Leading global quant asset manager builds scalable text-intelligence layer
For one of the world’s largest quantitative investment managers , overseeing more than $100 billion in assets, every second and every dataset matters.
The firm’s systematic macro team had a clear goal: to capture and interpret the world’s shifting narratives about interest rates, inflation, and geopolitics and translate them into data-driven trading signals.
But there was a challenge. Traditional data feeds couldn’t keep up. They were rigid, slow to adapt, and unable to provide the flexible, theme-specific retrieval the firm needed. Analysts were spending valuable time stitching together sources instead of focusing on modeling and strategy.
Finding clarity in complexity
To solve this, the firm turned to Bigdata.com. By adopting the Bigdata.com API, its research team built a new kind of retrieval layer, one designed for agility, scale, and seamless integration into their proprietary NLP engines.
The API enabled them to:
- Surface relevant narratives on demand from over 14,000 live web sources, dynamically adapting to new market themes.
- Feed structured content directly into their in-house NLP and modeling workflows, with no disruption to existing systems.
- Scale effortlessly across teams, replicating successful setups for equities and other strategies.
What once took weeks (sourcing, cleaning, and contextualizing text data), could now be done in a matter of hours.
Transforming research into results
With Bigdata.com as the foundation of its retrieval layer, the firm’s macro team accelerated the full research cycle:
- Faster time-to-signal: Analysts could stand up new data pipelines in hours, enabling faster hypothesis testing.
- Deeper insights: NLP models applied structure and context to retrieved content, uncovering patterns hidden in traditional datasets.
- Broader adoption: What began in the macro group soon expanded to equities, as other teams recognized the value of unified text intelligence.
The firm’s researchers described the shift as going from “searching for signals” to systematically generating them.
Building the future of systematic text intelligence
Encouraged by its success, the firm is now working with Bigdata.com to take this architecture further, transforming a tactical solution into a long-term data backbone.
Next steps include:
- Expanding content coverage to filings, transcripts, and macroeconomic releases.
- Moving from manual retrieval to continuous, automated ingestion.
- Laying the groundwork for autonomous research agents capable of querying, retrieving, and modeling with minimal human input.
From data to discovery
What began as an effort to improve narrative retrieval has evolved into a firm-wide vision: a future where every research team — across asset classes and regions — can access the same precise, flexible, and scalable text intelligence.
Bigdata.com helps quantitative investors move from information overload to structured alpha, with the speed, clarity, and depth modern markets demand.