
How to Prevent AI From Scaling the Bias Already in Your Data
Your data is almost certainly biased. What matters is how your AI acts on it. Learn how knowledge graphs help make AI decisions defensible.

Steve focuses on model-led and data-driven technologies, particularly quantitative research to production workflows in financial services front and middle offices. At Quantexa, Steve advocates analytics, AI, and data management capabilities for the Quantexa Platform.
Prior to Quantexa, he worked for time-series and vector database provider, KX, Java OpenJDK support organization Azul Systems, satellite data specialist Geospatial Insight, and MATLAB developer, MathWorks.

Your data is almost certainly biased. What matters is how your AI acts on it. Learn how knowledge graphs help make AI decisions defensible.
Why the real gap in enterprise AI isn't model intelligence but the context layer underneath it.

Context graphs are having a real moment, but are they a real foundation for decision‑ready agents and a genuine evolution beyond knowledge graphs, or an oxymoron dressed as innovation causing conceptual confusion?

As AI has evolved, a new discipline has emerged at the heart of enterprise transformation: Context engineering. How does it drive Agentic AI and how can it be guided by your trusted Quantexa data foundation?

Bad AI and bad decisions deliver adverse consequences. The world needs good AI and good decisions to drive beneficial consequences via the decision lifecycle. Effective scoring helps assess and bolster the best decisions.

How will agentic AI transform decision intelligence? What tooling and methodologies are emerging that will ensure successful enterprise adoption alongside the transparency and consistency that customers, regulators and governments demand?