Selected work

Representative Papers

Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework

Jun 07, 2026

This work addresses the deployment blind spots commonly encountered in developing production-grade AI customer service agents for hundreds of millions of users, which often stem from fragmented workflows across evaluation, context engineering, training, and online metrics. To bridge this gap, the authors propose the first unified development framework centered on evaluation-driven design. The framework integrates structured context engineering, human-in-the-loop prompt iteration, an LLM-based judging mechanism with consistency guarantees (including GEPA optimization), and an end-to-end validation pipeline, achieving strong alignment between offline metrics and online performance. Evaluated across five real-world customer service scenarios, the approach substantially enhances user experience—evidenced by a 37-percentage-point increase in AI Net Promoter Score and a 29-percentage-point rise in self-service rate in the card delivery scenario—with most scenarios approaching expert human-level performance.

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Your Spending Needs Attention: Modeling Financial Habits with Transformers

Jul 31, 2025

To address the challenges of high-dimensional heterogeneity and costly manual feature engineering in financial user behavior data (e.g., transaction logs, app events, customer service records), this paper proposes nuFormer—the first end-to-end user representation learning framework that jointly encodes merchant textual features (e.g., business names) and structured fields (e.g., amount, timestamp) into a Transformer architecture. Leveraging self-supervised pretraining on raw transaction sequences, nuFormer jointly optimizes user embeddings and conventional handcrafted features without requiring auxiliary data sources. Empirical evaluation on Nubank’s large-scale recommendation system demonstrates substantial improvements in core metrics—including CTR and Recall—while also exhibiting strong generalization performance in risk prediction and fraud detection tasks. These results validate nuFormer’s industrial-grade effectiveness, scalability, and methodological novelty.

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Recent publications

Latest Papers

Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework

Jun 07, 2026

This work addresses the deployment blind spots commonly encountered in developing production-grade AI customer service agents for hundreds of millions of users, which often stem from fragmented workflows across evaluation, context engineering, training, and online metrics. To bridge this gap, the authors propose the first unified development framework centered on evaluation-driven design. The framework integrates structured context engineering, human-in-the-loop prompt iteration, an LLM-based judging mechanism with consistency guarantees (including GEPA optimization), and an end-to-end validation pipeline, achieving strong alignment between offline metrics and online performance. Evaluated across five real-world customer service scenarios, the approach substantially enhances user experience—evidenced by a 37-percentage-point increase in AI Net Promoter Score and a 29-percentage-point rise in self-service rate in the card delivery scenario—with most scenarios approaching expert human-level performance.

0 citationsRead paper

Your Spending Needs Attention: Modeling Financial Habits with Transformers

Jul 31, 2025

To address the challenges of high-dimensional heterogeneity and costly manual feature engineering in financial user behavior data (e.g., transaction logs, app events, customer service records), this paper proposes nuFormer—the first end-to-end user representation learning framework that jointly encodes merchant textual features (e.g., business names) and structured fields (e.g., amount, timestamp) into a Transformer architecture. Leveraging self-supervised pretraining on raw transaction sequences, nuFormer jointly optimizes user embeddings and conventional handcrafted features without requiring auxiliary data sources. Empirical evaluation on Nubank’s large-scale recommendation system demonstrates substantial improvements in core metrics—including CTR and Recall—while also exhibiting strong generalization performance in risk prediction and fraud detection tasks. These results validate nuFormer’s industrial-grade effectiveness, scalability, and methodological novelty.

0 citationsRead paper