Fleek✦ Second Look

Second Look · v2 roadmap

From “prove it” to trusted grades on both ends of the parcel

Now · 26 Sep 2026

Shipped at the hackathon

  • Listing vs arrival photo scan with defect boxes
  • New / worsened / disclosed damage split
  • Indicative refund + evidence-ready dispute pack
  • Order state ledger

Next · Oct – Dec 2026

Pilot with 20 buyers

  • Blind-test agent grades against buyer, supplier and QC decisions
  • Capture guidance + minimum evidence rules at delivery
  • Link review decisions to real dispute outcomes to calibrate refunds

Later · H1 2027

Close the loop upstream

  • Supplier dispatch scan becomes the baseline
  • Verified exceptions feed supplier quality scores and QC
  • Refund suggestions inside buyer protection, human-approved

Dates are proposed bets, not commitments. Pilot metric: minutes from last photo to evidence-ready pack.

Problem brief · Ayo Ahmed · 26 September 2026

Hackathon concept, not a Fleek product claim

The problem

A wholesale buyer orders to a stated condition grade. When the bundle arrives, some pieces may not resell at the expected price. The evidence is scattered across listing images, arrival photos, order values and support messages. The buyer has to prove the mismatch and then negotiate what it is worth.

“ordered some lululemon leggings A/B grade, most were grade C! Holes, bad stains, bobbling, didn't even bother trying to sell on.”

Trustpilot reviewer, February 2026

“the suppliers [are] really stretching the grading. Ie you order A and B grades and they hide tears and other damage”

Reseller on r/JoinFleek, 7 April 2026 (who also noted refunds were given)

These are individual reports, not a measured failure rate.

Why it matters

Fleek calls itself a wholesale secondhand marketplace connecting resellers and suppliers. It says FleekSort grades, prices and catalogues from a single photo in six seconds, and that trusted grades let buyers source at scale (Fleek). What’s missing is a check at the moment of receipt: can the buyer show, piece by piece, what arrived against what was sold? If that check is slow or disputed, trust and repeat sourcing are at risk. That is our bet, not a proven cause and effect.

The bet: buyer-side verification

  • The buyer uploads an arrival photo next to the listing photo and claimed grade.
  • An image model compares the arrival photo with the listing photo and the disclosed flaws. It flags possible below-grade pieces, explains the visible defects and says how sure it is. Unclear cases go to a person.
  • It puts together an evidence pack and an indicative refund for the buyer to review. Nothing is filed or paid out automatically.

Measure it

Primary: median minutes from the last photo upload to an evidence-ready dispute pack, compared with a pack prepared by hand on the same sample.

Guardrails: false-positive rate after human review · rate of missing or poor photos · gap between proposed and agreed refund · appeals from buyers and suppliers.

We don’t claim a baseline or target yet.

Not today

No payments, supplier-side tooling, automated refunds, binding grade decisions or claims about model accuracy. The demo uses seeded orders and output a person can review. Production use would need consent and a tested grading rubric.

Try the live demo →