Resale AI.

The problem

Resellers of vintage and secondhand goods work almost entirely by hand. Item identification, pricing, and listing creation are time-intensive and fragmented across multiple apps, and pricing uncertainty is the most severe pain point — sellers lack confidence in resale value, leading to underpricing and missed profit.

Existing tools stop short of the real need. Google Lens, Relic, and ChatGPT identify items and give rough valuations; Vendoo and List Perfectly handle cross-listing. But they focus on information retrieval, not decision-making. None help a seller answer the question that matters in the moment: is this item worth buying, and at what price?

The solution

Resale AI (ResaleAI) is an AI sourcing and listing copilot that turns information into decisions. A seller uploads an item image and purchase price and receives a structured BUY/PASS recommendation with confidence, estimated resale range, profit estimate, demand signal, risks, and reasoning.

The same workflow continues into negotiation guidance (opening offer, target, walk-away price), one-click listing generation with style regeneration (SEO Optimized, Collector Focused, Concise, Detailed), and outcome tracking that compares actual sale results against the original estimate. Estimates are always framed as directional, never guarantees, keeping a human in the loop for final decisions.

How it works

Resale AI runs on GPT-4o, chosen for multimodal item understanding, structured reasoning, and text generation. A unified capability-routing prompt directs each request to exactly one workflow — item analysis, negotiation guidance, listing generation, style regeneration, or outcome tracking — so the model never completes the wrong task.

Strong grounding rules require the model to use only provided inputs, with guardrails against fabricating comparable sales, exact sold prices, measurements, provenance, or authentication status. Missing-input handling returns NEEDS MORE INFO rather than guessing, and authentication-risk guardrails apply to luxury items. A 25-row OpenAI eval dataset drove calibration: baseline runs scored 54–68% (mostly wrong-workflow routing), rose to 80% after consolidating into one routing prompt, and reached 100% pass after targeted edge-case fixes.

Who it's for

The product is B2C, built for individual vintage and secondhand resellers — casual side hustlers, part-time vintage sellers, and full-time independent resellers. The most revenue-impacting users are high-frequency, semi-professional sellers who source multiple times per week and manage moderate-to-high inventory; they are both the daily user and the paying subscriber.

A secondary segment includes small resale businesses such as vintage shops and thrift stores managing higher volumes. Revenue is a freemium SaaS subscription: a free tier with limited scans, and paid unlimited usage with advanced pricing insights and bulk tools.

Why it matters

Recommerce is expanding fast: the secondhand apparel market is projected to grow at roughly 15–20% CAGR, outpacing traditional retail, with broader recommerce around 10–15%. Yet the segment remains fragmented and operationally inefficient for sellers working with unstructured, one-of-a-kind inventory — a strong fit for AI-driven efficiency.

Resale AI is at the MVP prototype and evaluation stage, with a validated end-to-end workflow. The launch plan moves through internal QA, a closed pilot with selected resellers, a limited beta or A/B test, and broader release once reliability, support documentation, and legal review are complete. Positioning is deliberately careful: a decision-support copilot, not an authenticator or guaranteed-profit tool.

At a glance

Project

Resale AI

Built by

Chesna Henderson

One-liner

Resale AI is an AI-powered sourcing and listing co-pilot for vintage and secondhand resellers.