Moshi builds recommendation engine for commodity traders
Anekha Sokhal, former JPMorgan energy‑derivatives trader and Moshi CEO, says the startup is creating a recommendation engine that turns massive market data into real‑time insights for commodity traders. Moshi ingests structured data such as futures time series and technical indicators, unstructured sources like news, podcasts and Twitter, and private user data from traders’ own systems. Sokhal…
Key points
- Moshi processes structured, unstructured and private data to generate real‑time trader alerts
- Sokhal says Moshi learns roughly 300 new facts in four seconds of processing
- The platform offers a recommendation system with confidence scores for conflicting sources
The system builds an n‑dimensional graph from cleaned and tagged data, then pushes tailored alerts to users, offering second‑order connections that a trader might miss—for example, how an energy‑equities move could affect oil prices. Moshi also provides confidence scores for conflicting sources and uses a vision‑language model to extract information from PDFs. The company is handling inbound interest from traders and AI engineers, scheduling meetings and a webinar to manage demand.
The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi
Latent Space · 22 October 2026
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