Model routing and registry
Use OpenAI, Anthropic, Gemini, and open-weight models behind a governed catalogue with shared policies and provider flexibility.
Your engineers have better things to build.
Model routing, approvals, audit, and deployment are already built. Your team writes the business logic and nothing else. Odokai runs the layer underneath, on a shared instance, in a cloud account we manage, or in your own.
Ship business workflows sooner, without building AI plumbing first
Your engineers skip quarters of rebuilding a model registry, approval chains, policy controls, observability and deployment tooling. They work on what moves revenue, operations and customer outcomes.
7-11 months
typical internal build to recreate the governed layer
3 deployment modes
shared, managed, or in your own cloud account, from one workflow model
1 foundation
your team keeps control over data, workflows, and policies
Faster time to value for engineering and the business
Odokai provides the platform layer, so production workflows go live sooner and your teams stop waiting on platform rebuilds.
Approval and governance controls
Insert human checkpoints, role-based access, and policy enforcement in workflows where accountability matters.
Audit and observability
Track workflow activity end-to-end with exportable logs, decisions, and operating visibility for risk and compliance teams.
Workflow orchestration
Coordinate multi-step processes with agents, tools, approvals, and schedules in the same runtime model.
Deployment portability
Run the same workflows in Odokai-managed, your cloud, or isolated environments without rewriting the system.
Developer-ready interfaces
Use APIs and integration patterns that let your engineers extend workflows instead of rebuilding infrastructure services.
Lower platform sprawl. Higher delivery velocity.
Engineering teams often inherit a fragmented AI stack: separate tools for chat, agents, workflows, retrieval, and apps. Odokai consolidates that sprawl into one governed platform, reducing integration overhead and speeding up delivery.
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| Capability | Build it internally | With Odokai |
|---|---|---|
| Provider flexibility | Custom adapters and ongoing maintenance | Multi-provider support in one governed model layer |
| Policy control | Separate controls per tool or team | Central model catalogue and access policy |
| Workflow portability | Rework required when provider decisions change | Swap model providers without re-architecting workflows |
| Cost visibility | Multiple fragmented billing streams | Unified spend visibility across teams and workflows |
| SaaS consolidation | Chat here, agents there, workflows somewhere else | One platform. Chat, agents, workflows, knowledge, apps, connectors. |
Deploy on your terms and scale without rewriting
Start in the environment that fits current risk and speed. Move later if governance changes. The workflow model remains the same.
- Shared: fastest route to first production workflows
- Managed: a dedicated instance in a cloud account we run
- Your cloud: a dedicated instance inside your own AWS account
- Consistent runtime: keep one operating model across all deployment choices
Same day
to get started on the shared platform
Days
for a dedicated instance, managed or in your own cloud
One
workflow model across every deployment
Built for engineering control and production accountability.
Governed model access and policy controls
Approval checkpoints and auditable decisions
API and workflow automation support
Your data, workflows, and IP stay yours
Let your engineers build business outcomes
If your team is spending more time on AI plumbing than business workflows, we should talk. Odokai cuts stack sprawl, improves delivery speed, and keeps governance under your control.