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Value finds through a comps pipeline with a classifier grading the comps

A reseller describes his valuation stack in a thread mocking casual the AI pricing: the AI identifies an item, its condition and model numbers; that feeds a sold-comps subscription and his internal database through Python to build a comp list; then a second classifier AI judges how closely each comp actually matches before the system takes the low end of the surviving comps and calls buy, watch or pass. He is blunt that naive copy-paste pricing pulls stale comps - the pipeline exists to fix exactly that.

How it works

  1. Photograph the item
  2. AI identifies it with condition and model numbers
  3. Query the sold-comps subscription and internal database via Python
  4. A classifier AI scores how closely each comp matches
  5. Take the low end of validated comps
  6. Compute buy, watch or pass

Why it’s worth knowing

One AI's only job is to distrust the other AI's evidence - it grades every comp for closeness before a single dollar figure is trusted.

Where it’s from

Summarized from a public post on reddit.com. See the original ↗

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