Enterprise AI
Shadow AI Doesn’t Knock First
Why your employees stopped waiting for a governed answer, and what closes the gap for good. Ask any CIO what keeps them up at night in 2026 and you will hear some version of the same sentence: employees are putting company data into AI tools nobody approved. The instinct is to treat this as a […]
A Thousand Tables Deep
Where Enterprise AI Quietly Gives Up, and What Finally Gets It Past the Wall There is a moment in almost every enterprise AI program that nobody puts on a slide. The demo works. Someone asks a question in plain English, the AI writes a flawless query, the right number comes back, and the room nods. […]
Switch To Production: Building An AI-Ready Data Foundation
As organizations embrace enterprise AI, a fundamental question rumbles beneath the excitement: How do we govern enterprise AI and switch pilot projects into production? The answer depends less on AI’s unseen algorithms and far more on the data that supports them. Without a unified data foundation that is AI-ready, enterprise AI is not only difficult […]
Confidently Wrong: Why Enterprise AI Falls Off a Cliff the Moment It Meets Your Real Data
I want to start with the most dangerous sentence in enterprise AI today. It is not “the model failed.” A failed model is easy. It throws an error, someone gets paged, the query gets fixed. The dangerous sentence is the one that comes back clean. Well formatted. Confident. Delivered in plain English to a VP […]
Governing the AI log explosion: why every enterprise needs an intelligent archival strategy
Artificial intelligence is no longer a pilot project, it is mission-critical infrastructure. But with every model inference, agent workflow, and automated decision comes an avalanche of logs that traditional data platforms were never designed to handle. Solix Enterprise AI Data Archival Solution was built for exactly this moment: to help enterprises store, govern, and leverage […]
Strategic Evolution of AI Analytics using AI-ready Data Platforms
Abstract Life sciences organizations are rapidly moving from experimental AI pilots to production scale, agent-driven research workflows. As Model Context Protocol (MCP) based architectures gain traction for orchestrating queries across compound and target databases such as ChEMBL, BindingDB, and PubChem, performance constraints that were once tolerable in proof of concept environments are emerging as material […]