Faster than real time: Scaling AI to predict, decide, and act before markets move

As the race to real-time evolves, we look to the next frontier: scaling AI to move beyond reaction and into anticipation.

“It is far better to foresee even without certainty than not to foresee at all.” — Henri Poincaré

When milliseconds can mean millions of dollars, it’s no wonder capital markets firms have spent so much blood and treasure on capabilities like real-time analytics and low-latency execution. Unfortunately, fast trades are no longer enough; today’s firms need to go beyond real time to keep their competitive edge, acting before markets move.

In markets where real-time analytics is merely table stakes, AI can simulate millions of potential outcomes faster than a human can take a single breath. This million-scenario AI advantage is the next frontier of capital markets: simulating all possible futures and selecting the one that wins.

Harnessing this kind of anticipatory AI is like having your own time machine. It promises the ability to predict, decide, and act preemptively, at machine scale and speed, in an environment that’s faster, more complex, and more volatile than ever.

And the defining challenge of building this AI time machine? Not clever algorithms, but scale.

Data at scale: 99.999% noise, 0.001% edge

“Information is the oil of the 21st century, and analytics is the combustion engine.” — Peter Sondergaard

Capital markets data is vast, volatile, and deafeningly noisy. Across the industry, firms are under growing pressure to assess more information, more quickly. Whether it’s hedge fund quant research or FX post-trade analytics, the time window to generate useful insight is narrowing fast. Meanwhile, an increasingly unstable macro environment shaped by sudden policy shifts and geopolitical shocks can make or break alpha in moments.

Buried somewhere in all that chaos is a fleeting signal: your edge. AI is the obvious tool to find it, but models often break when bombarded with constantly changing, incomplete, and conflicting inputs. Compared to simpler training challenges like computer vision, where a cat is always a cat, market data is the ultimate AI stress test.

Moreover, alpha is ephemeral and decays quickly. The faster you can isolate and act on a signal, the more value you capture before the market adapts to your edge.

KX is built for this environment. From the Big Four in the U.S. to the Big Three in Japan, we help leading institutions ingest, normalize, and analyze vast data streams in real time, spotting signals before they disappear.

Development at scale: AI pilot pitfalls

“Good data, good processes, and good judgment—that’s what drives AI success.” — Erin Stanton

AI pilots are everywhere as organizations invest in discriminative and generative AI (GenAI) to accelerate insight and action. To pick just one example, BNP Paribas is currently experimenting with more than 700 AI use cases and more than 25 GenAI use cases.

But while funding is growing fast, capturing enterprise-level value from AI can be frustratingly elusive. It’s relatively easy to build a proof-of-concept in a sandbox. It’s hard to grow pilots into production-grade systems that deliver continuous value; systems that learn, adapt, and perform under live market pressure.

Why do most pilots fail to scale? Because the underlying infrastructure wasn’t designed for this level of complexity and can’t support the demands of real-time performance. Firms take models that worked in isolated pilots and bolt them onto systems that were never designed to handle live data at production speed. High-latency pipelines, siloed teams, and slow feedback loops kill the speed and agility AI demands.

For decision-makers in capital markets, this isn’t just a technical problem, it’s a strategic one. When infrastructure can’t keep up, the best ideas stall before they create value. According to Gartner, at least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025, while McKinsey states that 80% of all AI initiatives fail to achieve their intended bottom line impact.

To overcome these barriers, KX ensures a solid foundation of complete data, lightspeed analytics, and extreme efficiency. Our unified platform is a real-time AI execution engine: designed for live data, live decisions, and live learning. It also connects data science, engineering, and production teams with one system, streamlining every phase of AI development.

I’ll dive deeper into how we industrialize AI in my upcoming blog on the AI Factory approach.

Performance at scale: Enter the time machine

“The only problem with market timing is getting the timing right.” — Peter Lynch

AI scalability isn’t just a challenge; it’s a differentiator that reshapes the market in your favor. To seize advantage, leading firms are embedding AI-driven decision-making across every part of their businesses and turning it into an automated, continuous process.

Models are trained on historical and streaming data in real time via automated feedback and updates, helping them maintain top performance as conditions change. This improvement loop (data in, model updated, signal out, action triggered) runs around the clock, enabling advantage across trading, risk management, the customer lifecycle, and many other areas.

The goal isn’t just reacting faster. It’s anticipating smarter. Our high-performance data layer, including KDB.AI for unstructured data and kdb+ for time series analytics, lets firms simulate millions of future outcomes before making a move.

Our partnership with NVIDIA also ensures the performance needed to bring AI inference to capital markets. Leveraging NVIDIA’s powerful GPU acceleration enables streaming-first AI at industrial scale to power faster decisions, sharper models, and better outcomes.

From backtesting to forecasting, think of this as a time machine for decision-making, giving you the temporal intelligence to make the right call. Before reality catches up, you’ve already played out the scenarios, eliminated risk, and acted on the best signal.

Trust at scale: Solving explainability

“The future depends on how we build trust into AI.” — Fei-Fei Li

Before you switch on your AI time machine, you need to know you can trust it. If you can’t rely on its insights or decisions, anticipatory AI doesn’t just fail to deliver value, it becomes an active risk. You need a systematic approach that ensures repeatability and explainability.

In highly regulated markets, AI explainability isn’t optional, it’s mandatory. From MiFID II to the EU AI Act, firms must be able to prove how and why every automated decision was made. An error or hallucination that causes inaccurate real-time reporting, insider trading, or a misstep in algorithmic execution could come at a high price.

But this isn’t just about meeting regulatory standards, it’s about building trust in every AI-driven decision. Errors that dent confidence can delay or halt your firm’s AI journey internally, while any external impact could also damage your revenue, brand perception, or customer loyalty.

To meet this challenge, KX drives consistent and explainable outputs across teams and use cases:

• Every data point, model update, and decision is logged and timestamped
• Every trade or alert is fully auditable and repeatable
• Every decision can be reviewed to understand exactly why it was made

Proven at scale: Why KX?

“Applying AI is often harder than not applying it. But when done right, the payoff can be tremendous.” — Heidi Lanford

In today’s real-time economy, foresight is your unfair advantage. As boundless AI ambition collides with real-world infrastructure constraints, KX gives you the ability to act with conviction before others even see the signal.

Our high-performance data layer is engineered for the unique demands of AI in capital markets:

• Real-time pipelines: Process and act on data with microsecond latency to support alpha generation, automated execution, and dynamic risk management
• Unified data access: Integrates structured, unstructured, historical, and streaming data into a single pipeline
  Time-aware architecture: Natively handles high-frequency, time-series, and streaming data, the lifeblood of capital markets AI
• Closed-loop learning: Supports continuous model updates and feedback loops that help AI systems adapt as markets evolve
• Integrated model lifecycle: One environment for exploration, training, deployment, and monitoring eliminates version drift and accelerates time to value
• Enterprise-grade governance: End-to-end auditability, explainability, and compliance to meet regulatory and risk mandates

We’re ready to be your data backbone when it comes to the heavy-lifting AI demands. We’ll support you with the fastest time-series analytics platform out there, known for its ability to efficiently manage mission critical data at massive velocity and scale.

We ensure firms don’t just keep up, they get ahead.

Are you ready to go faster than real time? See why the world’s leading firms, including the titans of Wall Street, trust KX to maximize AI’s value with continuous, high-performance analytics that power agile decision-making intelligence