ForecastOS provides Hivemind, an AI‑driven engine that transforms unstructured public discourse—news, filings, podcasts, and proprietary data—into point‑in‑time, company‑level exposure scores. The platform identifies emerging market narratives, quantifies their direction and magnitude, and delivers customizable factor data via API or UI for integration into quantitative investment and risk models, enabling institutional quants to capture narrative‑alpha and improve portfolio performance.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Institutional investors lack timely, quantitative measures of how emerging market narratives and public discourse affect company valuations, leading to gaps in risk assessment and missed alpha opportunities.
Solution
ForecastOS offers Hivemind, an AI‑driven engine that ingests unstructured text sources (news, filings, podcasts, proprietary data) and converts them into point‑in‑time, company‑level exposure scores. The system identifies market‑relevant trends, quantifies their direction and magnitude, and produces customizable factor data that can be integrated via API or UI into existing quant workflows. By providing dynamic narrative‑alpha signals alongside traditional risk models, Hivemind enables quants to capture emergent themes, improve out‑of‑sample variance explanation, and construct macro‑overlay strategies with measurable excess returns.
Target Audience
Primary users are institutional quantitative analysts, systematic portfolio managers, and risk teams seeking to augment traditional factor models with narrative‑driven signals.
Features
- AI‑powered text mining and embedding models that transform raw discourse into clean, point‑in‑time factor exposures for every security in a user‑defined universe
- Real‑time trend identification and ranking, delivering daily updates on emergent themes such as generative AI, geopolitics, inflation, and tariffs
- Customizable scoring pipelines (“knobs”) allowing users to adjust data inputs, weighting schemes, and output schemas via UI or API
- Export of exposure matrices in standard formats for seamless integration with backtesting, portfolio construction, and risk‑management systems
- Open‑source Portfolio Management library enabling backtesting and optimization in as few as ten lines of Python code
- FeatureHub library providing thousands of pre‑engineered point‑in‑time factors accessible with a single function call, eliminating data‑engineering overhead