Caudals
AI startup building a B2B marketplace and managed data operations layer for ML-ready datasets.
Cofounded and leading an AI startup that helps businesses monetize proprietary data and helps AI teams buy clean, compliant, ML-ready datasets.
Overview
Caudals is an AI startup focused on one of the hardest parts of applied AI: getting access to clean, compliant, domain-relevant data. The company sits between data-rich businesses and AI teams that need high-quality datasets for training, fine-tuning, evaluation, and enrichment.
What we are building
Caudals combines two layers:
- a B2B marketplace for proprietary datasets,
- and a managed data operations layer that turns raw source material into ML-ready assets.
That means the work is not only about listings or demand generation. It also includes ingestion, rights review, privacy handling, schema normalization, labeling, quality assurance, packaging, and buyer delivery.
Why it matters
Many AI teams do not struggle because models are unavailable. They struggle because the right data is fragmented, messy, legally unclear, or operationally difficult to buy. Caudals is designed to reduce that friction and create a more credible supply layer for AI-ready datasets.
My role
As Cofounder & CTO, I lead the technical direction behind the product and operations model:
- defining the platform architecture,
- shaping the AI data operations pipeline,
- building the internal workflows that move datasets from raw intake to buyer delivery,
- and aligning technical execution with commercial reality.
The result is a company positioned not just as a directory or services shop, but as a transaction and operations layer for AI data.