Build, experiment, and scale your AI research with confidence. Pipelines gives your team the infrastructure to explore fearlessly and iterate with precision.
Built by a team from
Every change measured. Every decision defensible.
Define prompts, models, tools, and other variables you want to use or evaluate.
Define prompts, models, tools, and other variables you want to use or evaluate.
Configure fields, data sources, and routing logic to produce evaluation or fine-tuning datasets. Assign any step to humans, models, or both.
Configure fields, data sources, and routing logic to produce evaluation or fine-tuning datasets. Assign any step to humans, models, or both.
Push data through your workflow and collect structured results at every step.
Push data through your workflow and collect structured results at every step.
Visualize and explore your data with built-in analytics.
Visualize and explore your data with built-in analytics.
Tweak variables, create variations, and run again.
Tweak variables, create variations, and run again.
Rigor is coming to AI.
Every team in the world is integrating AI. But almost none of them have a structured way to experiment before they ship. Instead, they rely on ad hoc testing and gut-feel evaluations — or worse, they skip experimentation entirely, push to production, and stamp fixes only after problems become obvious.
The problem isn't talent or compute — it's that there's no dedicated environment for structured AI experimentation. Comparing approaches, gathering human evaluations, and building evidence for decisions all happen in scattered notebooks, spreadsheets, and one-off scripts. The research process itself has no infrastructure.
But that's how every new discipline begins. And it's never how they stay. Higher standards always emerge — in science, in engineering, in medicine. AI will be no different. We're here to champion that transition: to give teams the tools to continually iterate and produce AI that isn't just functional, but optimized along every dimension they care about.
Every team is integrating AI. Almost none have a structured way to experiment before they ship. Eval tools either score production traces after the fact or test a handful of examples in a playground. Neither is experimentation.
Everyone agrees AI needs better evaluation. But what does that mean in practice? Here's the anatomy of an evaluation system designed for reproducibility, not just vibes.
Your team made a hundred model and prompt decisions in the last six months. How many can you defend with data? Rigor isn't about slowing down -- it's about building a foundation your team can actually stand on.
We're opening Pipelines to a small group of early collaborators. Work closely with us to shape the platform.
