Deploy a production-grade AI platform entirely on your own infrastructure — with full source code, zero external dependencies, and complete control.
On-premise AI deployment means running the entire AI platform stack inside your own data center, private cloud, or hybrid environment — not routing data through a vendor's servers.
ibl.ai delivers pre-built Docker images and Kubernetes-ready configurations that your infrastructure team can deploy, configure, and operate independently. No callbacks to external services. No SaaS dependencies. No shared tenancy.
With 1.6M+ users across 400+ organizations — including NVIDIA's global AI training platform — ibl.ai is purpose-built for production environments where security, performance, and sovereignty are non-negotiable.
Most enterprise AI vendors offer a cloud-hosted SaaS product with an "enterprise tier" that still routes your data through their infrastructure. Your sensitive documents, user queries, and operational data leave your environment every time someone interacts with the system. Compliance teams flag it. Security teams block it. Procurement stalls.
When organizations try to self-host alternatives, they inherit fragmented open-source components with no production support, no audit trail, and no clear upgrade path. The result is months of integration work, brittle deployments, and an AI system that can't scale — leaving teams back where they started.
Cloud-hosted AI platforms transmit user inputs, documents, and query context to vendor-controlled servers for inference and processing.
Regulated industries face compliance violations. Classified environments cannot adopt the technology at all. Legal and security reviews block deployment indefinitely.SaaS AI vendors control the model, the infrastructure, the update schedule, and the data pipeline. Customers have no visibility into what runs beneath the UI.
A vendor outage, pricing change, or product discontinuation immediately disrupts operations. Organizations have no fallback and no leverage.Most AI platforms provide minimal logging of agent actions, model decisions, or data access events — making forensic review and regulatory reporting impossible.
Organizations cannot demonstrate compliance to auditors, cannot investigate incidents, and cannot meet requirements like FedRAMP, HIPAA, or SOC 2.Stitching together open-source LLM runtimes, vector databases, orchestration layers, and access controls requires deep ML engineering expertise and ongoing maintenance.
Deployment timelines stretch to 12–18 months. Internal teams burn cycles on infrastructure instead of business value. Security posture degrades as components drift.Many platforms are tightly coupled to a single model provider — OpenAI, Anthropic, or Google — making it impossible to switch models without rebuilding the integration layer.
Organizations are exposed to model deprecations, price increases, and capability gaps with no migration path and no negotiating position.ibl.ai delivers the full platform as versioned Docker images and Helm charts alongside complete source code. Your team receives everything needed to deploy, inspect, and modify the system — no black boxes.
Stand up the platform on your data center hardware, VMware environment, private cloud (OpenStack, vSphere), or air-gapped Kubernetes cluster. Pre-tested configurations reduce deployment time from months to days.
Configure the platform to use your preferred LLM — whether that's a locally hosted Llama or Mistral instance, an on-premise GPU cluster, or a private Azure OpenAI endpoint. The platform is fully model-agnostic.
Use the built-in Model Context Protocol (MCP) layer to connect AI agents to internal databases, document repositories, APIs, and enterprise systems — all within your network perimeter.
Define organizations, roles, and permissions using the multi-tenant architecture. Integrate with your existing identity provider (LDAP, SAML, OIDC) to enforce role-based access across departments and user groups.
Every agent action, model call, and data access event is logged to your infrastructure. Your security team owns the audit trail. Updates are applied on your schedule — the platform runs without any dependency on ibl.ai's servers.
Customers receive the complete codebase — not a compiled binary or a managed service. Your engineering team can audit, modify, extend, and fork the platform. No license restrictions on internal use.
The platform is architected to run with zero external network dependencies. Once deployed, it operates entirely within your environment — no telemetry, no license callbacks, no external API requirements.
Pre-built Helm charts and Docker Compose configurations support deployment on any Kubernetes distribution — including OpenShift, Rancher, and air-gapped K3s clusters. Horizontal scaling is built in.
Connect to Claude, GPT-4, Gemini, Llama 3, Mistral, or any custom fine-tuned model. Swap models without rebuilding workflows. Run multiple models simultaneously for different use cases or security tiers.
Every agent action, tool call, API request, and model response is logged with full context — user identity, timestamp, inputs, outputs, and execution path. Logs are stored in your infrastructure and exportable to your SIEM.
Serve multiple departments, business units, or client organizations from a single deployment with strict data isolation. Role-based access control enforces boundaries at the API, data, and agent level.
