Engineers from the model vendor, deploying the model vendor's models — or engineers who hand you the platform and let you choose the model
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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OpenAI has industrialized the forward-deployed engineer. It capitalized a deployment company at more than $4 billion — led by TPG with Advent, Bain Capital and Brookfield as co-lead founding partners — and acquired Edinburgh-based Tomoro for roughly 150 deployment engineers.
The model is borrowed from Palantir, and it works. Engineers who know the frontier model intimately, embedded in your environment, will get more out of that model than a generalist will. If your objective is the best possible GPT deployment, this is the most direct route to it.
The structural question is what the arrangement optimizes for. A deployment arm inside a model company exists to make the model successful in production, which is a legitimate business and not a criticism.
It does mean two things follow by construction: the engagement will not make you model-agnostic, and it will not leave you owning the platform.
by ibl.ai
Owned platform + forward-deployed engineeringby OpenAI
Model vendor's deployment arm| Criteria | ibl.ai | OpenAI Forward-Deployed Engineers |
|---|---|---|
| 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. | Unmatched depth on their own frontier models — the people deploying them work alongside the people who trained them, with early access and direct escalation paths no third party can match. |
| 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 | OpenAI Forward-Deployed Engineers |
|---|---|---|
| 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. | Oriented around one vendor's models, which is what the deployment arm exists to support. |
| 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 — open-weight models on a self-hosted platform run with zero external calls. | No. The models are API-only and the weights are not distributed, so an isolated network cannot use them. |
| Criteria | ibl.ai | OpenAI Forward-Deployed Engineers |
|---|---|---|
| How It Is Priced | A flat platform licence plus a bounded integration engagement. | Typically enterprise engagements attached to model consumption. |
| 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. |
A model-agnostic platform is indifferent to which model wins. Routing sends each task to whatever fits on cost, latency, and capability — including OpenAI's models, which remain excellent for many tasks.
A deployment arm inside a model company is measured on that model's success in production. That produces genuine expertise and a genuine orientation toward one model layer.
Both facts are true at once. The question is whether you want a partner whose incentive is your architecture's flexibility or one vendor's adoption.
ibl.ai runs on your infrastructure, on-premise or fully air-gapped with zero external calls, because the platform and the open-weight models it can serve are both yours to host.
OpenAI's frontier models are not distributed as weights, so they cannot be brought inside a perimeter that forbids external calls, however skilled the engineers are.
For classified, air-gapped, or strictly residency-bound workloads this decides the question before expertise is considered.
The full source under a perpetual licence, running on your own infrastructure — an asset that keeps working regardless of any commercial relationship.
A well-integrated deployment that depends on continued access to the vendor's API, pricing, and model availability.
Ask what happens to the system if the commercial relationship changes. That answer is the real measure of who owns the outcome.
If the strategy is deliberately to standardize on GPT models, engineers from the source will extract more from them than anyone else.
Frontier models cannot be hosted inside a network with no external connectivity; open weights on an owned platform can.
A model-agnostic platform keeps the model layer replaceable, which is the one decision most likely to be revisited within three years.
Routing bulk work to open weights on owned GPUs and reserving frontier calls for the hard cases is materially cheaper than sending everything to one API.
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 provides the same embedded-engineering model with a different centre of gravity: the platform is yours, and the model layer stays replaceable. Forward-deployed engineers deploy a platform already run by users from 400+ organizations, integrate it with your systems over APIs and MCP, and build the agents specific to your workflows. It routes across any model — including OpenAI's — and serves open weights inside your own network when data cannot leave it. You own all the code and the data, run it model-agnostic, with no per-seat pricing, and can deploy anywhere including fully air-gapped.
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.