Harvey ships deep, mature legal workflows on its own cloud. ibl.ai is the agentic platform your firm owns and runs entirely on its own infrastructure — so privileged client-matter data never leaves your perimeter.
Harvey is a genuinely strong legal AI platform. It powers roughly half the Am Law 100, serves 1,000+ customers across 60 countries, and ships mature, pre-built legal workflows — contract analysis, due diligence, and research — out of the box.
For many firms, that breadth of legal-specific tooling is exactly the right choice. Harvey's security posture is serious too: SOC 2 Type II, ISO 27001, tenant isolation, and contracts that respect privilege and confidentiality.
But Harvey is vendor-hosted SaaS — matter data is processed on Harvey's cloud, not the firm's own servers, and firms don't own the code. ibl.ai is for firms that want an agentic platform they own and run air-gapped, so client data never leaves their perimeter.
Harvey is the leading AI platform for legal and professional services, valued at roughly $11B after its March 2026 raise co-led by GIC and Sequoia. It serves 1,000+ customers across 60 countries — including A&O Shearman and PwC, around half of the Am Law 100, magic-circle and Big Four firms, and Fortune 500 in-house legal teams. Built on frontier models, it ships deep, pre-built legal capabilities out of the box.
| Criteria | Harvey | ibl.ai | Verdict |
|---|---|---|---|
| Where Client-Matter Data Is Processed | On Harvey's cloud infrastructure — strongly secured, but a third party's environment, not the firm's own servers | Inside the firm's own perimeter — air-gapped or on-premise, so privileged data physically never leaves | ibl.ai |
| How Privilege Is Protected | By contract and controls — tenant isolation, encryption, and privilege-respecting enterprise agreements | By design — data never leaves the firm's network, so there is no third-party processing to contract around | ibl.ai |
| Training on Customer Data | Does not train base models on customer data | Does not use your data to train any third party's model — you control all data and any tuning | Tie |
| Encryption & Tenant Isolation | Encryption in transit and at rest with tenant isolation, validated by independent audits | Encryption plus full isolation within your own infrastructure under your own controls | Tie |
| Criteria | Harvey | ibl.ai | Verdict |
|---|---|---|---|
| Air-Gapped / Disconnected Deployment | Not available — runs as a connected, vendor-hosted SaaS | Fully supported — runs in air-gapped, disconnected environments with no external connectivity | ibl.ai |
| On-Premise on the Firm's Own Hardware | Not available — hosted on Harvey's cloud infrastructure | Deploy on the firm's own servers, private cloud, or data center | ibl.ai |
| Cloud Flexibility | Runs on Harvey's chosen infrastructure | Deploy on AWS, GCP, Azure, private cloud, or hybrid — your choice | ibl.ai |
| Time to First Value | Fast — pre-built legal workflows are usable shortly after onboarding | Structured deployment; production typically within weeks as agents are configured to the firm | competitor |
| Criteria | Harvey | ibl.ai | Verdict |
|---|---|---|---|
| Source Code Ownership | None — the platform remains Harvey's; firms license access | Full source code delivered to the firm; you own and can modify it permanently | ibl.ai |
| Model Flexibility | Built on frontier models selected and managed by Harvey | Model-agnostic — run Claude, GPT, Gemini, Llama, Mistral, or your own tuned models | ibl.ai |
| Roadmap & Pricing Control | Firms depend on Harvey's roadmap and pricing decisions over time | Firm controls its own roadmap on an owned platform; flat-fee licensing, not per-seat | ibl.ai |
| Criteria | Harvey | ibl.ai | Verdict |
|---|---|---|---|
| Pre-Built Legal Workflows | Extensive and mature — contract analysis, due diligence, and legal, regulatory, and tax research ready out of the box | General agentic platform — firm-specific legal agents are built on top, not shipped pre-packaged | competitor |
| Pre-Built Legal Agents | Mature library plus 25,000+ custom agents and long-horizon multi-step automation | Build autonomous legal agents on an owned platform; no pre-packaged legal agent library at launch | competitor |
| Adoption & Benchmarking | Massive elite-firm adoption and rigorous legal benchmarking across ~50% of the Am Law 100 | 1.6M+ users across 400+ organizations, but not a legal-domain benchmarking specialist | competitor |
| Custom Firm-Specific Agents | Custom Workflow agents and Shared Spaces for secure cross-team and external collaboration | Build any custom agent on a platform you own, integrated via MCP and APIs into firm systems | Tie |
| Criteria | Harvey | ibl.ai | Verdict |
|---|---|---|---|
| Pricing Model | Vendor-managed SaaS subscription tied to Harvey's commercial terms | Flat-fee source-code licensing — one price, decoupled from seat count | ibl.ai |
| Cost at Scale | Subscription costs recur and are subject to Harvey's pricing changes | Flat-fee ownership can run materially lower than per-seat SaaS as adoption grows | ibl.ai |
| Long-Term TCO | Perpetual subscription — no ownership asset at the end | Owned platform becomes a firm asset; no perpetual access fees after licensing | ibl.ai |
Some firms decide that the strongest privilege protection is data that physically never leaves their own network. Harvey secures matter data well on its cloud, but it is still a third party's environment. ibl.ai processes data on the firm's own infrastructure.
