Model improvement for application developers
The model layer
belongs in your
workflow.
LLMs are already part of how products are built. Calling frontier models via API is often the right place to start. But it is not always the right end state for a product.
The next step isn't just calling larger models through APIs. It's smaller, cheaper, task-specific models that fit inside real products.
That layer shouldn't require becoming an ML engineer.
Early adopters get priority access and launch benefits.
Why hasn't this been part
of your workflow yet?
Most application developers want better model behavior, but fine-tuning has felt distant. Not because it's intellectually hard, but because the practical friction is too high.
No GPU access
Most developers don't work in environments where GPU access is already part of the workflow. That alone makes fine-tuning feel like someone else's job.
Cost feels opaque and risky
Even when GPUs are available, the cost is hard to predict. One wrong configuration can mean a large bill. So developers avoid the space entirely.
It feels like ML work, not product work
Training settings, data formats, model choices: the prior knowledge required makes it feel like entering a different professional world, not extending your own.
Arkor reduces the friction.
We build the model-improvement layer for application developers.
Not a service that takes over your ML strategy. A developer tool that makes improving models a practical option within the workflow you already have.
Smaller models, shaped for real product work
We focus on making smaller, task-specific models perform at the level of frontier models, for the specific tasks that matter to your product. Cheaper to run. Easier to deploy.
Transparent cost, no surprises
Training cost, inference cost, deployment cost are all first-class concerns. You should know what you're spending before you spend it.
No ML expertise required to get started
You shouldn't need to know training settings or ML-specific tooling to improve your model. Arkor abstracts that layer so you can stay in your normal development workflow.
How we see it
LLMs are already an application primitive
The question is no longer whether to use LLMs. It's how to use them inside real products with acceptable cost, latency, and operational burden.
Small models are not a compromise
For many product tasks, a smaller task-specific model is the better tool. What matters is being useful, fast, and deployable, not maximizing general intelligence.
Model improvement belongs in the developer workflow
Improving model behavior doesn't have to stay in ML teams. Over time, it should become a normal part of building a product.
There's a layer here
you haven't used yet.
You don't need to become an ML engineer to work at the model layer. You need a way to work at this layer without entering the full ML world.
Early adopters get priority access and launch benefits.