Every platform capability is exposed through documented RESTful APIs. Integrate AI agents into existing enterprise workflows, internal portals, and operational systems without UI dependency.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Data Residency | User queries, documents, and context are transmitted to vendor cloud infrastructure for processing. Data residency is a contractual promise, not a technical guarantee. | All data is processed exclusively within your infrastructure. No data leaves your network perimeter at any point — by architecture, not by policy. |
| Vendor Dependency | The platform stops functioning if the vendor has an outage, changes pricing, discontinues the product, or terminates your contract. You have no fallback. | The platform runs independently on your infrastructure indefinitely. ibl.ai's operational status has zero impact on your deployment. You own the code. |
| Source Code Access | You receive a compiled binary, a managed service, or a containerized black box. Security review is limited to what the vendor discloses. Internal modification is prohibited. | You receive the complete, unobfuscated source code. Your security team can audit every line. Your engineers can modify, extend, and fork the platform for internal use. |
| Audit & Compliance | Audit logs are partial, vendor-controlled, and accessible only through vendor tooling. Demonstrating compliance requires vendor cooperation and is limited by their logging architecture. | Every agent action, model call, and data access event is logged to your infrastructure in your format. Your team controls retention, access, and export — no vendor coordination required. |
| Model Flexibility | The platform is tightly coupled to one or two model providers. Switching models requires rebuilding integrations or migrating to a different vendor entirely. | Connect any model — GPT, Claude, Gemini, Llama, Mistral, or custom fine-tuned models — through a unified interface. Swap or run multiple models simultaneously without rebuilding workflows. |
| Deployment Timeline | Self-hosting open-source components requires assembling an LLM runtime, vector database, orchestration layer, auth system, and UI — typically 12–18 months of engineering effort. | Pre-built Docker images and Helm charts reduce deployment to days. The platform arrives tested, versioned, and production-ready with documented configuration for your environment. |
| Air-Gapped Environments | Cloud AI vendors cannot serve air-gapped networks, classified environments, or OT networks by definition. These environments are simply excluded from AI adoption. | The platform is architected for air-gapped operation from the ground up. Deploy on classified networks, factory floors, and disconnected environments with full capability. |
User queries, documents, and context are transmitted to vendor cloud infrastructure for processing. Data residency is a contractual promise, not a technical guarantee.
All data is processed exclusively within your infrastructure. No data leaves your network perimeter at any point — by architecture, not by policy.
The platform stops functioning if the vendor has an outage, changes pricing, discontinues the product, or terminates your contract. You have no fallback.
The platform runs independently on your infrastructure indefinitely. ibl.ai's operational status has zero impact on your deployment. You own the code.
You receive a compiled binary, a managed service, or a containerized black box. Security review is limited to what the vendor discloses. Internal modification is prohibited.
You receive the complete, unobfuscated source code. Your security team can audit every line. Your engineers can modify, extend, and fork the platform for internal use.
Audit logs are partial, vendor-controlled, and accessible only through vendor tooling. Demonstrating compliance requires vendor cooperation and is limited by their logging architecture.
Every agent action, model call, and data access event is logged to your infrastructure in your format. Your team controls retention, access, and export — no vendor coordination required.
The platform is tightly coupled to one or two model providers. Switching models requires rebuilding integrations or migrating to a different vendor entirely.
Connect any model — GPT, Claude, Gemini, Llama, Mistral, or custom fine-tuned models — through a unified interface. Swap or run multiple models simultaneously without rebuilding workflows.
Self-hosting open-source components requires assembling an LLM runtime, vector database, orchestration layer, auth system, and UI — typically 12–18 months of engineering effort.
Pre-built Docker images and Helm charts reduce deployment to days. The platform arrives tested, versioned, and production-ready with documented configuration for your environment.
Cloud AI vendors cannot serve air-gapped networks, classified environments, or OT networks by definition. These environments are simply excluded from AI adoption.
The platform is architected for air-gapped operation from the ground up. Deploy on classified networks, factory floors, and disconnected environments with full capability.
Meets the strictest data sovereignty and classification requirements while delivering production-grade AI capability to analysts and operators.
Eliminates BAA complexity with cloud vendors. Enables AI-assisted clinical workflows without exposing patient data to third-party servers.
Passes security review without architectural exceptions. Audit logs satisfy examiner requests without vendor coordination.
Supports ATO processes with full system documentation and source code review. Operates independently of commercial cloud availability.
Brings AI-assisted monitoring, anomaly detection, and workflow automation to environments that cloud vendors cannot reach.
Preserves attorney-client privilege. Satisfies client data handling requirements. Passes law firm security audits without carve-outs.
Operates in low-connectivity environments. Protects proprietary process data and trade secrets from exposure to external infrastructure.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.