Deployment engineers from the model vendor, aimed at regulated industries — or a platform you own that can run Claude without standardizing on 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.
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Anthropic calls its forward-deployed function the Applied AI Engineer, and it has industrialized it the same way OpenAI has. A joint venture reported above $1.5 billion — with Blackstone, Hellman & Friedman and Goldman Sachs — underwrites customer deployment, explicitly aimed at regulated industries including financial services, healthcare, legal, and government. Compensation for these engineers reportedly runs $350,000 to $550,000, competitive with research engineering.
That is a serious commitment, and the expertise is real. Engineers who work alongside the people training the model will get more out of Claude than anyone else will.
The targeting is also notable: regulated industries are precisely where ownership, residency, and audit questions bind hardest.
Which makes the structural limit worth stating plainly. Claude is not distributed as weights, so it cannot run inside a perimeter that forbids external calls — and a deployment arm inside a model company is not the party that will make your architecture model-agnostic.
by ibl.ai
Owned platform + forward-deployed engineeringby Anthropic
Model vendor's deployment arm| Criteria | ibl.ai | Anthropic Applied AI 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. | Exceptional depth on Claude, a genuine safety and interpretability culture that regulated buyers value, and a services arm deliberately built for financial services, healthcare, legal and government. |
| 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 | Anthropic Applied AI 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 | Anthropic Applied AI Engineers |
|---|---|---|
| How It Is Priced | A flat platform licence plus a bounded integration engagement. | Typically enterprise engagements attached to model consumption, delivered through the joint venture. |
| 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. |
Self-hosting resolves it structurally: models run inside your network, so there is no disclosure to a third party and no dependence on contractual assurance.
Anthropic's enterprise terms and regulated-industry focus are genuinely strong, and for many regulated workloads they are sufficient. What they cannot change is that Claude runs in Anthropic's cloud.
For workloads where a vendor may process the data under agreement, this is a strong option. For those where data cannot leave at all, expertise does not substitute for architecture.
A model-agnostic platform routes long-context reasoning to Claude where it earns the cost, and serves high-volume or sensitive work from open weights on your own GPUs.
Claude leads on several dimensions that matter for regulated work, particularly long-context reasoning and instruction-following. Wanting access to it is entirely rational.
Wanting Claude available and standardizing your architecture on one vendor are different decisions. The first is sensible; the second is the one that becomes expensive to reverse.
A perpetual source licence on your infrastructure means the system survives any change in commercial relationship, pricing, or model availability.
A deployment engagement produces integration that depends on continued API access on terms set by the vendor.
In regulated environments the ability to keep operating through a vendor change is itself a control, not merely a commercial preference.
If the strategy is to standardize on Claude, engineers from Anthropic will extract more from it than any third party.
Claude cannot run without outbound connectivity. Open weights on an owned platform can, which decides this class of workload outright.
Where a regulator or client requires code-level review and independent operability, a perpetual source licence answers it and a services engagement does not.
A model-agnostic platform calls Claude for the tasks that merit it while keeping the rest of the estate portable.
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 lets a regulated organization have Claude without building its architecture around Anthropic. The platform is model-agnostic, so it routes to Claude where that earns its cost and serves everything else from open weights running inside your own network. Forward-deployed engineers deploy a platform already in production with users from 400+ organizations and integrate it with your systems of record over APIs and MCP. Because it is self-hosted, PHI, privileged matters, and classified material never leave your perimeter. You own all the code and the data, 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.