Orchestration that runs inside your perimeter

Durable, replayable, auditable workflows on infrastructure you control — your cloud account, your own hardware, or a rack that never touches the internet.

Your data does not leave, and inference runs on your own model keys.

Built for teams under a regulator: lenders, insurers, healthcare operators, and pharmaceutical or controlled-goods distributors — anywhere data residency, audit evidence, or chain of custody is a requirement, not a preference.

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**From process to production**Steps, waits, and human decisions laid out as one durable, replayable run.

Isolation by architecture, not by policy

Your Runtara instance runs on dedicated compute with its own databases and its own runtime. Isolation is a property of how the system is deployed, not a permission someone can misconfigure.

No shared kernels, no shared tables, no noisy neighbours competing for your throughput.
One database you can point at, back up, and hand to an auditor.
Tenant boundaries cannot drift, because there is no shared boundary to enforce.

Dedicated Runtara runtime enclosed within its own compute and data boundary — **A boundary you can draw**Runtime, data, and compute enclosed inside a single dedicated instance.

Deploy anywhere

Runtara runs where your data is allowed to live. The same engine and the same workflow definitions, whether that means a managed instance, your own cloud account, or a rack that never touches the internet.

Managed cloud, your private cloud, or bare metal you already own.
Ready for air-gapped environments — bring your own model endpoint.
Changing the deployment target does not mean rewriting the workflows.

Runtara engine connected to cloud, private infrastructure, bare metal, and air-gapped deployments — **Same engine, your ground**One orchestration engine across cloud, private infrastructure, bare metal, and air-gapped sites.

Your models, your bill

Runtara calls language models through your own credentials against your own endpoint. Prompts and documents travel from your infrastructure to your model provider under agreements your legal team has already signed. There is no Runtara-operated inference layer in the path.

Bring your own endpoint — hosted provider, private deployment, or a model running on hardware you own.
Inference is billed to your account, at your negotiated rates.
Changing model provider is a configuration change, not a rewrite.

Prompts and documents moving from your own infrastructure straight to your own model endpoint, with no Runtara-operated inference in the path — **No vendor between your data and the model**Prompts and documents moving from your infrastructure straight to your own model endpoint.

Durable by design

Workflows checkpoint as they run. A restart, a deploy, or a crash does not lose the work already done — the run resumes exactly where it left off, with the state it had.

Every step commits its state before the next one starts.
Waiting a week on a human decision or an external event costs nothing.
Retries pick up at the failed step instead of starting the process again.

Checkpointed workflow pausing for an external event and resuming from its saved state — **Time is not a failure mode**A checkpointed workflow pausing for an external event and resuming from its saved state.

Debug every step

Every run keeps the full record of what happened: what each step received, what it returned, and how long it took. When something breaks, the answer is on the canvas rather than in a log search.

Inspect any step's inputs and outputs exactly as the runtime saw them.
Replay a past run on the canvas and trace a failure to the reference that broke.
Pause a live run, correct the data, and resume it from any step.

Workflow execution trace with a failed step magnified between inspected inputs and outputs — **Failures with an address**A failed step magnified between the inputs it received and the output it produced.

Reports that act

Reports are not read-only dashboards. They query your operational data, render it as metrics, charts, and tables, and let the people reading them do something about what they see.

Edit records inline, with lookup fields resolved against your object model.
Trigger a workflow straight from a UI interaction — approve, retry, escalate.
Publish once, and let operators run the whole process from the report.

Interactive report triggering a workflow action and writing data back — **From reading to doing**An interactive report triggering a workflow action and writing the result back.

API and MCP

The platform is addressable by your software and by your AI tooling at the same time. Workflows are callable from your application and authorable from any MCP-capable assistant.

Start runs, read results, and manage triggers over the API.
Connect an MCP client to author, debug, and deploy workflows in your instance.
Anything the interface can do is available programmatically, against the same runtime.

Runtara orchestration core connecting application APIs with MCP-capable AI tools — **Two ways in, one runtime**Application APIs and MCP-capable AI tools working against the same orchestration core.

Run it where the data already is

Tell us what the process is and which constraint you are under. We will show you the example builds running, then scope what the same thing looks like inside your environment.

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