◈
Governed AI Access
One governed way for applications and teams to reach approved AI capabilities.
- Support for multiple models and providers
- Access aligned to identity and entitlements
- Usage visibility and audit
Enterprise AI Platform
Enterprise AI Platform.
Built once. Reused everywhere.
A reusable platform layer that lets every team build and operate AI products on the same governed foundation — AI access, enterprise knowledge, policy, evaluation, observability and cost control provided as platform capabilities rather than rebuilt for each project.
The problem
Most enterprises don't have an AI problem. They have an AI duplication problem.
Three teams integrate three different model providers, each with its own credentials, prompts, logging and failure handling. None of it is comparable, none of it is governed centrally, and the fourth team starts again from zero. An enterprise AI platform ends that pattern by making the hard parts — access, policy, evaluation, telemetry — capabilities that every product inherits.
Intellivetrix builds this as a product, not as a bespoke infrastructure project. That means a supported path a team can adopt quickly, defaults that satisfy security and compliance without a review board, and deliberate flexibility for teams whose requirements genuinely differ. The platform earns adoption by being the fastest way to ship, not by being mandated.
The result is leverage. The second AI product starts from a foundation that already exists. Governance is implemented once and inherited everywhere. And when a model, a provider or a regulation changes, you change it in one place instead of auditing every repository in the organisation.
Platform capabilities
What the platform provides.
⌘
Enterprise Knowledge
Your organisation's own information made usable by AI, without loosening who can see what.
- Grounded, source-cited answers
- Existing permissions respected
- Content kept current as sources change
⬡
Policy & Governance
Rules about what may be sent where, enforced by the platform rather than by review meetings.
- Data classification and residency rules
- Per-tenant and per-use-case entitlements
- Enforcement applied to every request
✦
Evaluation & Quality
Whether a change is safe to release becomes a measurement instead of an opinion.
- Agreed quality baselines
- Automated regression checks
- Release gates on model and prompt change
◉
AI Observability
Behaviour, quality, latency and spend in one place, attributable to product, team and tenant.
- End-to-end request visibility
- Quality and cost telemetry
- Drift and anomaly alerting
◎
Deployment Flexibility
The same capabilities whether the platform runs in your environment or ours.
- Self-hosted in your own cloud
- Managed SaaS operated by Intellivetrix
- Hybrid for mixed requirements
What changes
The outcomes the platform is meant to produce.
Time to second product
The first product establishes the foundation. Every one after it starts with access, governance and telemetry already available.
Governance without gatekeeping
Policy is enforced by the platform at request time, so teams stop queueing for manual security review before every release.
Vendor portability
Provider independence plus evaluation baselines make swapping or adding a foundation model a configuration change with a measurable quality check, not a rewrite.
Cost you can attribute
Spend is traced to product, team, tenant and workflow — the precondition for controlling it. See AI Cost Optimization for the controls that build on this.
Common questions
Frequently asked.
What is an enterprise AI platform?
A shared foundation that provides governed model access, enterprise knowledge, policy enforcement, evaluation and observability as platform capabilities, so individual product teams inherit governance and telemetry instead of rebuilding them. It is the difference between five teams each integrating a model provider and five teams consuming one governed platform.
Do we need a platform before our first AI product?
No. Most organisations start with one product on the platform and expand from there. The platform is what the second and third product build on, but it does not have to be fully adopted before the first one ships.
Can we deploy the platform in our own environment?
Yes. The platform runs self-hosted in your own cloud or private infrastructure, as a managed SaaS service operated by Intellivetrix, or as a hybrid of the two. The capabilities are the same in each model; what changes is who operates the infrastructure.
Which AI models and clouds are supported?
The platform is vendor-neutral by design. It supports managed foundation models and self-hosted open-weight models, and runs on AWS, Azure, Google Cloud, or private and on-premises infrastructure, so a change of model or provider does not become a rewrite.
Who owns the data?
Customers do. The platform provides the AI capabilities while your data, identity and compliance boundary remain yours — including in the managed SaaS model.
How does this relate to AI cost optimization?
The platform is what makes cost control possible. Efficiency measures and budget policy have to live at a shared layer to work at all, and per-product cost attribution depends on platform-level telemetry. Cost optimization is a set of controls built on the platform rather than a separate system.
Can we see the platform architecture in detail?
Detailed technical architecture and security documentation are available during enterprise technical evaluation.
Related products
The platform does not travel alone.
✦
Intelligent Automation
Orchestration of AI-driven workflows that carry real work to completion, safely.
⌘
Enterprise Knowledge
Search and grounded, source-cited answers across your organisation's own information.
⬡
Platform Foundations
The cloud, delivery and security foundation the platform and your own workloads share.
◎
AI Cost Optimization
Improve AI efficiency and keep enterprise AI spend predictable as adoption grows.
Next step
Give every AI product the same foundation.
Talk to us about the Enterprise AI Platform — whether you are consolidating existing pilots or establishing the foundation before the first one ships.