Advisory depth on governance and regulatory risk, or a platform whose architecture answers the control questions before the assessment starts
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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EY's AI practice is built around adopting AI responsibly while managing operational and regulatory risk — governance frameworks, control design, model risk, and the assurance work that regulated boards require.
That is real expertise and the demand is real with it: more than 80% of organizations now report prioritising responsible-AI and data-privacy frameworks. For a board that needs an independent view of its AI risk posture, an advisory firm is the right instrument, and a platform vendor is not a substitute.
The limitation is what advice can do on its own. A governance framework describes controls; something still has to enforce them.
And many of the hardest control questions — where data is processed, who can retrieve which record, what is logged, whether the system can operate without external connectivity — are settled by architecture, before any framework is written.
by ibl.ai
Owned platform + forward-deployed engineeringby EY
Professional services firm| Criteria | ibl.ai | EY AI Consulting |
|---|---|---|
| A Working Platform | In production with 1.6M+ users from 400+ organizations, deployed on your infrastructure. | People and method. The platform is built during the engagement. |
| Domain and Industry Depth | Production deployments across higher education, K-12, government, legal, financial services and healthcare, with sector agents already built and running. | Independent governance, risk and regulatory expertise, model-risk assurance, and the standing with boards and regulators that an advisory relationship carries. |
| Time to First Production Workload | Weeks — there is no construction phase, because the platform already runs. | Quarters, most of them spent building infrastructure common to every client. |
| Capacity to Deliver Your Programme | A dedicated forward-deployed team plus a finished platform delivers the programme end to end — no bench to wait on and no second firm required. | Very large global staffing, though your programme is resourced from it and priced accordingly. |
| Criteria | ibl.ai | EY AI Consulting |
|---|---|---|
| Source Code Ownership | Full source under a perpetual licence, running on your infrastructure. | A contract question rather than a product property, and frequently under-negotiated. |
| Model Freedom | Model-agnostic by construction — any LLM, switchable without rewriting the platform. | Whatever was built in, and whatever the maintaining team will keep supporting. |
| Independent Operability | Documented, supported, and maintained upstream so your own team can run it. | Depends on knowledge transfer and on continued access to whoever built it. |
| Can It Run Air-Gapped | Yes — the same deployment runs on-premise or fully air-gapped with no outbound connectivity. | Achievable as bespoke scope, but it is significant additional engineering rather than a property of the offering. |
| Criteria | ibl.ai | EY AI Consulting |
|---|---|---|
| How It Is Priced | A flat platform licence plus a bounded integration engagement. | Typically advisory and delivery engagements priced on people. |
| Is There a Ceiling | Yes — the licence plus the compute you run. Extending to more users does not multiply it. | Bounded by the contract if fixed-price, otherwise by the estimate's accuracy. |
| Cost of Undifferentiated Infrastructure | Zero — retrieval, guardrails, access control and audit already exist and are amortised across every customer. | Funded by you, and rebuilt for the next client afterwards. |
| Ongoing Maintenance | Upstream releases carry model support, protocol updates and security fixes. | A separate contract, or an internal team, for a system built only for you. |
Data residency, permissions-aware retrieval, audit completeness, and air-gapped operation are properties of where and how the platform runs. A self-hosted deployment answers them by construction.
A governance framework specifies what must be true and how to evidence it — which is necessary, and is not the same as making it true.
Buy advice for the questions that require judgment. Buy architecture for the ones that require enforcement. Confusing the two is how organizations end up with a well-documented framework and no way to satisfy it.
We are not independent about our own platform, and should not be treated as if we were. That is a genuine argument for an advisory relationship alongside.
EY's independence is the product. For board-level assurance and regulatory engagement, a vendor's assessment of its own controls carries less weight.
These are complementary rather than competing. The mistake is buying implementation from the party whose value is independence.
Starting from a platform that already enforces guardrails, RBAC, and audit means the delivery spend goes to configuration against your policy rather than to building the enforcement layer.
Advisory-led delivery frequently ends with a well-specified requirement to build exactly what an existing platform already does.
Have the framework written, then implement it on something that already satisfies most of it.
An independent assurance opinion should come from someone other than the platform vendor, so keep your auditor. That is a separate purchase from who builds and operates the platform, which we deliver end to end.
Residency, permissions-aware retrieval, audit completeness and air-gap are architectural properties, not procedural ones.
A platform already enforcing these controls shortens the path from framework to evidence considerably.
Bring the framework you have. The platform enforces and evidences the controls it specifies out of the box, so reaching production does not require a second advisory engagement first.
Timeline: Four to ten weeks depending on how much has already been built
Timeline: Days to weeks to contract
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.
ibl.ai is the enforcement layer a governance framework assumes. Guardrails, permissions-aware retrieval, sandboxed agent execution, RBAC, and complete audit logging are built into the platform rather than specified as future work. Because it is self-hosted, the hardest control questions resolve architecturally: data stays in your perimeter, the system runs air-gapped where there is no outbound connectivity, and audit evidence lives in systems you already own. You own all the code and the data — which is what allows code-level review — run it model-agnostic across any LLM, with no per-seat pricing.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
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 how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.