[DAN25] (opens in new tab)

Naver recently released the full video archives from its DAN25 conference, highlighting the company’s strategic roadmap for AI agents, Sovereign AI, and digital transformation. The sessions showcase how Naver is moving beyond general AI applications to implement specialized, real-time systems that integrate large language models (LLMs) directly into core services like search, commerce, and content. By open-sourcing these technical insights, Naver demonstrates its progress in building a cohesive AI ecosystem capable of handling massive scale and complex user intent.

Naver PersonA and LLM-Based User Memory

• The "PersonA" project focuses on building a "user memory" by treating fragmented logs across various Naver services as indirect conversations with the user.
• By leveraging LLM reasoning, the system transitions from simple data tracking to a sophisticated AI agent that offers context-aware, real-time suggestions.
• Technical hurdles addressed include the stable implementation of real-time log reflection for a massive user base and the selection of optimal LLM architectures for personalized inference.

Trend Analysis and Search-Optimized Models

• The Place Trend Analysis system utilizes ranking algorithms to distinguish between temporary surges and sustained popularity, providing a balanced view of "hot places."
• LLMs and text mining are employed to move beyond raw data, extracting specific keywords that explain the underlying reasons for a location's trending status.
• To improve search quality, Naver developed search-specific LLMs that outperform general models by using specialized data "recipes" and integrating traditional information retrieval with features like "AI briefing" and "AuthGR" for higher reliability.

Unified Recommendation and Real-Time CRM

• Naver Webtoon and Series replaced fragmented recommendation and CRM (Customer Relationship Management) models with a single, unified framework to ensure data consistency.
• The architecture shifted from batch-based processing to a real-time, API-based serving system to reduce management complexity and improve the immediacy of personalized user experiences.
• This transition focuses on maintaining a seamless UX by synchronizing different ML models under a unified serving logic.

Scalable Log Pipelines and Infrastructure Stability

• The "Logiss" pipeline manages up to tens of billions of logs daily, utilizing a Storm and Kafka environment to ensure high availability and performance.
• Engineers implemented a multi-topology approach to allow for seamless, non-disruptive deployments even under heavy loads.
• Intelligent features such as "peak-shaving" (distributing peak traffic to off-peak hours), priority-based processing during failures, and efficient data sampling help balance cost, performance, and stability.

These sessions provide a practical blueprint for organizations aiming to scale LLM-driven services while maintaining infrastructure integrity. For developers and system architects, Naver’s transition toward unified ML frameworks and specialized, real-time data pipelines offers a proven model for moving AI from experimental phases into high-traffic production environments.