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Technical writing on AI deployment, reliability engineering, and the future of edge AI.
Introducing Grysics: Verification-First AI Deployment
Today we're publicly unveiling Grysics — a verification engine that tests, validates, and monitors AI models before and after they reach production.
The problem we set out to solve
Every AI team hits the same wall. The model works beautifully in a notebook — high accuracy, clean outputs, fast inference. Then it ships to production, and things start breaking. Not catastrophically at first — subtle drift, edge cases the test set didn't cover, hardware constraints nobody accounted for.
We spent months talking to engineering teams across industries. The stories were remarkably consistent: weeks of debugging deployment failures, models silently degrading in production, no clear way to know if a model would actually work on target hardware before committing to a rollout.
The tooling gap was obvious. There were great tools for training models, great tools for serving them — but almost nothing for the critical step in between: verifying that a model is actually ready for the real world.
More articles
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A deep dive into how Olyxee Edge Box translates research models into production-ready edge deployments across constrained hardware.
The State of AI Verification in 2026
An analysis of how the industry approaches model testing, where the gaps are, and what a mature verification practice looks like.
Why We Started Olyxee
The story behind Olyxee — what we saw missing in AI infrastructure and why we decided to build it ourselves.
Detecting Model Drift Before It Hits Production
How Grysics continuously monitors deployed models and catches performance degradation in real-time.
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