AI New Product Buy/Pass Evaluation Scorecard
Discovery Lens
F Pain Point Scan
Specific, urgent, and still unsolved — the kind of pain that converts
One-Liner
When a sales rep brings a new specialty product, the buyer enters 6 data points; AI evaluates fit against the store's current assortment, calculates margin at suggested retail, estimates velocity against comparable category items, and generates a structured buy/pass recommendation with rationale.
Kill Reason
This is a simpler variant of an AI evaluation scorecard that any specialty retailer can build in a spreadsheet or generate with a general-purpose AI assistant in under an hour. There is no proprietary data or switching cost that would make a buyer pay for a standalone product.
What do you think?
Related ideas you can explore free:
killed: Document collection and compliance checklists for vendor onboarding are solved by general-purpose tools (PandaDoc, Airtable automations, Checklist.com) that any food hall manager can configure in a few hours — food-type-specific permit logic adds minor value that does not justify a standalone product or prevent a well-resourced competitor from replicating it immediately.
killed: Established allergen labeling services (Nutrifox, MenuCalc, Genesis R&D) already handle FDA and EU compliance for small food producers, and general LLM tools can generate allergen matrices on demand. There is no proprietary data or technical barrier to prevent instant replication.
killed: Event run-of-show timelines are a commodity output that any AI assistant produces in minutes from a simple prompt. Event management platforms (Honeybook, Perfect Venue, Planning Pod) already include timeline features, and the AI generation layer adds no defensible differentiation for a standalone product.