Every SafeShelf product, working. In the order a shopper meets them.

Product recalls are published as English web pages on regulators' sites, one country at a time. A power bank recalled in the US for fire can still be listed on an Indian or Australian marketplace, under the same model number or a new brand name. SafeShelf checks a listing at the point of purchase and answers two questions with evidence:

A shelf is where you keep what you bought; SafeShelf watches it before and after you buy. Eddy is the guide who talks you through the answer, in chat, by voice or on WhatsApp. Eddy explains; code decides.

India, Japan, Australia, Singapore and Southeast Asia buy the same mass-market electronics as the US, but most published recall data is American: India has 36 notices on file against 2,537 from the US. SafeShelf loads 8,689 records from 10 regulators and carries a US recall across the border as "recalled abroad, same model", never as "recalled here".

A chatbot can invent a recall or miss one. In SafeShelf, Gemini reads, searches and explains, but plain code sets the colour from the evidence, and only an exact model match inherits a recall. When the evidence is thin it says "Can't tell yet".

• Online shoppersbuying cheap electronics where recall notices are rare or in another language
• Parentsbuying chargers, heaters and children's products
• Migrants and cross-border familiesordering for relatives back home
• Visually impaired and low-literacy shoppersthrough large text and the spoken "Ask out loud" mode

On 5 May 2026 a power bank ignited in a seat pocket after IndiGo flight 6E-108 landed at Chandigarh; about 200 passengers were evacuated and at least 6 were hurt. No brand or model was named.The Times of India, citing the DGCA

Lithium batteries, heaters and children's products cause harm long before a regulator acts. In our early work, a product recalled in one country was recalled in a second a median of 22 days later, and up to 293 days later.

It answers where people already shop and talk: on the listing (Chrome extension), on WhatsApp (text and voice notes), in an Android app and on the web. Answers can be read aloud in 26 Asia-Pacific languages, and it works with screen readers and the keyboard. See step 2.

My Shelf keeps watching saved products and shows when a status changes. If something goes wrong, SafeShelf gives safe steps first, then a refund request and a complaint to the right regulator (India NCH 1915, ACCC, Japan's CAA and others), already filled in with the official record. It drafts them; it never sends anything on the shopper's behalf.

Yes. The same engine scans a marketplace catalog and says what to do with each finding (stop selling, quarantine, notify buyers): on 500 test listings it found 49 of 50 planted problems with no wrong findings. Regulators get an evidence pack of products recalled abroad but still sold locally, with a case file per finding. See step 5.

1. ReadBrand, model, ratings and safety marks, from text and photos
2. MatchThe identity engine finds every copy under other names, so their reviews pool
3. CheckEight agents: recalls, review hazards, safety marks, price, photos, the shop
4. VerifyIs the source official? This exact model, batch and country? Copies count once
5. DecideCode sets red, amber, green or grey; Gemini explains it with a source per line

A live check takes a median of 6.6 seconds.

Regulators' own feeds and pages, plus 29,379 real buyer reviews and an 8,470-record Australian energy registry. Sources that block automated access are shown as "couldn't be checked", never as clear.

• CPSC · US
• ACCC · Australia
• KATS · Korea
• CAA · Japan
• CCPA, BIS · India
• BSMI · Taiwan
• CPSO · Singapore
• ASEAN alerts
• OECD GlobalRecalls

Yes. Ten read-only tools over MCP, a streaming REST API and an A2A agent card, so an agent buying on someone's behalf can ask the same question. See step 6 or the API docs.

Team Hashes, two builders in a week for the AI Builder Cup 2026, Retail & Commerce: Solomon, the brain (engine, data, API, MCP, WhatsApp, benchmarks) and Seshu, the face (extension, ADK agents, web, voice). All the code is on GitHub.

On 25 real US recalls and 40 never-recalled products, reading only reviews written before each recall, SafeShelf flagged 6 recalls with 2 false alarms. Keyword rules flagged more (11) but raised 11 false alarms. On the recalls we did not tune on, it caught 2 of 9 with no false alarms. The sample is small, and every number is in Results.

We attacked it ourselves, with Gemini writing the attacks: fake regulator pages, look-alike model numbers, hidden instructions aimed at AI tools. The red team found two real holes, which we fixed: lightly reworded reposts, and fake-shop notices "for AI shopping assistants" (now 13 of 13 caught, no false alarms on 27,822 real listings). See step 7.

No. Green means no problems were found in what was checked, and every answer lists what was checked and what could not be. Verdicts are evidence for shoppers, not legal findings; AI findings marked "under review" are not regulatory determinations.

Real: recalls, reviews and registries. Fictional: Kelvo, SafeVolt, VoltNova, Zorvana and MegaDealz24, so the demo can show every answer without accusing a real seller. Cross-border "still on sale" matches are leads a person has not yet confirmed, so we do not count them as findings.

Shoppers use it free. Marketplaces, insurers, payment providers and regulators would pay for catalog scans, purchase risk flags and the recall-check API. Tenant keys, webhooks and usage metering are already built.