Recall Radar: Instantly Detect Product Recalls with Algolia Agent Studio

#devchallenge #algoliachallenge #ai #agents

# Submission for the Algolia Agent Studio Challenge

Consumer-Facing Non-Conversational Experiences

This is a submission for the Algolia Agent Studio Challenge.

🚨 What I Built

Recall Radar is a consumer-facing Safety Agent designed to instantly detect whether a product has been officially recalled.

Instead of relying on slow, conversational AI flows, Recall Radar adopts a non-conversational, retrieval-first approach. It delivers an immediate, authoritative Safe / Not Safe verdict based on official recall databases.

The experience is designed for high-stress scenarios, such as parents checking a product’s safety. There are no chats, no forms, and no ambiguity. Just instant feedback powered by fast, contextual retrieval.

Core idea:

Search is not just a lookup tool here. It is the decision engine.

🎯 Key Capabilities

Instant recall detection for product names, brands, and models
Clear, color-coded safety verdicts
Typo-tolerant fuzzy matching for real-world input errors
Zero backend logic. Fully search-driven intelligence
Professional, “government-style” UX for trust and clarity

🧪 Demo

Live Application

https://algoliachallenge-project-recall-rad.vercel.app
Source Code (GitHub)

https://github.com/AsamaeS/algoliachallenge_project_-Recall-Radar

🔍 Safety Verdicts in Action

🔴 Match Found	🟠 Possible Match	🟢 No Records
Recalled product detected with high confidence	Close or typo-based match requiring verification	No official recall found

Screenshots and demo video are available below.

🎥 Video Demo

A short walkthrough demonstrating how Recall Radar reacts in real time to exact matches, fuzzy matches, and clean searches:

https://drive.google.com/drive/folders/1M0exKMaHXqKDBpFyXCtxXi2X28TT9Dbl

🧠 How I Used Algolia Agent Studio

Algolia is not used as a passive search layer, but as the core intelligence system.

Data Indexing

I indexed structured consumer safety recall data inspired by official standards (RAPEX / DGCCRF), including:

Product name
Brand
Model / reference
Risk severity
Source authority

Retrieval-Driven Logic

This is a non-conversational agent, so “prompting” happens through retrieval metadata, not text generation.

Algolia’s ranking signals drive the agent’s behavior:

Exact Match (RED)
- Zero typos on critical attributes
- High-confidence recall detected
- Immediate high-risk alert
Fuzzy Match (ORANGE)
- Typo tolerance or partial similarity detected
- User warned to verify details
No Match (GREEN)
- No relevant recall found
- Reassuring confirmation with official disclaimer

This creates logical branching directly in the UI, without any backend decision rules.

⚡ Why Fast Retrieval Matters

In safety-critical workflows, latency destroys trust.

If a user has to wait for an AI to “think,” they may not check at all.

Algolia’s fast, contextual retrieval enables:

Instant Verdicts

The safety status updates live as the user types.
Stress-Proof Input Handling

Typos like “TohyWorld” still trigger critical warnings.
Perceived Authority & Reliability

Speed + consistency creates a professional, official feel.

Fast retrieval turns search into a protective mechanism, not just an interface.