Retab Secures $3.5M In Pre-seed Funding Round And Launches AI Platform For Developers

Retab offers a developer platform and SDK designed for the age of large language models. Developers just define the data schema they need—Retab handles the rest, including dataset labeling, evaluation, prompt engineering, and model selection.

The idea came from the founders’ experience building internal automation tools for logistics workflows. Their key insight wasn’t just the results but the orchestration layer that made AI models work reliably. That innovation became the foundation of Retab.

Louis de Benoist, co-founder and CEO of Retab shared: People keep building demos that look like magic, but break the moment you put them into production.

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.

Today, dozens of companies rely on Retab’s all-in-one platform to turn messy PDFs, handwritten scans, and other unstructured documents into clean, structured data—without depending on fragile third-party tools. Users only need to define the data they’re looking for and upload their files; Retab takes care of the rest, from dataset labeling and extraction to evaluation and benchmarking. It automatically assigns tasks to the best-performing AI model and seamlessly switches to newer, more efficient models as they become available.

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Unlike traditional large language models, Retab serves as the intelligence layer that makes advanced models from providers like OpenAI, Google, and Anthropic truly practical for high-stakes, real-world use. By managing the entire document extraction process with verified accuracy, Retab empowers teams to replace manual tasks with fast, precise, and continuously improving workflows—ideal for processing contracts, invoices, compliance records, and more.

Retab is the OS for reliably extracting structured data. 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, said de Benoist.

With a lean team of just ten employees and a fast-growing developer community, Retab is positioning itself as a foundational layer in the AI infrastructure stack—built not just to showcase AI’s potential, but to empower others to build with it.

Customers in sectors like logistics, finance, and healthcare are already seeing the impact. One large trucking company used Retab to identify the smallest and fastest model that still delivered 99% accuracy, slashing operational costs. A financial services firm now extracts both quantitative data and risk insights from 200-page reports—work that previously took analysts days. Other users are applying Retab to streamline tasks like claims processing, medical records handling, identity verification, and onboarding, all with minimal setup.

The funding round was supported by top early-stage investors including VentureFriends, Kima Ventures, and K5 Global, as well as notable angels like Eric Schmidt (via StemAI), Olivier Pomel (CEO, Datadog), and Florian Douetteau (CEO, Dataiku).

Investor Florian Douetteau emphasized that the broader adoption of AI depends on turning document-heavy operations into structured, reliable data—the kind that autonomous systems can actually understand and act on.

On a large scale, this process hinges on quality control, cost efficiency, and rapid implementation. The team at Retab understands this thoroughly and is uniquely positioned to solve it for the thousands of AI-first companies that are emerging.

Looking ahead, Retab is expanding beyond documents to bring its reliable data extraction methods to websites. It’s also launching integrations with tools like n8n, Zapier, and Dify, making it easier to streamline end-to-end workflows.

At the core of Retab’s long-term vision is becoming the intelligent middleware between the world’s unstructured data and the AI agents that rely on it. Whether it's a loan file, contract, or customs manifest, Retab transforms messy content into clean, secure, and programmable data.

The newly raised funding will support further platform development, community growth, and scaling of infrastructure to meet rising demand from vertical AI startups and internal innovation teams.

About Retab

Retab is a full-stack developer platform and SDK for next-gen document processing with LLMs. Define schemas in plain language, auto-label data, run evaluations, and deploy in minutes. It handles file edge cases and picks the best model automatically so you can focus on building, not infrastructure.