AI-native resale operations OS
Every item priced, placed and acted on.
UnivoAgent maintains a digital twin for every one-of-one item in your stores and continuously decides what should happen to it next: accept, price, floor, list, mark down, transfer or source more like it.
Runs alongside Square · Shopify · eBay · label printers
intake / item_928172
decision ready
- 1–5
- locations per account, one shared item graph
- ≤7
- ranked actions delivered each morning
- 4
- photos to a complete, priced item record
- 0
- priced or published records without approval
Platform
One record per item. Every decision attached to it.
Retail systems assume repeated SKUs. Resale is uncertainty: unknown brand, era, condition, demand and channel on every single piece. UnivoAgent resolves that uncertainty and turns it into operations.
Photos, label text, condition observations, comparables, price changes, moves, views and the eventual sale accumulate on one object. Agents read and write it through typed tools, so every recommendation is traceable to evidence and reversible.
01
Identify
Vision plus a label parser that reads style codes, not just brands.
02
Value
Market comparables separated from your store’s own sell-through history.
03
Place
Floor, online or premium review, decided on net value after labor and fees.
04
Act
Markdowns, moves, bundles and buy decisions ranked by expected dollars.
Store Manager
Managers approve a queue, not a dashboard.
A nightly review reads the full portfolio (age, price, sell-through, capacity, online status) and ranks the work by expected impact, confidence, effort and reversibility. Accepted or rejected, each decision trains the next queue.
Monday, 8:10 AM
6 actions · $1,040 expected
Questions
Before you talk to us.
Do we have to replace our POS?+−
No. UnivoAgent installs as an intelligence layer beside Square or Shopify and takes ownership only of item state and availability, so payments, taxes and receipts stay where they are.
Does a language model set our prices?+−
No. Valuation is a traceable model over comparable sales and your own transaction history. Language models extract attributes and explain decisions; they never invent a number.
What happens when the system is unsure?+−
It shows candidate matches with the evidence that separates them, and holds anything high-value for manager review rather than guessing a price.
Who owns the data?+−
You do. Item records, images and sales history are yours and exportable, with role-based access per store and a full audit trail on every mutation.
How long until it pays for itself?+−
The pilot measures it directly. If intake time and underpricing do not move within four weeks, we would rather you not renew.
Bring us a rack of unprocessed inventory.
We process 20–50 items with your team, time every step against how you work today, and show you what was about to be underpriced.
Include your city, store count and current stack.