Enterprise AI on infrastructure you govern
Deploy AI at enterprise scale - with full visibility into how your data is used and complete control over its access.
AI breaks at governance - not at models
Running AI in production requires control over models, data, and usage - and that control must be built into the infrastructure, not added as an afterthought.
Data exposure risk
Models that access enterprise data without governed boundaries create compliance and security exposure - often without visibility into what isbeing shared, when, or with which model.
No visibility into AI usage
Without a centralized AI control plane, there is no consistent view of which models are running, what data they are accessing, or what they are costing across the organization.
Infrastructure lock-in
Building AI on proprietary cloud AI services recreates the same vendor dependency that organizations are working to reduce in other parts of their tech stack.
The infrastructure gap for AI
82%
of organizations say their infrastructure cannot support on-premises AI efficiently
80%
identify data sovereignty as a significant challenge for AI adoption
88%
of organiations already use AI in at least one business function
The infrastructure gap in enterprise AI
Data sovereignty ranks as a significant challenge for AI adoption among 80% of organisations.
An oversight layer for enterprise AI
A structured control plane between your applications and your models - managing access, usage, cost, and AI visibility across the organisation, enforced at the infrastructure level.
Route requests across multiple models with access policies, cost controls, and provider abstraction applied at the gateway level.
Connect models to enterprise data using RAG, embeddings, and semantic search - over data that remains within your environment.
Run AI agents that interact with internal systems, APIs, and data sources in a controlled, auditable execution environment.
Deploy and operate open-source models within your own infrastructure for workloads that require data isolation or latency control.
Enforce policies, ensure prompt safety, and maintain full auditability of AI usage across every model and every team.
Determine AI across your organization
Control AI across teams and use cases
Policies, access rules, and usage visibility are applied at the platform level - not left to individual teams to configure independently.
Run AI on sensitive enterprise data
Keep proprietary and regulated data within your defined boundaries while enabling advanced AI capabilities on top of it.
Operate across multiple models
Route workloads across internal and external models according to cost, capability, and governance requirements - with no dependency on a single provider.
Move AI from pilots to production
Operate AI as a managed, governed infrastructure capability - with the reliability and auditability that production systems require.
Cloudboostr - built for every AI deployment pattern
LLM-powered applications
Enterprise chatbots, knowledge assistants, and customer-facing AI products.
AI agents & workflows
Agentic systems that interact with internal APIs, databases, and enterprise systems.
RAG systems
Retrieval-augmented generation over your enterprise knowledge base
Data analytics pipelines
AI-powered data processing and analytics on sensitive enterprise data.
Mission-critical decision systems
Regulated AI systems requiring full auditability and governance controls.
Multi-model orchestration
Environments routing across multiple AI providers with cost and policy controls.
Ready to put AI into production - on your terms?
Tell us where you are with your AI enablement. We'll tell you what's missing.