YPAI builds production AI systems and supplies the multimodal data those systems depend on, offering both services independently or as one connected engagement. The company manages collection, annotation, validation, and human evaluation across speech, video, image, text, agents, and robotics, with delivery backed by full traceability and EEA residency options. YPAI also designs and integrates AI systems such as RAG pipelines, document workflows, and custom applications, with a single accountable structure covering data and system performance together.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations deploying AI systems often face fragmented accountability, where the data used to train and evaluate models and the AI systems themselves are delivered by separate suppliers. This split creates gaps in quality, traceability, and performance, especially when a system fails due to missing data, edge cases, or evaluation gaps that cross the boundary between data and model.
Solution
YPAI provides a connected engagement model that delivers both AI data production and AI system implementation under one accountable structure. The company collects, licenses, annotates, validates, and evaluates multimodal data across speech, video, image, text, sensors, and robotics, while also designing, integrating, and deploying AI systems such as assistants, agents, RAG pipelines, document intelligence, and workflow automation. Every data delivery carries its record, including guideline versions, reviewer decisions, and acceptance criteria, and the company can engage at any stage—from source data creation to production behavior monitoring. YPAI supports 150+ languages across 50+ countries, with EEA-based processing and data residency available, and offers both independent service lines and combined engagements where system and data must improve together.
Target Audience
Primary customers are enterprises and AI teams that need production-grade multimodal data and AI system implementation, particularly those deploying models across languages, geographies, or regulated environments like healthcare, robotics, and multilingual applications.
Features
- Managed collection, annotation, validation, and human evaluation across speech, video, image, text, agents, and robotics
- Identity-verified contributors and credentialed domain experts for specialized tasks like clinical model evaluation and red teaming
- 150+ languages, 50+ countries, with EEA data residency and end-of-contract erasure SLA
- Every label bound to the guideline version it was made against, with second labeller and adjudication before acceptance
- Specialized validation including provenance audits, duplication checks, representativeness review, and consent verification
- AI implementation covering RAG systems, agents, document workflows, automation, and custom applications with deployment and regression evaluation
- Synthetic data evaluated for utility, fidelity, coverage, and downstream model performance, not treated as a substitute for real data
- Acceptance sampling, gold sets, and project-specific quality gates with full traceability records