Ocular AI positions itself as a partner for teams that need data beyond what scraping or synthetic generation can produce. Rather than selling generic labeled datasets, it runs a pipeline: a global network of domain experts (linguists, clinicians, lawyers, engineers, voice actors and similar specialists) produces real recordings, decisions and judgments, and the Data Foundry platform turns that raw material into structured training sets, reward and preference alignments, RL environments, rubrics and rigorous evaluation sets. Everything is organized by modality, language, accent and domain, so each discipline gets its own pipeline.
The platform's public research arm reinforces the ambition. Ocular AI publishes the Converse benchmark family — Converse-STT measures word and character error rate for conversational speech-to-text, while Converse-Diarization measures diarization error rate for two-person American English conversations — alongside technical reports, datasets and an open-source scoring harness. These leaderboards rank frontier and open models side by side, giving the industry a shared yardstick for voice-native AI.
The company targets three audiences: frontier AI labs that need expert-level data for post-training, enterprises deploying voice, vision or agentic products that must work in the real world, and the experts themselves, who find paid work through the Ocular expert network and the Workbolt freelancing side. Pricing is not published on the website; prospective customers are routed to [email protected], and some datasets are released as free open source.
