Koretex is a marketplace of small, specialized models — fast, sovereign, and yours to run. Train one for your use case, deploy it on your own hardware or a distributed node, and take the cloud, the rate limits, and the surveillance out of your stack.
Now onboarding design partners.
The only defence against one all-powerful AI is a plurality of them — one in every hand, accountable to its owner.
Two or three companies now hold intelligence the way the East India Companies once held trade. Power that concentrated has no feedback mechanism and no real check. Koretex exists to hand that power back — small, capable models you can train, run, and own yourself.
Depending on one or two model providers has exposed a fatal weakness. They can play the monopoly card at any time. The best models earn the most resources, which buy the next, better models — a spiral that ends in concentration so complete that market forces simply stop functioning.
And privacy is not the whole answer. Even when a provider does not train on your data, it learns from your agentic traces, absorbing your processes and your subject-matter expertise in an ambient way. The wrapper you built today is the competitor they launch tomorrow.
A centralized AI can never truly advocate for you. When you and your counterparty both run on the same provider, you are sock puppets talking to each other.
The defence against a single super-intelligent AI is not regulation written by the people who would benefit from it. It is plurality — capable models distributed so widely that no one of them can dominate the rest.
When intelligence is spread across many owners, there is no single point to capture, no monopoly to bend you to its will, and a real feedback loop on how resources are used. Power stays answerable.
A new class of models — from a few hundred million to tens of billions of parameters — runs on the silicon you already own. The capable ones run on consumer hardware; the smallest run on a mid-range phone. You are not in Opus territory yet, but a Gemma-class model trained on your use case cuts inference costs radically and keeps your stack sovereign.
Cal AI proved it. A calorie counter built on a cloud API, it blew up — then the bills exploded and the rate limits arrived. So they trained a small model fit for their one purpose and cut the provider out entirely. Inference costs fell to almost nothing. No data leaving. No limits.
A small model is a specialized tool. It does its one job better than a far larger generalist can.
Several small models, combined, rival — or beat — a single Fable-class model on real work.
Phone, laptop, or your own node. The marginal cost of a token approaches zero.
It will not do everything a large general model can. But the one thing it is designed for, it does exceptionally — and often better than a far larger model that was built to do everything at once.
And they compose. OpenRouter has shown a fusion architecture where a number of smaller models, working together, deliver results that rival or beat even a frontier model. Plurality is not only safer. It is competitive.
At Koretex we are building a marketplace where those with the expertise to train models are rewarded for it — and where you can run the models yourself or pick a hosting provider from a distributed network of inference nodes. Either way, the stack stays yours.
The applications already run from the everyday to the industrial:
Train it, host it, or run it on your own silicon — and never rent your intelligence again.
Every centralized provider treats your work as a stream to be uploaded, metered, and learned from. Koretex was built so that none of that has to happen.
| Koretex | OpenAI | Anthropic | Cloud wrapper | |
|---|---|---|---|---|
| Runs on your hardware | ✓ | ✕ | ✕ | ✕ |
| Your data & traces stay yours | Always | No | No | No |
| Marginal cost per token | ≈ $0 | Metered | Metered | Metered |
| Rate limits | None | Yes | Yes | Yes |
| Vendor can absorb your product | Never | Yes | Yes | Yes |
| Works air-gapped | ✓ | ✕ | ✕ | ✕ |
Vendor practices summarized from public documentation, as of 2026. Centralized providers process your content on their infrastructure and may learn from the way you use it. Koretex models run on hardware you control.
And anyone whose future cannot be entrusted to a single provider.
If your business can benefit from a sovereign AI stack — lower costs, no rate limits, and data that never leaves your control — we would love to work with you.