Beyond real time: The reflex imperative for trading AI
Real time is no longer enough. In this issue, we explore how capital markets firms are moving beyond reaction speed to build AI systems that anticipate change and act ahead of it.
In our lead story, our CEO Ashok Reddy looks at why the real challenge in predictive execution isn’t model quality, but infrastructure. When systems aren’t built for continuous, high-speed adaptation, even the most promising AI pilots stall at scale. The firms that succeed have systems that can simulate, decide, and act before conditions shift, and do so reliably across data volumes and teams.
This need for operational foresight continues with the latest episode of the Data in the AI Era podcast, featuring Cat Turley , CEO of ExeQutionAnalytics . We discuss the dual role of trading analytics and how the most effective systems help teams pinpoint what’s moving the tape at a microstructure level, while also identifying broader shifts in regime or sentiment. Cat highlights how developments like temporal similarity search are extending this capability, enabling earlier signal detection and more proactive decision-making.
That same shift toward earlier, more contextual signal detection is the focus of Ryan Siegler ’s blog on high-context trading. He shows how combining real-time data with unstructured inputs, from earnings transcripts to news and sentiment, can uncover signals that speed alone would miss. These hybrid systems don’t just react quickly, they reason more fully, helping firms improve decision quality, manage risk, and adapt to shifting market narratives in flight.
Ashok’s second blog provides a perspective on what makes AI systems resilient in fast-moving markets. He argues that the most important metric isn’t model accuracy but reflex: how quickly a system detects drift, simulates impact, and adjusts before losses build.
We close with a practical guide from Laura Kerr on building tick architecture with kdb+ and q. Laura breaks down what a clean, efficient setup for powering real-time and historical analytics pipelines should look like.
Let’s get into it.
Blog: Faster than real time: Scaling AI to predict, decide, and act before markets move
Speed isn’t enough. In a market where real-time reaction is table stakes, the next edge is anticipatory execution: simulating outcomes, optimizing strategies, and acting before conditions change. Our CEO, Ashok Reddy , explains why the biggest challenge isn’t the algorithm, it’s infrastructure at scale.
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Podcast: Cat Turley on the future of trading analytics
In this episode of Data in the AI Era, Cat Turley , CEO of ExeQutionAnalytics , joins us to unpack the operational realities of making analytics work in capital markets, where speed, trust, and clarity are non-negotiable.
Drawing on her experience leading data initiatives across global banks, Cat offers a practical look at what separates firms that generate insight from those that just collect data.
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Blog: High-context analytics sharpen trading decisions and reduce risk
Not all alpha lives in microseconds. In this blog, Ryan Siegler explores how high-context trading, combining real-time data with unstructured signals and historical patterns, is giving firms an advantage that speed alone can’t deliver.
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Blog: The intelligence reset – Reflex over accuracy
Traditional metrics like accuracy and precision offer a rearview mirror. In latency-sensitive environments, they come too late. In this post, Ashok Reddy argues that modern AI systems must be judged not by what they got right but by how quickly they know when they’re wrong.
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Blog: Tick architecture made simple with kdb+
Tick systems power the heart of real-time trading infrastructure but building them shouldn’t feel like an obstacle course. In this practical guide, Laura Kerr walks through how to set up a complete tick architecture using kdb+ and q, without unnecessary complexity or tool sprawl.
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