Own the model
behind your product.#
You proved the feature works. Now you need a model you can own, deploy on your infrastructure, and control.
Early adopters get priority access and launch benefits.
Task families
Tasks where open-weight models
are already production-ready.#
A class of backend tasks shares a property: the model reads meaning and returns a structured result. Routing, classification, extraction, normalization, redaction. For these patterns, open-weight models often match frontier quality. When they don't, Arkor closes the gap.
Semantic routing
Classify an incoming message, event, or request into the right queue, handler, or team. No keyword rules. The model reads intent.
Classification
Label content by type, topic, sentiment, or policy category. Works on free-form text, support tickets, user feedback, anything without a fixed schema.
Extraction
Pull specific fields out of unstructured text: names, dates, amounts, product identifiers. Returns data your backend can act on directly.
Normalization
Take free-form or multilingual input and resolve it to a canonical backend representation. Handles variation, language differences, and format inconsistency.
Redaction
Identify and suppress sensitive, regulated, or policy-violating content before it is stored, logged, or passed downstream. The model understands what to remove based on meaning, not just patterns.
The problem
Owning the model is harder
than it should be.#
API calls are not a deployment
You've validated the task. Calling a frontier API forever is not a product you own. You need a model behind your own endpoint.
GPU infra is a full-time job
Provisioning compute, managing training jobs, building evaluation pipelines. That's a separate discipline, not a weekend project.
Per-token cost compounds
API pricing makes cost opaque. At volume, you can't own your margin when you don't own the model.
Arkor removes that burden. You bring the task. Arkor handles the rest.
See how Open-weight models perform on semantic tasks.#
Three tasks your product already runs on a frontier API. See the same task handled by the base open-weight model, the Arkor-deployed version after training, and the frontier reference. Same input, same prompt.
Support triage
A customer message arrives. The model reads intent, assigns a category and urgency level, and recommends a next action. No keyword rules, no routing trees.
Waiting for input.
Waiting for input.
Waiting for input.
Models run via OpenRouter.
How it works
From API prototype to
deployed model in three steps.#
Arkor works with supported teacher/student model pairs. You don't configure training infrastructure. Describe the task and approve the results.
Choose a supported model pair
Select from Arkor's supported teacher and student model combinations. The teacher model generates training signal. The student open-weight model learns the task.
Compare outputs and launch training
Review side-by-side output quality before committing. When you're ready, launch training on Arkor's managed GPUs. No infrastructure to configure.
Deploy behind a production endpoint
The trained model deploys behind a production endpoint you control. Swap in your existing application code. The model is yours.
Why Arkor
Built for developers who ship.#
Own your deployment
The resulting model is yours. Deploy it on your infrastructure, behind your endpoint, on your terms.
Run where your product needs it
Your deployed model runs on the infrastructure your product already uses. Not locked into a third-party provider.
No GPU ops to manage
Training runs on Arkor's managed GPU infrastructure. You never provision, configure, or pay for idle compute.
Everything defined in TypeScript
Tasks, training configurations, and deployment definitions live in TypeScript. Fits inside the stack you already have.
Common questions
What to know before you start.#
- What model pairs are supported?
- Arkor supports a curated set of teacher and student model combinations. Supported pairs are listed in the platform and updated as new compatible combinations are validated. You select from this list; arbitrary teacher/student combinations are not supported.
- Where can my deployed model run?
- Arkor deploys to endpoints you control. The trained open-weight model can run on your own cloud infrastructure, a VPS, or any environment that supports the model's serving requirements. The deployment target is yours.
- How are GPUs, training, and deployment handled?
- Training runs on Arkor's managed GPU fleet. You don't provision or pay for idle compute. You pay for training runs. Once training is complete, Arkor packages and hands off the model. Deployment configuration is defined in TypeScript.
- Is the workflow restricted to permitted model combinations?
- Yes. Arkor supports only compatible model pairs and deployment workflows that fit applicable provider terms and model licenses. This is enforced at the platform level. You can't configure a workflow that falls outside permitted combinations.
Your model.
Your endpoint. Your terms.#
Arkor gives application developers a path from frontier API prototype to production-deployed open-weight model, without the ML operations overhead.
Early adopters get priority access and launch benefits.