TreasureLens AI

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  From one photo to done. An autonomous AI agent that detects, appraises, lists, and negotiates — you just approve.

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  Value X-Ray: $4,870 across 9 items in one room, with hidden treasures (lamp, oil painting, grandfather clock) flagged to appraise first.

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  AI drafts the listing, picks the best marketplace, and cross-posts on your approval. Nothing goes live without your OK.

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  Negotiation copilot: a $200 lowball gets a smart $272 counter against your floor — accept, counter, or decline math, not vibes.

🏠 Inspiration

Helping clear out a grandparent's house, we kept hitting the same wall: "Is this worth anything?" We almost donated a lamp worth hundreds. The hard part of downsizing isn't selling — it's knowing what's valuable before it's gone. We wanted to put an AI estate-sale expert in everyone's pocket.

💡 What it does

From a single room photo, TreasureLens AI:

• Detects every item and estimates total room value ($4,870 across 9 items in our sample)
• Value X-Ray — darkens the room and overlays live price tags on every object
• Hidden Treasure detection — flags heirlooms to appraise before selling, so nothing is under-sold
• Liquidation Plan — routes each item to Sell / Bundle / Appraise / Donate / Recycle / Keep
• Auto-drafted listings with title, price, floor, and best-fit marketplace
• Negotiation copilot — accept / counter / decline against your floor price
• Human approval gate on every listing and offer

For downsizers, estate executors, and anyone clearing a garage or attic. The question it answers isn't "how do I list this?" — it's "what is this whole room worth, and what should I do with it?"

🛠️ How we built it

We modeled the agent as a five-stage pipeline, each stage a swappable module behind one shared interface:

Stages 4–5 run for real via a ResaleAgent interface (draftListing() + negotiate()) in src/lib/agent.ts, with a fully offline implementation. Stages 1–3 use prepared outputs so the demo is fast and reliable — a live vision model (GPT-4o, Gemini) and real sold-price data drop in with no UI changes.

Valuations use an explainable model, not a black box:

value = base_range × brand × condition × demand × completeness

Each item ships with a value range and confidence score. Low-confidence items are gated to "Appraise First" instead of guessed at.

Stack: React + Vite + TypeScript + Tailwind + Framer Motion on our "Electric Slate" design system, built with Claude Code as our AI pair-programmer.

Built responsibly: approval-first, privacy-aware image handling, and honest ranges with confidence so heirlooms aren't under-sold.

🧗 Challenges we ran into

• Making a scripted demo feel live — animation timing, the climbing value counter, and the X-Ray reveal all had to land in seconds
• Designing five swappable stages so a real model slots in without a rewrite
• Keeping valuations honest with ranges + confidence instead of fake-precise numbers

🏆 Accomplishments that we're proud of

• End-to-end flow that reads as a real agent: photo → detection → valuation → plan → listings → negotiation
• Working Value X-Ray and real negotiation math (accept / counter / decline)
• Judge-ready interface with a consistent design system

📚 What we learned

• Decision-making was the hard part, not listing creation. The product is stronger as a resale advisor than a listing generator.
• The human-in-the-loop approval gate is a feature, not a limitation.
• Designing swappable stages early makes "sample data now, real model later" a real path, not a promise.

🚀 What's next for TreasureLens AI

• Live vision model + real sold-price data (eBay sold listings) behind the existing five stages
• Real marketplace cross-posting (Facebook, eBay, OfferUp, Poshmark)
• Buyer-message integration with approved negotiation rules and pickup coordination

© 2026 Enrica Garrino. TreasureLens AI — original concept, design, and source code. All rights reserved.

Built With

• claude-code
• react
• typescript
• vite