Why we built WriftAI

Over the past few years at Sych, we kept running into the same situation. A team wants to add a machine learning capability to something they're building. The model they need exists. The problem they want to solve is clear. What stops them is everything around it.

GPU provisioning. CUDA environments. Container images. Serving infrastructure. Cold starts. The cost of running it all. None of this is the actual problem they're trying to solve. But all of it has to be in place before anything runs in production. For teams whose core work was not ML infrastructure, this was routinely weeks of engineering time, before a single prediction could be made.

We saw this enough times, working with different teams on different projects, that it stopped feeling like an individual team's problem. It was the same problem everywhere. The same detour, every time.

WriftAI is our attempt to close that detour. The API went live today at api.wrift.ai/v1. You send a prediction request with your model and input. You get output back. No servers to manage, no CUDA to configure, no serving infrastructure to stand up or maintain.

The reason I think this matters beyond operational convenience is that software has always evolved through layers. Operating systems abstracted hardware. Databases abstracted storage and retrieval. Web frameworks abstracted networking. Package managers made dependencies something you declare rather than something you build. Each new layer made the one above it faster to build, and opened up development to people who did not have to understand everything underneath.

Machine learning inference is the next layer, and right now it is largely missing from the stack for most teams. What exists is either a cloud service that bundles everything together in ways you cannot control, or a self-managed setup that requires expertise most teams do not have and do not want. The thing that is missing is the layer that simply runs the model and gives you the result, the way a database simply stores and retrieves data.

That is what we are trying to be.

We have been running this with a small number of partners for a while, working through the places where it breaks down and fixing them.

It is early. The documentation is thin. Not everything is smooth yet. But the core loop works, and it is running real models. If you are spending time on ML infrastructure that is not your actual problem and want early access, request access.