We present ConsumerBR, a large-scale corpus of consumer complaints and company responses in Brazilian Portuguese, compiled from publicly available data on the Consumidor.gov.br platform. The corpus comprises over 3.1 million consumer–company interactions collected between 2021 and 2025 and combines anonymized textual content with rich structured metadata, including temporal information, complaint outcomes, and consumer satisfaction indicators. We describe a data collection strategy tailored to the platform’s dynamic interface, a preprocessing pipeline that includes response clustering to identify template-based replies, and a hybrid anonymization approach designed to mitigate privacy risks. We also provide a detailed statistical characterization of the corpus, highlighting its scale, coverage, and distributional properties. ConsumerBR is publicly available for research purposes and supports a wide range of applications, including complaint analysis, sentiment modeling, dialogue and response generation, and preference-based evaluation.

Dense retrieval is a critical component of Retrieval-Augmented Generation (RAG) systems and is highly sensitive to document representations. In consumer complaint settings, raw interaction texts are often lengthy and noisy, which limits retrieval effectiveness. This paper investigates whether schema-guided structured summaries can improve dense retrieval in RAG. We compare embeddings derived from raw interaction texts and from LLM-generated structured summaries in a controlled evaluation on Portuguese-language consumer complaints. Summary-based retrieval achieves a Recall@1 of 0.527, compared to 0.001 when indexing raw interactions, and reaches Recall@10 of 0.610, demonstrating gains of more than two orders of magnitude. These results show that structured summaries enable more effective and reliable retrieval at low cutoffs, making them particularly suitable for RAG pipelines.