The client was developing an AI-driven product aimed at simplifying product discovery across online marketplaces. The goal was to help users make purchasing decisions without manually browsing listings, comparing specifications or reading dozens of reviews.

The system was designed to operate on top of existing marketplaces, aggregating publicly available data and turning it into clear, actionable recommendations.

Online shopping rarely starts with precise requirements. Most users describe what they want in subjective terms: “a good gaming graphics card,” “a reliable humidifier,” “a comfortable mouse.”

In reality, many people solve this by delegating the task to someone they trust — a friend or family member — who manually researches reviews, prices, delivery times and seller reputation, then suggests one or two solid options.

Marketplaces are optimised for inventory exposure and filtering, not for reasoning and decision-making. They provide data, but leave users to interpret it on their own.

Subjective product discovery introduces several layers of complexity:

• User intent is ambiguous and context-dependent
• Relevant information often lives outside marketplaces (reviews, comparison articles, expert opinions)
• Marketplaces actively restrict automated access
• Reviews and seller data are incomplete or noisy
• Ranking requires balancing multiple factors, not a single metric

Solving this requires more than a simple recommendation model or a conversational interface.

The implementation required addressing several non-trivial challenges:

• Operating under marketplace anti-bot protections
• Simulating mobile traffic patterns to reduce blocking
• Processing large volumes of unstructured text
• Designing ranking logic that remained explainable
• Managing latency across a multi-step agentic pipeline

The system was fully implemented and technically operational across the entire discovery flow.

The solution automated subjective product discovery by combining external knowledge ingestion, LLM-based reasoning and deterministic ranking logic.

The resulting architecture provides a practical foundation for building AI-driven product discovery systems on top of existing marketplaces.