An AI concept for equipment price intelligence. We built the MVP, validated the idea, then made the call on what to do next.
EquipIQ was a concept piece. The question was simple: could an AI pull useful pricing intelligence on heavy equipment from messy public data? We built the smallest version that could answer that honestly, instead of guessing for months.
How we built it
Define the smallest useful answer
Pick one equipment category, one geography, and one question: is this listing priced fairly? Resist the urge to build everything.
Build the MVP fast
A scraping pipeline feeds an AI model that scores each listing against comparable sales. Just enough UI to actually use it.
Validate, then decide
We ran the MVP against real listings and reviewed the output with people who buy this equipment for a living. The data told us where the idea had legs and where it did not.
The problem
What we walked into
Equipment pricing is fragmented across auction sites, dealer listings, and private sales. No single source tells an operator whether the price they are seeing is fair. The hypothesis was that AI could pull these signals together into one number.
The outcome
What changed
- Validated the core hypothesis with real users instead of speculation
- Identified the specific category and use case worth pursuing further
- Avoided spending months building features the market did not actually want
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