How agents, models and data work as one system.

An application receives a request. Agents coordinate the work, use models and enterprise knowledge, and act through permitted tools. Origon keeps the context and execution record available for what happens next.

The complete system can run in a fully private or air-gapped environment, with components and integrations engineered for that boundary.

How requests become tasks and tool calls.

Agents coordinate work across people, tools, and conversations. Your organization defines the goals, resources, permitted actions, and decisions that require approval.

A deployable AI system contains a root agent, worker agents, actions, and a runtime. The root agent receives input, keeps context, plans, and delegates.

Workers perform defined tasks using the tools, MCP functions, and knowledge they are allowed to access. They can run asynchronously and in parallel.

The runtime manages schedules, retries, timeouts, and resources. Work can begin from a conversation, a schedule, or an event and continue in the background.

Bring past decisions and company knowledge into the task.

AI Datastore combines object storage, a knowledge graph, vector search, full-text retrieval, sessions, lineage, and memory in one engine. S3-compatible APIs provide object access; native MCP interfaces expose artifacts, workspaces, and memory operations to agents.

Agents can retain and retrieve past decisions, commitments, and lessons across sessions. Correction, authorized deletion, access controls, and configured retention govern that memory.

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Serve foundation, fine-tuned or your own models.

Origon's inference engine serves foundation, fine-tuned, and customer models. Origon-hosted systems run on Origon GPU infrastructure; private Enterprise deployments can keep models and inference inside the customer's environment.

Model engineering connects behavior to the task through training data, knowledge retrieval, tools, and evaluation. Tests help determine whether to change a model, an application, or the workflow.

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Foundation models

General-purpose models ready for a broad range of enterprise tasks.

Fine-tuned

Models adapted to your domain, data, and quality requirements.

Customer-owned

Your models served within the deployment boundary you choose.

Inspect what you designed and what actually ran.

Origon's studio compiles visual agent designs into an executable system. Saved versions and rollback support controlled configuration changes.

Each session exposes a transcript, execution log, and summary. Engineers can inspect planning, tool calls, knowledge access, errors, memory activity, and costs. OpenTelemetry-compatible traces connect the record to supported observability tools.

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Run the complete stack without external model APIs.

The platform can run in a customer VPC, on-premises, hybrid, or air-gapped. Fully private configurations keep models, inference, agents, data, memory, and observability inside the deployment boundary without third-party API dependencies. Integrations are selected to fit that boundary.

Origon's inference, storage, media, and speech engines are built in Rust and communicate over binary RPC on QUIC. Origon-hosted deployments use Origon datacenters, GPU clusters and global network.

A complete system or services for your own build.

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Origon Enterprise

Put AI to work across your business with the complete platform, implementation and ongoing engineering. Your team sets the goals, permissions and approvals.

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Origon Cloud

Build your own AI with GPU compute, AI Datastore, Speech and Voice Network services, together or on their own.

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