Production-grade agentic AI.
Without giving up control.
An operating system for enterprise AI — every agent inherits the same contract: traceable, reproducible, cost-bounded. Frontier intelligence, with the guarantees of a system you own.
What your board started asking — that wasn't on the agenda last year.
Every enterprise board in 2026 is asking the same three questions about AI. The rest of this page answers them — in that order.
The AI reasons once. Then that reasoning becomes the agent.
In the Mind Designer, AI works out how to solve your problem — step by step, the full chain of thought. We capture that reasoning and compile it into the MindSchema: a fixed standard operating procedure the agent follows exactly. The thinking happens once, at design time. After that, the Mind runs the locked plan — the same way, every time.
One operating system. Countless agents — built for what your enterprise actually does.
You don't wire a hundred building blocks. The Designer composes a small set of operators — and writes new ones in Python when the job needs them. Its orchestrator generates the skills and tools an agentic system requires, automatically. You build the differentiation; the platform builds the plumbing.
Built to run on its own. Designed to work with what you already have.
Your mission-critical Minds are designed, governed and run on ai/Studio — on your edge cluster, inside your perimeter. And it works with the AI investment you've already made: deploy a Mind into your factory as a callable model, or point ai/Studio at any model you like. The platform is the foundation; the hyperscalers are optional integrations.
Watch one Mind run. Every number is real, and metered.
An agent retrieves across your sources, reasons, and produces an artifact — while the Control Tower meters cost, traces every step, and bounds the budget.
✓ budget enforced at runtime
✓ same input → same plan
Your AI is only as good as what it knows.
We turn your documents, databases and systems into a knowledge graph — then retrieve from it deterministically. No LLM call sits in the retrieval path: vector search, fuzzy matching and graph traversal assemble the grounded context, and only then does the model answer. Faster, cheaper, reproducible.
Structured
SQL · tables
Unstructured
PDFs · docs · email
Semi-structured
APIs · systems
Governance isn't a setting. It's how every Mind is built.
Trace, reproducibility and the cost ceiling are properties of the compiled MindSchema — not settings anyone can switch off. Your board's three questions about production AI each have a structural answer — and each is patent-backed.
Deterministic where it can. The LLM only where it must.
Deterministic code does the work wherever it can; the model is invoked only where judgment is genuinely required. This isn't a cost trick — it's a governance property. Deterministic steps are reproducible by definition; the bill dropping is the side effect.
Runs inside your perimeter. On your cloud. Under your keys.
A stateless tier in your cloud or datacentre; stateful services in your own tenancy. Single-tenant, deployed where your data already lives. Data never leaves. Keys never leave. The audit trail stays in your tenancy.
Real names. Real recognition. Patent-backing.
Plugs into the systems you already run — both directions.
Minds consume your systems, and your systems — and the hyperscalers — consume Minds.
Design-time intelligence. Runtime guarantees.
See a Mind run on your data. Thirty minutes, with an engineer who can answer the hard questions.