Large Language Model (LLM) based conversational systems generate vast interaction logs containing critical diagnostic signals such as failure modes and safety violations. However, the cross-turn dependencies, implicit state, and topic drift inherent to these conversations defeat dense retrieval, whose lone embedding cannot concurrently encode discrete structured constraints (e.g., refusal type) and open-ended semantic content. Our central claim is conceptual: conversational retrieval must span two complementary information planes, structured metadata and semantic content, and these are best reconciled by a unified hybrid representation that jointly supports filtering over structured metadata fields and vector similarity search over unbounded open-ended fields. We instantiate this as the Extract-Annotate-Query (EAQ) framework, which derives a metadata taxonomy, applies LLM-driven annotation, and compiles natural-language queries into hybrid graph operations. On the CDR-Benchmark, EAQ lifts NDCG@10 by 30.8% over the strongest dense baseline, with consistent gains across five analytical categories and two external datasets.

Many real-world decisions about products (e.g. how they function, how they should be used) depend on their components rather than the object as a whole. Accurately identifying product component has applications like automated defect detection, visual spare-parts search, and verified assembly. However, existing object detectors treat components as isolated objects, ignoring their inherent structure. We propose Know What You See (KWYS), where we localize components by grounding them using a textual knowledge base (e.g., manuals or web descriptions). KWYS converts raw text into a hierarchical component taxonomy, which then guides an open-vocabulary object detector using a hierarchical verification algorithm. We evaluate on 1,000 product images across 5 diverse categories, improving component localization accuracy by 11% along with reducing component hallucinations by 25%.

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.