Your work is specific.
Your models can be, too.
A few years ago, putting a language model into a product felt like the breakthrough. Write a prompt. Make an API call. Get something useful back. A first version could come together in an afternoon.
Then it met the actual work. The documents with their own conventions. The support replies that were almost right. The same small correction, made for the twentieth time.
A general model can do many things. Your work asks it to do a few particular things, over and over. That gap is why we’re building Middleware.
First, access got easier.
More models arrived. Frontier models pushed capabilities forward. Open models gave developers more room to run and adapt them.
Tools like OpenRouter made many models available through a common API. Trying a different model became a smaller engineering decision. That was real progress.
But choosing a model still leaves the work of making it fit. It might be excellent at writing, yet miss your team’s conventions. It might understand a document, yet repeat the same mistake with your particular format.
Then, we worked on the context.
We added instructions, examples, retrieved documents and tools. Context engineering gave a name to an important job: putting the right information in front of the model at the right time.
That work matters. It gives a model facts it couldn’t otherwise know. It helps it act within a workflow. We expect it to remain part of building useful software.
There is another opportunity in what happens after a response. Someone fixes a field. A reviewer accepts an answer. A task succeeds or fails. Over time, that is evidence of what good work looks like.
A correction can help the next prompt. We think it should also help shape the model behind it.
Let the work shape the model.
Middleware sits between your application and the models you use. We’re building it to turn corrections and outcomes into small, task-specific adaptations to open models. Those adapted models can handle recurring work alongside the frontier models you already trust.
Think of a team extracting information from the same kinds of documents every day. Each corrected field says something about the task. Enough useful examples can become the basis for an adapted model that handles that job better.
This takes evidence. A response is not automatically a good example, and a new adaptation is not automatically an improvement. The aim is to introduce changes when the results support them.
You keep your application and workflow. Middleware handles the adaptation work, without a separate training pipeline for every task.
The goal is simple: more correct work, fewer repeated fixes, and models that get better at the jobs you actually need done.