Blog

Featured

Our latest post

Rockfish Vision: Every Enterprise, Data Ready for AI

Every AI team tells the same story — the demo works, then the project quietly stalls between "this works" and "this is in production." The gap was never talent or ambition. It was data. Here's where we're headed.

Read article →

MGMuckai Girish, CEO•Aug 3, 2026•4 min read

Latest Posts

Ideas on synthetic data, agent evaluation, and reliable AI

Continuous Pipelines Break Without Continuous Data

Synthetic Data

Modern analytics and AI systems evolve continuously — schemas change, models are retrained — yet the data used to test them stays static. Here's how Rockfish closes that gap.

Least Privilege Meets Information Theory via Generative AI

Synthetic Data

Two conflicting views on the nature of data: is it the "new oil," or something we should share as little of as possible? A look at both camps through the lens of generative AI.

Why Schema-Driven Generation Changes Everything

Synthetic Data

Data availability has quietly become one of the biggest bottlenecks to AI innovation. Schema-driven generation lets teams prototype, test, and validate before production systems even exist.

Synthetic Data in Cybersecurity: Closing the Data Gap

Cybersecurity

Recap of our webinar with Carahsoft and Cympire — bringing together public-sector, civilian, and defense teams to close the data gap in training and detection.

We've Heard of Synthetic Data — Now What?

Synthetic Data

From buzzword to business tool. A year of customer conversations shows the shift: teams have stopped asking what synthetic data is and started asking how to put it to work.

Understanding Synthetic Data Quality: A Beginner's Guide

Data Quality

Why should you care about synthetic data quality? Imagine training a new hire from a badly translated manual. A beginner-friendly guide to measuring it right.