# ibl.ai vs Deloitte for Enterprise AI Delivery

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


*Industry depth and transformation muscle, or a platform already in production with the engineers who built it*

**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 Deloitte AI Services?

Deloitte delivers AI as part of broader transformation: industry process depth, change management, operating-model design, and the programme governance large organizations need when the technology is the smaller half of the problem.

That framing is often correct. Most failed AI programmes fail for organizational reasons rather than technical ones, and a firm that can redesign the process around the technology is solving the harder problem.

Where it becomes expensive is the platform underneath. A transformation programme that also constructs an AI platform is funding infrastructure — retrieval, evaluation, guardrails, access control, audit, model routing — that is identical across every client the firm serves.

Separating those two purchases usually improves both: buy the transformation capability, and license the platform rather than commissioning it.

## Feature Comparison

### What Arrives on Day One

| Criteria | ibl.ai | Deloitte AI Services |
|----------|--------------------|--------------------|
| 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. | Industry process depth, change management, and the programme governance to run AI as one strand of a large transformation — the organizational half that most AI failures are actually about. |
| 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 | Deloitte AI Services |
|----------|--------------------|--------------------|
| 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 | Deloitte AI Services |
|----------|--------------------|--------------------|
| How It Is Priced | A flat platform licence plus a bounded integration engagement. | Typically transformation programmes priced on people, often multi-year. |
| 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

### Is the hard part organizational or technical?

**ibl.ai:** Often organizational — which is exactly why the technical side should go to a team that has already solved it. Our engineers deliver the platform and the agents without consuming the programme's change-management capacity.

**Deloitte AI Services:** Process redesign and adoption matter, and large firms do that work well. It also proceeds independently of who builds the platform.

**Verdict:** If the hard part is organizational, do not spend the programme's attention constructing a technical base. Buy one that runs and give the organizational work the focus.

### What does the programme actually fund?

**ibl.ai:** With a licensed platform, programme budget goes to integration, workflow design, adoption, and the agents specific to your operation.

**Deloitte AI Services:** In an integrated build, a large share funds undifferentiated infrastructure that the same firm has built for other clients and will build again for the next.

**Verdict:** Ask what fraction of the technical scope is unique to your organization. It is usually a small minority of the effort.

### Who runs it in year three?

**ibl.ai:** A platform with source rights, upstream maintenance, and documentation is operable by your own team after the programme closes.

**Deloitte AI Services:** Bespoke systems built inside transformation programmes frequently outlive the team that built them and become expensive to change.

**Verdict:** Plan for the operating model after go-live, not just the delivery. That is where the ownership question is settled in practice.

## FAQ

**Q: Is ibl.ai a replacement for a transformation consultancy?**

For the AI programme, yes — we deliver the platform, the integration, and the agents end to end. Where an organization is also redesigning processes and operating models, that work runs in parallel and does not change who builds or operates the AI platform.

**Q: Why not have the consultancy build the platform too?**

Because the technical base is largely identical across clients. Retrieval, guardrails, access control and audit are not differentiating, and funding them inside a transformation programme is the most avoidable line in the budget.

**Q: Can Deloitte or a similar firm deploy ibl.ai?**

Yes. The platform ships with full source under a perpetual licence, so a consultancy can prime the programme and implement it — a common and effective arrangement.

**Q: Who operates the system after go-live?**

With an owned platform, your team can, because it is documented, supported, and maintained upstream with the source in your possession. That is frequently the weakest point of bespoke builds.

**Q: How do we compare the two commercially?**

Normalise to total cost to a working outcome over three years, including maintenance and what you own at the end. A services quote and a platform licence are not directly comparable line items.

**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 Deloitte AI Services?

**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 supplies the technical base so a transformation programme can spend its budget on the organizational work that actually determines success.

The platform is in production with users from 400+ organizations and ships with full source under a perpetual licence, deployed on your infrastructure. Forward-deployed engineers handle integration and build the agents specific to your operation, while upstream releases keep model and protocol support current — so your own team can operate the system in year three. 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.

- **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.