Unified governance for all your AI traffic
A single platform for AI governance, self-service model access, and EU AI Act compliance.
AI gateway & model access
All model traffic - private and public - flows through an AI gateway, a single, governed access point.
The gateway enforces access policies, rate climits, and token budgets per team and application.
It tracks usage and cost across every model interaction and provides a unified API regardless of the underlying provider, with a full audit trail on every call.
Private model serving
Self-hosted LLMs and predictive models run on an optimized inference layer within your own environment.
The platform handles model versioning, staged deployments, autoscaling, and lifecycle management - somodels are operated with the same rigor as any production workload.
Orchestration and AI governance
A central catalog registers all available models, agents,and tools - giving the organization a single source of truth for its AI capabilities.
Agentic AI runs in a managed execution environment with controlled access to enterprise systems via MCP-based tool integration.
Access management and EU AI Act compliance controls are built into this layer.
Observability and guardrails
Everymodel interaction and agent execution is traced end-to-end - LLM calls, tool invocations, inter-agent communication.
Guardrails and policy controls apply consistently across all access paths, scoped per model, team, or use case.
The platform provides the visibility needed to monitor behavior, detect anomalies, and produce audit-ready evidence for regulators.
A shared control plane for every AI stakeholder
Aiboostr serves every stakeholder in enterprise AI - from business users and developers to platform administrators and compliance teams - through a single, unified system.
Aiboostr for AI users
Discover available AI models, assistants, and agents through a self-service catalog. Request access and interact with models via a built-in conversational interface - without involving IT for every new use case.
Aiboostr for AI developers
Access models and agents via API using issued keys, with a unified endpoint across all private and public models. Build integrations, connect enterprise systems via MCP-based tool integration, and deploy agentic AI within a governed runtime - without building access controls or agent governance from scratch.
Aiboostr for AI administrators
Manage the model and agent catalog, configure access policies, set rate limits and token budgets, and monitor usage and costs across teams and applications - with full visibility into every model deployed and every team that uses it.
Aiboostr for compliance officers
Maintain a complete AI inventory of every AI system in use across the organization. Assign risk classifications, record intended use and deployment context, and track which teams and users have access to each system - the foundation of AI risk management and the audit-ready record for EU AI Act.
Deploy anywhere. Own everything.
Any Kubernetes environment
Deploys on any CNCF-conformant distribution, on-premises, on EU cloud providers, or any major public cloud.
No migration required
Aiboostr runs on your existing Kubernetes infrastructure - no changes needed to get started.
Cloudboostr-ready
Running on Cloudboostr? Aiboostr deploys as a native extension of the platform.
Talk to our team about enterprise AI governance
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FAQ
IoT Data Monetization for Industrial OEMs
The Aiboostr AI gateway routes all model traffic — private and public — through a single, governed access point. It enforces access policies, rate limits, and token budgets per team and application, provides a unified API regardless of provider, and records a full audit trail on every call.
MCP-based tool integration connects AI agents to enterprise systems and APIs through a governed interface using the Model Context Protocol. In Aiboostr, agentic AI runs in a managed execution environment with controlled tool access — so every action an agent takes stays visible, permissioned, and enforceable.
AIBoostr traces every model interaction and agent execution end-to-end — LLM calls, tool invocations, and inter-agent communication are recorded and queryable. This full-execution observability lets teams monitor behavior, detect anomalies, and produce audit-ready evidence for regulators.
Aiboostr serves every stakeholder in enterprise AI through one system: AI users get a self-service catalog and conversational interface, developers get API access and a governed agent runtime, administrators manage policies and budgets, and compliance officers maintain the AI inventory and audit records.
Aiboostr gives compliance officers a complete AI inventory of every system in use, with risk classifications, intended use, and access records. Combined with policy enforcement, log retention, and continuous monitoring, it produces the audit-ready evidence the EU AI Act requires — the foundation of AI risk management.
Yes. Aiboostr runs self-hosted, open-source LLMs and predictive models on an optimized inference layer inside your own environment. The platform handles model versioning, staged deployments, autoscaling, and lifecycle management — so private models are operated with the same rigor as any production workload, with no data leaving your perimeter.
Aiboostr tracks token consumption, API costs, and usage patterns across teams, projects, and models, with attribution for budget control. The AI gateway enforces token budgets and rate limits per team and application, so costs stay visible and capped before they escalate.
Aiboostr applies configurable guardrails and policy controls consistently across every model and access path — global, or scoped per team or use case. Input and output filtering and safety controls run on all traffic through the gateway, so policy is enforced uniformly rather than model by model.
Yes. Aiboostr deploys on any CNCF-conformant Kubernetes distribution — on-premises, on EU cloud providers, or any major public cloud — with no migration required. It runs on your existing infrastructure, keeping AI, data, and governance inside your own perimeter and data-residency boundary.
Yes. If you already run Cloudboostr, Aiboostr deploys as a native extension of the platform. On any other setup it runs on your existing Kubernetes infrastructure with no migration required, so getting started does not disrupt your current environment.