In e-commerce logistics, accurate geospatial clustering is essential for optimizing resource allocation, manpower planning, and delivery network design. However, existing density-based clustering approaches, particularly their reliance on heuristic parameter tuning, have been underexplored in datasets with significant density variations, limiting robustness and scalability. This study presents an unsupervised framework that extends DBSCAN by leveraging Gaussian Mixture Models (GMM). First, we propose a method that systematically identifies suitable clustering scales through statistical modeling. Second, the approach iteratively applies DBSCAN to extract clusters from dense to sparse regions, overcoming single-parameter limitations. Finally, we validate the method through large-scale offline experiments using data from over 200 last-mile dispatch centers (LMDC). The results demonstrate the framework’s effectiveness in identifying heterogeneous geographic demand patterns and supporting workforce planning and operational benchmarking. This framework provides a scalable solution to a critical challenge in e-commerce logistics, offering a valuable reference for strategic and operational decision-making.

Abuse detection in e-commerce platforms is critical for preventing operational losses, particularly for transaction types vulnerable to abuse such as Return-to-Origin (RTO) in Cash-on-Delivery (COD) workflows. Detecting such abuse accurate, real-time decisions to intercept malicious orders before placement, imposing stringent sub-second latency requirements on deployed systems. In this work, we present TRUST, a deployed, production-scale abuse detection system based on a unified architecture of heterogeneous Graph Neural Networks (GNNs) and Transformer-based sequence encoders. This design enables joint reasoning over multi-relational entity interactions and temporal behavioural signals, allowing the model to combine complementary information for effective abuse detection when either modality is sparse or absent. TRUST processes millions of transactions daily with an average inference latency of ~25 ms, achieving a ~9.6% absolute precision improvement over a strong XGBoost baseline in live RTO detection. We report systematic ablation studies across both graph and sequence stages, evaluating GNN variants, sampling strategies, sequence lengths, and positional encoding schemes to guide architectural choices. Deployed end-to-end in a high-throughput environment, TRUST demonstrates that GNN–Transformer cascades can deliver state-of-the-art accuracy, scalability, and operational reliability in real-world abuse detection, offering a reproducible blueprint for similar industry-scale applications.

Geocoding in India presents unique challenges due to the unstructured, multilingual and diverse nature of its address systems. While recent advances in geospatial AI have explored the combination of spatial and semantic cues, existing methods often fall short in effectively integrating both dimensions for robust address resolution. In this work, we propose GeoIndia-V2, an enhanced version of GeoIndia [21], that unifies geospatial and semantic modeling through a novel fusion framework. Our unified model combines the Graphormer architecture [27] and a Pre-trained Transformer based Language Model (PTLM) that is trained from scratch on proprietary Indian address data, using our proposed Key Modulated Cross-Attention (KMCA) mechanism. KMCA enables deep cross-modal interaction between geospatial topology and linguistic structure and allows the model to reason contextually across both geographic and textual dimention, effectively handling the semantic intricacies of Indian addresses-including colloquial usage, inconsistent formatting, and multilinguality. We leverage last-mile e-commerce delivery data to construct a fine-grained graph of neighbourhood connectivity, enabling Graphormer to capture rich spatial relationships. Unlike prior methods that rely on self-loops, we generate graphs dynamically at inference time to exploit Graphormer's topological strength. Additionally, we introduce a generative decoding strategy for predicting hierarchical H3 cells. https://www.uber.com/en-IN/blog/h3/, moving beyond conventional bit-wise classification approaches. To the best of our knowledge, this is the first method to explicitly fuse graph-based geospatial learning with language-driven semantic modeling via cross-attention in the Indian geocoding context. Our approach significantly outperforms existing solutions and marks a substantial advancement toward building scalable real-world geocoding systems for complex address ecosystems like India.