# Air-Gapped, Owned Alternative to Harvey for Legal AI

> Source: https://ibl.ai/resources/alternatives/harvey-ai-alternative


*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.

## About Harvey

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.

**Strengths:**
- Deep legal-specific workflows out of the box — contract analysis, due diligence, and legal, regulatory, and tax research
- Mature pre-built and custom Workflow agents, with 25,000+ custom agents and long-horizon multi-step automation
- Massive elite-firm adoption and rigorous benchmarking — ~50% of the Am Law 100, plus Big Four and magic-circle firms
- Strong security and compliance posture — SOC 2 Type II, ISO 27001, tenant isolation, encryption, and no training on customer data
- Privilege-respecting enterprise contracts and independent audits from Schellman, NCC Group, and Bishop Fox
- Rapid deployment with Shared Spaces for secure collaboration across internal teams and external partners

**Limitations:**
- Vendor-hosted SaaS — privileged client-matter data is processed on Harvey's cloud, a third party's environment rather than the firm's own servers
- No fully air-gapped or on-premise-owned deployment on the firm's own hardware
- Firms do not receive or own the source code — the platform remains Harvey's
- Firms depend on Harvey's roadmap, model choices, and pricing over time
- Privilege protection is established by contract and controls, not by the data physically staying inside the firm's perimeter

## Comparison

### Data Control & Privilege

| 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 |

### Deployment

| 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 |

### Ownership & Model Choice

| 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 |

### Legal-Specific Capabilities

| 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 |

### Cost & Licensing

| 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 |

## Why ibl.ai

### Privilege by Design — Data Stays in Your Perimeter

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.

### Fully Air-Gapped Deployment

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.

### Complete Source Code Ownership

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.

### Model-Agnostic Architecture

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.

### Autonomous Agents You Build and Own

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.

### Audit Trail You Own

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.

### Flat-Fee Licensing

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.

## Migration Path

1. **Discovery and Requirements Mapping** (Week 1–2): 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.
2. **Infrastructure Provisioning and Platform Deployment** (Week 2–4): 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.
3. **Legal Agent and Workflow Configuration** (Week 3–6): 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.
4. **Pilot Rollout and Validation** (Week 5–8): 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.
5. **Firm-Wide Production Rollout** (Week 8–12): 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.

## FAQ

**Q: Does client-matter data leave our servers with ibl.ai?**

No. ibl.ai runs on the firm's own infrastructure — on-premise, in your private cloud, or fully air-gapped. Privileged client-matter data is processed entirely within your perimeter and physically never leaves your network. This differs from a vendor-hosted SaaS, where data is processed on the vendor's cloud even when it is well secured and isolated.

**Q: Can ibl.ai run air-gapped or on-premise?**

Yes. ibl.ai is built to run fully air-gapped, with no internet connectivity, external API calls, or telemetry, as well as on-premise on the firm's own hardware. This is the core architectural difference from Harvey, which is a connected, vendor-hosted SaaS without an air-gapped or on-prem-owned deployment option.

**Q: Does ibl.ai have pre-built legal workflows like Harvey?**

Honestly, no — not in the same out-of-the-box way. Harvey ships deep, mature legal-specific workflows and a large library of pre-built legal agents, and it benchmarks rigorously across elite firms. ibl.ai is a general agentic platform you own; firm-specific legal agents are built on top of it. If your priority is ready-made legal tooling on day one, Harvey is genuinely strong. If your priority is owning the platform and keeping data air-gapped, ibl.ai is the better fit.

**Q: Is ibl.ai a replacement for Harvey or a complement?**

It depends on the firm. Harvey excels at pre-built legal workflows, adoption, and benchmarking. ibl.ai excels at deployment sovereignty, source-code ownership, air-gapped operation, and model choice. Some firms choose ibl.ai for the most confidential matters or for a platform they own outright, while others may use Harvey for broad out-of-the-box legal tooling. The two are not mutually exclusive.

**Q: How does ibl.ai protect attorney-client privilege?**

By keeping privileged data inside the firm's own perimeter. Because ibl.ai runs on infrastructure the firm controls, there is no third-party processing of matter data — privilege is protected by architecture rather than only by contractual terms. Harvey protects confidentiality through strong controls and privilege-respecting contracts, but the data is still processed in its cloud environment.

**Q: Which underlying AI models can ibl.ai use?**

ibl.ai is model-agnostic. You can run Claude, GPT, Gemini, Llama, Mistral, or your own fine-tuned models, and route different matters to different models based on cost, capability, or client requirements. Harvey is built on frontier models that it selects and manages on the firm's behalf.

**Q: How does ibl.ai's pricing compare to Harvey's?**

ibl.ai uses flat-fee source-code licensing rather than a vendor-managed subscription, so costs do not compound with seat count and the firm ends up owning the platform. Harvey is a SaaS subscription tied to its commercial terms. For firms with broad, growing adoption, an owned flat-fee model can run materially lower over time, though Harvey delivers more legal tooling immediately.

**Q: Is ibl.ai production-ready for a law firm?**

Yes. ibl.ai is production-grade, serving 1.6M+ users across 400+ organizations, including learn.nvidia.com, Kaplan, and Syracuse University — which runs with full source-code ownership on its own GCP at roughly 85% lower cost than per-seat SaaS. It is not a legal-domain benchmarking specialist the way Harvey is, but the underlying platform is mature and operated at scale.