# ibl.ai vs EY for AI Consulting and Delivery

> Source: https://ibl.ai/resources/comparisons/ibl-ai-vs-ey-ai-consulting
> Last updated: 2026-08-19


*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.**

## What's the difference between ibl.ai and EY AI Consulting?

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.

## Feature Comparison

### What Arrives on Day One

| 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. |

### What You Own Afterwards

| 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. |

### Commercial Shape

| 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. |

## Detailed Analysis

### Which controls are architectural rather than procedural?

**ibl.ai:** 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.

**EY AI Consulting:** 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.

**Verdict:** 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.

### What does independence buy?

**ibl.ai:** 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 AI Consulting:** EY's independence is the product. For board-level assurance and regulatory engagement, a vendor's assessment of its own controls carries less weight.

**Verdict:** These are complementary rather than competing. The mistake is buying implementation from the party whose value is independence.

### Where does the delivery budget go?

**ibl.ai:** 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.

**EY AI Consulting:** Advisory-led delivery frequently ends with a well-specified requirement to build exactly what an existing platform already does.

**Verdict:** Have the framework written, then implement it on something that already satisfies most of it.

## FAQ

**Q: Is ibl.ai an alternative to AI governance consulting?**

No, and it should not be treated as one. Advisory work provides independent judgment about risk posture. What a platform provides is the enforcement and evidence layer those frameworks specify — the two are complementary.

**Q: Which AI controls are decided by architecture?**

Where data is processed, whether inference can occur without external connectivity, whether retrieval honours each user's real entitlements, and whether every prompt and tool call is logged in a form a reviewer accepts.

**Q: Can a governance framework be satisfied by a hosted AI service?**

Many can, under the right agreements. The ones that cannot are those requiring that data never leave the perimeter or that the organization can operate and inspect the system independently.

**Q: Does ibl.ai provide audit evidence?**

Yes. Every prompt, retrieval, and tool call is logged in systems you own, so evidence production does not depend on a vendor's cooperation or retention policy.

**Q: Can an advisory firm implement ibl.ai?**

Yes. The platform ships with full source under a perpetual licence, so an advisory or integration partner can deliver it inside a broader governance programme.

**Q: How does ibl.ai fit in?**

ibl.ai arrives with the base already built. The platform is in production with users from 400+ organizations and ships with the full source code, so customized engineering starts from something that works rather than from an empty repository — which is what makes it comparatively fast and cost-effective. You own all the code and the data, run it model-agnostic across any LLM, with no per-seat pricing, and can deploy anywhere including fully air-gapped.


## Where does ibl.ai fit alongside ibl.ai and EY AI Consulting?

**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.

- **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.