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Building a Self-Sovereign AI Training Loop: Improving Models Without Distilling From Frontier Providers
The default way to make a small model smarter is to have a big one teach it. We deliberately don't. Here's why a closed improvement loop — fed only by your own corpus and human corrections, never by a frontier model — is the harder path, and the right one if you intend to run anywhere.
Before you read
By Aaron Gammon · Founder, Inferrex · June 2026
This is the most technical thing I've written for this collection, and it comes with the strictest version of my standing rule: I'll share principles and outcomes, never the methodology. The specific recipes — how datasets get built, how layers are structured, where the thresholds sit — are the proprietary core, and I sell what the factory produces, not the schematics. So you'll find arguments and results here, and deliberately no blueprint.
I'm writing it because the position itself is uncommon enough to be worth defending in public, and because I'd want to read it if someone else had taken this path. Live platform figures, including model accuracy, are at inferrex.com/claims.