About RailCompute

Agentic fine-tuning for AI models.

RailCompute helps teams fine-tune models faster and cheaper by automating the ML pipeline from data prep to evals, with no human in the loop.

The problem

Fine-tuning is still too slow.

Teams need to clean datasets, choose the right base model, manage GPUs, run fine-tuning, build evals, and package results. This usually needs ML engineers, infrastructure knowledge, and a lot of manual work.

What changes

Agents run the workflow.

RailCompute removes that complexity by letting agents prepare data, run fine-tuning, evaluate quality, and explain the result.

Outcome

Private models for real workflows.

The goal is to turn messy company data into a trained, evaluated, private model ready for business use.

Our mission

Make AI fine-tuning accessible to every team.

We believe the future of AI will not only be bigger general models. It will also be smaller, private models tuned for specific company workflows.

RailCompute is building the layer that makes that possible.

Founders

Building the agentic fine-tuning layer.

Navaneeth Krishnan
CEO

Navaneeth Krishnan

Product, company direction, and design partner relationships.

Aditya Ray
CTO

Aditya Ray

Training systems, agent workflows, and infrastructure architecture.