03 / 05 · Gate
You only see what passed.
Most generation pipelines hand you everything they made and let you cull. Axion Studio culls before you see a single asset. The Gate runs three judges — one fast, two slow — and only assets that pass make it to your review queue.
Layer 1 — fast
SigLIP-2 baseline on every asset
SigLIP-2 (running on RunPod self-hosted A100s) checks brand similarity against your reference set. Outputs a 0–1 score. Anything below your workspace's threshold (default 0.85) gets rejected immediately and queued for re-generation with feedback. ~80% of obvious misses caught here.
Layer 2 — slow
Dual-judge in the grey zone
Anything that scores between 0.85 and 0.92 — the grey zone where SigLIP isn't sure — goes to two reviewers: GPT-4o vision and Gemini 2.5 Pro vision. Each gets the brand reference set, the brief, and the asset. Each returns a structured verdict with reasoning.
Both must agree for the asset to pass. If they split, a human review is queued (rare — <5% of assets reach this point).
Auto-regen
Misses don't waste budget — they teach
When a judge rejects, the rejection reason is fed back into the next generation pass as negative guidance. The system learns your brand's grey zone over time, and the re-generation hit-rate climbs from ~50% to ~85% by the time a workspace hits its 100th approval.
By the numbers
What the Gate prevents
Vertex AI Eval Service
The gate trains the LoRA
Every gate decision is logged to BigQuery and fed into Vertex AI's Evaluation Service. That dataset trains the next LoRA refinement pass — so the model gets better at hitting your brand without ever needing you to label data manually.