Moshi CEO Anekha Sokhal on a recommendation engine for traders
Original title: The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi
On the Latent Space podcast she describes fitting market data, workflows and recommendations to how energy-derivatives traders already work.
At AI Engineer New York, Moshi CEO Anekha Sokhal told Latent Space about leaving energy-derivatives trading at JPMorgan to build a recommendation engine for traders’ books. She compares Moshi to a mix of market data, a working interface and tailored recommendations. The conversation also covers trusted sources, document extraction, building versus buying models, and engineers told they must replace a whole team.
Unconfirmed: The page warns that the automated transcript may contain errors.
- Sokhal’s background is mathematics at Warwick and energy-derivatives trading.
- She says Moshi connects market information to what matters for a trader’s book.
- The episode discusses customer meetings at the conference before hiring.
- Show notes mention the GIQ research paper and Reducto for document extraction.
From the original article
16 min read at latent.spaceHow Moshi turns market data into actionable insights, and why AI tools have to fit the trader’s workflow.
By Latent.Space · latent.space
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