Resale Intelligence
I resell used Apple devices in Mexico City. This is the system I built to run it: a Chrome extension feeds every listing I browse into Postgres, deterministic code turns them into a live price model, and a multi-agent desk decides which deals are worth buying.
real data · last capture Oct 30, 8:27 AMListings tracked
16,739
since Sep 24
Likely sold
4,865
tracked ≥20 h, then gone
Price changes logged
18,459
every re-capture
New in last 7 days
1,709
iPhone, iPad, Mac, audio
- 01
Chrome extension
captures the Marketplace listings I browse
- 02
Ingest API
zod-validated, deduped, price history
- 03
Parser + scoring
deterministic specs, P25/P75, sold detection
- 04
Postgres
listings, price changes, zones
- 05
Agents
LLM judgment on top of the numbers
What used Apple devices really sell for in Mexico City
Bar = middle 50% of asking prices (P25–P75) · tick = median · MXN
- iPhone 17 Pro Max$20,000
- iPhone 16 Pro Max$14,000
- iPhone 16 Pro$12,500
- iPhone 15 Pro Max$10,400
- iPhone 16$9,750
- iPhone 15 Pro$8,900
- iPhone 14 Pro Max$7,500
- iPhone 15$7,000
- iPhone 14 Pro$6,800
- iPhone 14$5,500
Today's flagged deals · real listings
Scored by the CRM against the market. Pick one and let the agents decide.
Or paste any listing
Deal desk · multi-agent
Six steps, two kinds of work. Code and SQL produce every number: the parse, the comparable sales, the margin target and the max price. LLM agents only judge what code can't — ambiguous text, scam signals, and how to word the offer — and their output is schema-validated and clamped to the numbers.