Retab launched a document AI platform, designed to handle dataset labeling and evaluations, automated prompt engineering and model selection for logistics, finance, and healthcare markets.
“People keep building demos that look like magic, but break the moment you put them into production,” says Louis de Benoist, co-founder and CEO of Retab. “We lived that pain ourselves. Wiring up fragile pipelines just to extract a few fields from a PDF. We built Retab because it’s the developer-first platform we always wished we had.”
Key takeaways:
· Retab’s AI platform is an intelligence layer that makes other models usable for critical workflows. Developers define the data they need, and Retab’s platform manages the entire lifecycle to ensure verifiable accuracy.
· An AI agent automatically tests and refines instructions based on a user’s documents, maximizing accuracy before the system ever goes live.
· The platform is model-agnostic. It automatically benchmarks and routes each task to the best-performing model for the job, whether the priority is cost, speed, or accuracy.
· Retab forces models to "think" step-by-step and uses a consensus mechanism among multiple models to quantify uncertainty, acting as a powerful safety net to ensure trustworthy results.
“Retab is the OS for reliably extracting structured data,” says de Benoist. “It wraps the best models in a layer of logic that actually makes them usable with error handling and structured outputs. That’s what devs need if they want to build production apps, not just prototypes.”
