review rates).
- Negotiate enterprise terms: SSO, role-based access, retention windows, and data residency.
- Run time-boxed pilots on narrow, high-volume tasks (e.g., NDAs, playbooked clauses).
- Track measurable outcomes: turnaround time, review coverage, and escalation rates.
Due diligence questions to ask Ivo (or any legal AI vendor)
- Security: Do you have current SOC 2 Type II/ISO 27001? How do you segregate tenant data? Any third-party sub-processors?
- Data use: Is customer data used for model training by default? Can we opt out at the tenant and workspace level?
- Confidentiality: How is PII/privileged material handled? Built-in redaction? Field-level encryption?
- Quality: What is the measured error rate for our specific use cases? How is attorney-in-the-loop enforced?
- Traceability: Do you provide full prompts, sources, and action logs for audit and client disclosure?
- Deployment: Options for private cloud/VPC? Data residency controls? On-prem connectors for DMS (iManage/NetDocuments) and M365?
- Governance: Fine-grained permissions, holdbacks for sensitive matters, and conflict controls across clients and matters?
- Integrations: Native connectors for eDiscovery, CLM, intake/ITSM, and SSO (Okta/Azure AD)?
- Pricing: Seat vs. usage model, overage rates, and caps during pilots. How are tokens/attachments billed?
- Continuity: Runway post-round, key SLAs, uptime history, and breach notification timelines.
What to watch next
- Product releases focused on contract review, drafting aides, and workflow automation.
- Enterprise references with Am Law firms and Fortune 500 legal departments.
- Partnerships with DMS, CLM, and eDiscovery vendors-real integrations, not just logos.
- Clear policies on data usage and opt-outs as vendors chase model improvements.
- Guidance from bar associations on competence and disclosure for AI-assisted work.
Tech competence is now part of professional duty in many jurisdictions. If your team uses AI, build policies, training, and supervision that align with that standard. See the ABA's Model Rule 1.1 (Comment 8) for context: ABA Model Rule 1.1.
If you're standing up an internal enablement plan-prompts, review protocols, and governance-this curated set of courses can help your team ramp with guardrails: AI courses by job.
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