Certain matters, government engagements, or client mandates require AI that runs disconnected from the internet. Harvey is a connected SaaS with no air-gapped option. ibl.ai is built to run in fully disconnected environments.
With Harvey, the platform remains the vendor's and firms depend on its roadmap and pricing. Some firms prefer to own the source code so the system runs on their terms, indefinitely, regardless of any vendor relationship.
Harvey selects and manages the frontier models behind its product. Firms that want to route different matters to different models — for cost, capability, or client requirements — value a model-agnostic platform they configure themselves.
Harvey ships excellent general legal tooling. Firms with distinctive practice areas or proprietary methodologies sometimes want to build their own agents on a platform they own and integrate deeply with internal systems via MCP and APIs.
As firm-wide adoption grows, recurring per-seat or subscription economics can compound. A flat-fee, owned model gives some firms more predictable long-term costs and an asset on the balance sheet rather than perpetual access fees.
ibl.ai runs on the firm's own infrastructure, air-gapped or on-premise. Privileged client-matter data is never processed on a third party's cloud — it physically never leaves the firm's network. Privilege is protected by architecture, not only by contract.
Deploy ibl.ai in environments with no internet connectivity at all — disconnected data centers and isolated networks. No external API calls, no telemetry, no cloud dependency, for the most confidentiality-sensitive legal work.
ibl.ai delivers the full platform codebase to the firm. You own it, inspect it, modify it, and run it indefinitely — with or without an ongoing vendor relationship. The platform is a firm asset, not a subscription.
ibl.ai is not tied to any single model provider. Run Claude, GPT, Gemini, Llama, Mistral, or your own tuned models, and route different matters to the most appropriate model without re-architecting the platform.
ibl.ai is an agentic platform. Firms build autonomous agents that reason, plan, and execute multi-step work, integrated into firm systems via MCP and APIs — encoding the firm's own legal playbooks on infrastructure it controls.
Every agent action is logged within the firm's environment, owned by the firm and available for compliance, supervision, and governance. The complete audit trail lives in your infrastructure, not a vendor's.
ibl.ai uses flat-fee source-code licensing rather than per-seat subscription. Costs are predictable as adoption grows across the firm, and the investment results in an owned platform rather than perpetual access fees.
Identify the legal workflows, practice areas, and confidentiality requirements you want AI to support. Define which matters require air-gapped or on-premise handling, map data residency and privilege constraints, and choose your target deployment environment.
Provision the firm's own environment — on-premise, private cloud, or air-gapped — and deploy the ibl.ai platform codebase. Configure your chosen model providers, and establish SSO, role-based access, and matter-level data isolation aligned to the firm's structure.
Build firm-specific agents for your priority use cases — contract review, due diligence, research, and intake. Encode the firm's own playbooks, and integrate document management, practice systems, and internal tooling via MCP and APIs.
Deploy to a pilot group of practice teams. Validate agent quality on real matters, confirm integration reliability, verify the audit trail meets supervision requirements, and gather structured feedback before broader rollout.
Roll out across the firm with change management and training. Establish governance and supervision processes using the owned audit trail and admin controls, and transition to ongoing ownership of the platform.
Large firms handle highly sensitive matters across many clients and jurisdictions. Harvey serves this segment extremely well, but some firms conclude that the strongest privilege posture is keeping matter data on infrastructure they own rather than any vendor's cloud.
On-premise or air-gapped deployment keeps privileged data inside the firm's perimeter while flat-fee licensing scales predictably across thousands of timekeepers.
Litigation and specialized boutiques work on adversarial matters where data exposure assumptions matter, and they often have distinctive methodologies rather than generic workflows. They want agents tuned to their practice on infrastructure they control.
Build firm-specific agents on an owned, optionally air-gapped platform that encodes the firm's own litigation and practice playbooks.
Corporate legal teams often must keep highly confidential business and matter data within the company's own security boundary, governed by the same controls as other internal systems rather than processed on an external legal SaaS.
Deploy within the company's existing infrastructure and compliance perimeter, so legal AI inherits the same governance, audit, and data-residency controls as the rest of the enterprise.
Government law offices and public-sector counsel frequently face data sovereignty mandates, classification requirements, and authorization processes that connected commercial SaaS cannot satisfy.
Air-gapped, on-premise deployment on government-controlled infrastructure with a complete, firm-owned audit trail supports sovereignty and authorization requirements.
Regulatory and compliance practices demand defensible records of how AI handled each matter and assurance that sensitive filings never leave controlled environments — areas where owning the platform and the logs is decisive.
Complete audit trails owned by the firm and data that never leaves the perimeter make AI-assisted compliance work defensible and reviewable.
Patent and IP work involves unpublished inventions and trade secrets where any external processing of disclosure material is a serious exposure concern, often before filing.
Air-gapped deployment ensures invention disclosures and pre-filing material are analyzed entirely within the firm's own network, never on third-party infrastructure.
Schedule an assessment to see how ibl.ai can replace your current platform with a solution you fully own and control.