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  1. 01Zalando · architecture

    Automating Data Ingestion with a RAG Flywheel

    Zalando replaced manual schema mapping for diverse supplier data with an LLM-powered Retrieval-Augmented Generation (RAG) flywheel. The system combines Amazon Titan embeddings stored in PostgreSQL via pgvector with tool-constrained generative models running at zero temperature. Every LLM suggestion passes through strict API validation checks before being reviewed by operations specialists, creating a continuous learning loop that improves future predictions.

    DEPTH ■■■■■14 min read2 weeks ago

    Machine LearningPostgreSQL

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  2. 02Grab · architecture

    Smarter personalization: How property data helps us understand user price sensitivity

    Existing user personalization systems fail to systematically account for residential property values, leading to one-dimensional profiles and regional blind spots. A conceptual four-step method is proposed that combines public property data, large language models, building-level price estimation, and hierarchical clustering to classify user affluence. The approach remains unimplemented, with future viability depending on technical validation, data quality, and privacy approvals.

    DEPTH ■■■■■4 min read3 weeks ago

    Data EngineeringMachine Learning

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  3. 03Grab · architecture

    GrabMart data for faster disease outbreak detection

    The organization proposes a conceptual data pipeline to analyze aggregated GrabMart transaction data for symptom-related product demand, aiming to supplement clinical surveillance for disease outbreaks. The design relies on data aggregation, statistical baseline comparisons, and privacy safeguards like differential privacy without involving individual health inference. The system remains in an early planning phase requiring rigorous validation, privacy review, and public health oversight.

    DEPTH ■■■■■5 min read2 weeks ago

    Data EngineeringMachine Learning

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