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. Odokai runs the layer underneath, on a shared instance, in a cloud account we manage, or in your own.
The platform layer is already built.
Model registry, approval chains, policy controls, observability and deployment tooling are included, so your engineers work on business logic.
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
Workflows your team can read.
Each step, branch and approval is visible as it runs, and every run is recorded.
What's included.
The platform layer your engineers would otherwise build and maintain.
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.
One platform instead of several.
Chat, agents, workflows, retrieval and apps in one governed platform, instead of separate tools to integrate.
Swipe or scroll horizontally to compare options.
| 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. |
Deployment options.
Start on the shared platform and move to a dedicated instance later. Workflows move without changes.
- 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
Talk to us.
Tell us what your team is building and we will show you how it runs on Odokai.