Shopify Flywheel

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Shopify Flywheel
Shopify's self-improving AI pipeline: 0.8B model beats GPT-5.6, $27M→$1M cost

Production flywheel case study: Shopify fine-tuned a Qwen3.5 0.8B model to outperform GPT-5.6 Sol xhigh on a narrow buyer-profile generation task (AlphaSignal deep-dive · 2026-09-06 · news@alphasignal.ai). Key mechanism: 5-stage self-improving loop — (1) frontier model serves production, (2) LLM judges score every conversation (rubric: completeness, execution, response quality, safety), (3) frontier reasoning model critiques failures, (4) repairs are replayed and accepted if they pass the evaluator, (5) accumulated trajectories train the specialist via SFT + GRPO daily. Calibration: Cohen's kappa measures inter-rater reliability between 4 LLM judges and human annotators — labels accepted only when all 4 judges agree. Hard-negative mining from low-scoring production conversations. Key results: $27M/yr → $1M/yr (buyer profiles), 38% latency reduction, 16% throughput increase, 14% fewer GPUs, prompt tokens 6K → 1.5K. Shopify Sidekick GraphQL agent: 2,000 req/min, runs this loop daily. Score 8: real production case with concrete, reproducible flywheel pattern, quantified cost/latency metrics, and directly implementable LLM-judge calibration methodology.

What the reference demo shows: Gradio demo with 5 sub-tabs — (1) Flywheel Stages: interactive walkthrough of all 5 pipeline stages; (2) Failure → Repair: select a production conversation, see evaluator scores, frontier critique, and repaired output; (3) LLM Judge Calibration: Cohen's kappa table showing 4-judge consensus requirement; (4) Cost Analysis: frontier vs specialized model cost calculator; (5) When to Specialize: the 4 readiness conditions with Shopify examples.

## Reference implementation (Vector Ridge Labs, POC-031)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-031
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/shopify_flywheel
Original source: AlphaSignal deep-dive · 2026-09-06

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures task success rate, number of steps and token cost per task, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.