Research Paper

Core Innovation

This paper introduces Environment-Aware Search Planning (EASP) with a Probe-then-Plan mechanism that integrates real-time retrieval snapshots into search planning. Unlike prior LLM-based methods that ignore environment dynamics or incur high latency, EASP synthesizes execution-validated plans offline and adapts online serving based on query complexity, optimizing both accuracy and speed.

Why It Matters

E-commerce platforms face challenges in delivering relevant search results quickly due to dynamic inventory and complex user intents. EASP addresses this by grounding search plans in real-time environment data, reducing invalid queries and latency. This leads to better user satisfaction, higher conversion rates, and scalable improvements in search efficiency.

Potential Customers & Pain Points

• E-commerce platforms – Need to improve search relevance and conversion under dynamic inventory
• Online retailers – Struggle with balancing search latency and accuracy
• Search engine providers – Require adaptive planning to handle complex queries efficiently.

Business Model

SaaS or API-based licensing to e-commerce platforms and retailers, with pricing based on query volume and feature tiers including advanced planning and analytics.

Competitive Landscape

• Google Shopping Search
• Amazon A9 Search
• Microsoft Bing Shopping
• Algolia
• Coveo

Implementation Challenges

• Integration complexity with existing e-commerce infrastructure
• Maintaining low latency under high query volumes
• Continuous adaptation to rapidly changing inventory and user behavior

Validation Strategy

• Conduct extended A/B testing on multiple large e-commerce platforms
• Measure improvements in relevant recall
• conversion rate
• and gross merchandise volume
• Iterate planner training with real-world feedback and business outcome alignment