Token economics break at scale
Long-running agents make per-token budgets hard to forecast.
Autonomous AI infrastructure
Enterprise AI, under your control.
Oxy deploys models, compute, context, and policy across cloud, datacenter, and on-prem resources - with private security and predictable cost.
10x
lower inference cost for agentic workloads
18
cloud providers plus private infrastructure
30+
modalities from code to media to time series
0
rate-limit surprises on dedicated capacity
The enterprise AI bottleneck
AI stalls when cost, data, and security leave your control.
Tools spread fast. Control does not. Oxy brings usage economics, data boundaries, and security policy back into one operating layer.
Core knowledge leaves the organization
Keep code, docs, customer data, and procedures inside systems you control.
Security surfaces multiply
Agents, models, tools, clouds, and data all need one policy layer.
Point tools do not create an AI-native core
Copilots help workflows. Enterprises need a shared AI core.
How Oxy works
One control plane for models, compute, context, and policy.
Choose the workload. Oxy selects capacity, deploys the model stack, connects context, and enforces policy.
01
Match workload to model and capacity
Use frontier, open-source, auxiliary, or domain models based on quality, latency, privacy, and budget.
02
Provision across cloud, datacenter, and on-prem
Span public providers and private resources from one Oxy account.
03
Operate through GUI, CLI, or built-in agent
Deploy, monitor, and manage AI services without hand-building inference pipelines.
Platform outcomes
Everything AI needs before it becomes core infrastructure.
Move from experiments to secure, repeatable, organization-wide AI.
Predictable usage economics
Run long, high-context agent workflows on capacity-based infrastructure.
Global capacity without model lock-in
Route workloads across providers and model families without single-vendor dependency.
One agentic layer across many modes
Support code, text, OCR, computer use, media, 3D, time series, embeddings, and guardrails.
Use the right model for the job
Keep frontier models for rare needs. Move common work to private open-source deployments.
Natively secure foundations
A single security surface for users, agents, models, and clouds.
Oxy applies identity, private networking, encryption, secrets, audit logs, prompt-injection protection, and outbound data controls across the full AI path.
Private by architecture
Customer machines, data, logs, and code stay under customer control. Oxy manages access and policy without becoming another leak point.
Unified IAM
Virtual private cloud
Secret management
Encryption
Visibility and auditability
Resource mapping
Prompt-injection protection
Sensitive outbound controls
Where Oxy fits
For teams that cannot outsource their core intelligence.
Oxy fits organizations that need agentic AI without runaway cost, data exposure, or fragmented control.
Protect unit economics
Serve AI work without routing every interaction through expensive external APIs.
Keep privacy and policy central
Deploy AI where data control and auditability are non-negotiable.
Turn internal expertise into usable agents
Connect proprietary context to secure agents for expert teams.
Scale agentic products with cost discipline
Run AI products on private, optimized infrastructure with room for best-in-class integrations.
Pilot path
Start with one high-value workload. Expand when the economics prove out.
Start with a real team, not a sandbox. Map the workload, deploy the tenant, and measure cost, quality, adoption, and security.
Workload and environment mapping
Define users, quality targets, data boundaries, latency, security, and deployment constraints.
Private tenant deployment
Provision capacity, connect controls, deploy the first stack, and onboard the team.
Scale to common-use adoption
Move common workloads onto Oxy while keeping frontier providers for rare cases.