ibl.ai Agentic AI Blog

Insights on building and deploying agentic AI systems. Our blog covers AI agent architectures, LLM infrastructure, MCP servers, enterprise deployment strategies, and real-world implementation guides. Whether you are a developer building AI agents, a CTO evaluating agentic platforms, or a technical leader driving AI adoption, you will find practical guidance here.

Topics We Cover

Featured Research and Reports

We analyze key research from leading institutions and labs including Google DeepMind, Anthropic, OpenAI, Meta AI, McKinsey, and the World Economic Forum. Our content includes detailed analysis of reports on AI agents, foundation models, and enterprise AI strategy.

For Technical Leaders

CTOs, engineering leads, and AI architects turn to our blog for guidance on agent orchestration, model evaluation, infrastructure planning, and building production-ready AI systems. We provide frameworks for responsible AI deployment that balance capability with safety and reliability.

Back to Blog

Harvey & CoCounsel Alternative: Air-Gapped Legal AI

Mikel AmigotMay 24, 2026
Premium

Harvey and CoCounsel are powerful legal AI tools — and cloud services. For firms where privileged matter can't leave the building, here is the air-gapped, owned alternative.

The choice for law firms

Harvey and CoCounsel are capable, well-regarded legal AI products. They are also cloud services: your documents are processed on the vendor's infrastructure.

If you're weighing an alternative, it usually comes down to attorney-client privilege and the duty of confidentiality — whether privileged matter should leave the firm's network at all.

This is a factual comparison. Both products do real work; the question is where the data is processed and who owns the system.

Cloud SaaS vs. owned and air-gapped

Harvey / CoCounselibl.ai
HostingVendor cloudYour infrastructure — air-gapped if needed
Privileged dataProcessed by the vendorNever leaves the firm
ModelVendor's chosen modelModel-agnostic, including open-weight
PricingPer seatFlat-rate, unlimited users
CodeClosed SaaSFull source code ownership

Why architecture beats assurance for privilege

ABA Model Rule 1.6 requires reasonable efforts to prevent unauthorized disclosure of client information. "We use a vendor that promised not to look" is a weaker position than "the matter never left our network."

An air-gapped deployment makes the confidentiality question architectural, not contractual. Open models now handle research, review, and drafting at a level that was cloud-only two years ago, so the firm no longer trades capability for control.

What attorneys run

A contract review agent that redlines against your playbook, a legal research agent grounded in your own briefs with verified citations, a client intake agent that screens and books — all inside the firm's boundary.

This is the model behind air-gapped AI for law firms you own, built on the Agentic OS, with full code ownership and client data that never leaves your infrastructure.

Where to start

Pick one workflow with clear value and low risk — internal knowledge search or first-pass contract review — and run it air-gapped against a single practice group. Prove the privilege model on real matters before expanding.

Frequently Asked Questions

What is a Harvey or CoCounsel alternative?

An air-gapped, owned legal AI platform where privileged matter never leaves the firm — versus Harvey and CoCounsel, which are cloud services priced per lawyer.

Can privileged data stay in the firm?

Yes. The runtime, the data, and the agents run on-premise or fully air-gapped inside the firm, with matter- and field-scoped access and audit trails.

How does the cost compare?

Harvey and CoCounsel charge per-lawyer subscriptions (often $200-500/user); an owned platform has no per-attorney seat fee, so cost does not multiply with headcount.

Can you run any model?

Yes, it is model-agnostic — run any model behind the firm's boundary, including self-hosted open-weight models for the most sensitive matters.

Why does owning the AI stack matter?

ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.

  • You own all the code and the data

    Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.

  • Model-agnostic

    Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.

  • No per-seat pricing

    Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.

  • Deploy anywhere

    Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.

See the ibl.ai AI Operating System in Action

Discover how leading universities and organizations are transforming education with the ibl.ai AI Operating System. Explore real-world implementations from Harvard, MIT, Stanford, and users from 400+ institutions worldwide.

View Case Studies
Work with our team

Pilots, deployment, and full ownership

Most enterprise engagements are one-time, not subscriptions. You integrate ibl.ai with your own data, deploy it on your own infrastructure, and the engineering hours scale with the work — so the price tracks the scope, not your headcount.

Start here

Pilot

from $15K

fixed scope · fixed timeline

A time-boxed proof of value on your real data — not a slide deck.

Best for: Teams that want to see ibl.ai working before committing.

  • Deployed on your infrastructure or our cloud
  • 1–2 production agents wired to a slice of your data
  • One integration (LMS / SIS / SSO / data source)
  • Weekly working sessions with our engineers
  • Pilot fee credits toward a full engagement
Scope a pilot
Most common

Integration & Deployment

$25K – $80K

one-time · not a subscription

Full deployment integrated with your data and systems. Engineering hours scale with scope.

Best for: Organizations rolling ibl.ai out across a department, campus, or business unit.

  • Platform deployed in your VPC, on-prem, or air-gapped
  • Integrated with your data + identity (SSO / SAML)
  • Multiple custom agents built to your workflows
  • Engineering hours proportional to scope
  • You own the data · run any LLM you choose
Plan a deployment
Full ownership

Codebase Transfer + Custom AI Engineering

Six figures

perpetual license · you own the stack

We transfer the full source code. You own and self-host the entire platform — outright.

Best for: Government, defense, and enterprises that require perpetual ownership and sovereignty.

  • Complete source-code transfer + perpetual license
  • Dedicated AI engineering team on your roadmap
  • Custom agents, models, and integrations to spec
  • Air-gapped capable · zero vendor lock-in
  • Family-owned, New York–based long-term partner
Talk about ownership
You own the code and data Run any LLM — Claude, GPT, Gemini, Llama Family-owned & operated from New York, NY