Large Language Models (LLMs) enable natural language to SQL conversion, allowing users to query databases without SQL expertise. However, generating accurate, efficient queries is challenging due to ambiguous intent, domain knowledge requirements, and database constraints. Extensive reasoning improves SQL quality but increases computational costs and latency. We propose SQLGenie, a practical system for reliable SQL generation. It consists of three components: (1) Table Onboarder, which analyzes new tables, optimizes indexing, partitions data, identifies foreign key relationships, and stores schema details for SQL generation; (2) SQL Generator, an LLM-based system producing accurate SQL; and (3) Feedback Augmentation, which filters correct query-SQL pairs, leverages multiple LLM agents for complex SQL, and stores verified examples. SQLGenie achieves state-of-the-art performance on public benchmarks (92.8% execution accuracy on WikiSQL, 82.1% of Spider, 73.8% on BIRD) and internal datasets, surpassing the best single-LLM baseline by 21.5% and the strongest pipeline competitor by 5.3%. Its hybrid variant optimally balances accuracy and efficiency, reducing generation time by 64% compared to traditional multi-LLM approaches while maintaining competitive accuracy.

Businesses can benefit from customer feedback in different modalities, such as text and images, to enhance their products and services. However, it is difficult to extract actionable and relevant pairs of text segments and images from customer feedback in a single pass. In this paper, we propose a novel multi-modal method that fuses image and text information in a latent space and decodes it to extract the relevant feedback segments using an image-text grounded text decoder. We also introduce a weakly-supervised data generation technique that produces training data for this task. We evaluate our model on unseen data and demonstrate that it can effectively mine actionable insights from multi-modal customer feedback, outperforming the existing baselines by 14 points in F1 score.