The market mechanics behind every product we build: matching, margin, and risk under real pressure.
We treat AI as a decision-making layer, not a feature. In our work, that means models that forecast price movement, agents that manage risk exposure in real time without manual approval, and interfaces that let a user ask a plain-language question and receive a real answer from a complex financial system.
Global AI spending is projected to reach $2.52 trillion in 2026, and the market for AI agents specifically is expected to grow from $8.03 billion in 2025 to $11.78 billion in 2026, a compound annual growth rate above 46%. Gartner projects that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from under 5% a year earlier.
Market figures are drawn from public reporting current as of early-to-mid 2026. These move quickly, verify and refresh before relying on them.
Our AI work spans three layers, all running on production-grade MLOps: versioned models, monitored pipelines, and continuous retraining, since a model accurate six months ago is not automatically accurate today.
Forecasting and risk scoring models, trained on structured market and behavioral data.
Pattern recognition across market and behavioral data too complex for classical models.
Modern LLM-based agents for automation and decision support, wired directly into live systems.
This shows up directly inside the products we build for clients, and inside our own delivery process.
Let’s discuss how it applies to what you’re building.
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