Samaya

Sapien is an AI-powered market research platform that builds synthetic populations—simulated audiences grounded in purchase histories, behavioral and research data, demographics, surveys, and public conversations. It uses these populations to run surveys, qualitative interviews, and comparison tests for products, prices, and campaigns, with options to add customer data and past research for context. Sapien returns interactive reports with predictions, segment comparisons, recommendations, and the responses behind the findings. Users can ask follow-up questions of the same simulated audience; the service states that its team builds the audience, runs the study, and provides same-day predictions and interpretation.

AlphaSense is an AI and data provider used in hedge-fund workflows for research and data analysis. The videos describe it as a large-model and data service used to help investment teams analyze information.

Moshi is an invite-only AI market-intelligence product from Moshi Space for institutional traders, analysts, risk professionals, and portfolio managers. It uses four specialist analysts covering news, social, fundamentals, and markets, plus a Moshi Manager that reconciles their signals into a ranked stream of insights. The system watches live market data, answers what is happening, when it happened before, and what followed, and produces a consolidated briefing from the sources it monitors. Its insights are designed to be explainable, with deterministic, traceable calculations and cited sources. The service requires no positions or systems integration and states that user data is encrypted in transit and at rest, isolated per user, and not sold.

Roqoo is a holistic financial-data solution that integrates data from sources such as AlphaSense directly into an analyst's model and supports interaction with dedicated agents. The video describes it in the context of investment research and combining alternative data for analysts.

Vibe-Trading is an open-source AI personal trading agent and quantitative-finance research workspace developed by HKUDS. It turns natural-language finance questions into runnable analysis and backtesting workflows by connecting an agent to market-data loaders, portfolio and backtesting engines, report generation, persistent memory, and multi-agent research teams across asset classes. Its quantitative and risk-analysis capabilities include strategy backtesting, Heston stochastic-volatility pricing, hierarchical risk parity, copulas, and market-microstructure estimators. The project provides API and MCP interfaces, broker and market-data connectors, portfolio-management and broker-authorized trading workflows, examples, documentation, a demo, and a Shadow Account for testing. It supports read-only account sources such as official IBKR MCP and opt-in Binance USD-M snapshots, while scheduling actions require explicit confirmation and live-order safeguards fail closed on contradictory or invalid data. Implemented as a Python project, it uses a connector onboarding system that stores connection secrets in the operating-system keyring and is distributed under an open-source license.