Project Objective:

To develop a Semantic SuperSearch Engine leveraging advanced deep learning methods — specifically Dense Retrieval, Retrieval-Augmented Generation (RAG), and Re-ranking — that can understand and retrieve semantically relevant information from unstructured and structured aftermarket service data. In Phase 2, extend the capability to a multimodal search interface that can process and respond to both textual and visual (image-based) search queries.

Key Deliverables:

Phase 1: Building the Semantic SuperSearch Engine

Data Understanding & Preprocessing
Ingest and normalize diverse data sources: service manuals, maintenance records, support tickets, and parts catalogs.
Extract metadata and perform text cleansing, entity resolution, and standardization.
Dense Retrieval Module
Fine-tune a domain-specific transformer-based bi-encoder (e.g., BioBERT, Med-BERT) for semantic retrieval using contrastive learning.
Build a FAISS or Milvus-based vector index to enable efficient semantic search.
RAG (Retrieval-Augmented Generation)
Integrate a RAG pipeline that fetches top-k documents using the retriever and then generates contextual responses using a fine-tuned decoder model (e.g., T5 or LLaMA).
Re-ranking Layer
Implement a cross-encoder model for re-ranking retrieved documents based on the relevance of query-document pairs.
Evaluate multiple re-ranking strategies using metrics like nDCG, MAP, and Precision@k.

Phase 2: Upgrading to Multimodal Search Interface

Multimodal Query Embedding
Develop a unified embedding strategy to represent both text and image inputs using CLIP or BLIP-style models.
Convert user queries and domain images (e.g., faulty component photos) into joint embeddings in a shared latent space.
Multimodal Preprocessing Algorithm
Build a preprocessor that:
Aligns the visual and textual features using attention-based fusion models.
Reduces modality-specific noise and bridges representation gaps.
Normalizes domain-specific visual data (e.g., different lighting, annotation styles in diagnostic images).
Interface and Use Case Enablement
Build a prototype interface allowing:
Free-text search (“How to replace part X in Model Y”).
Image-based search (uploading a photo of a damaged part to identify replacement instructions).
Hybrid search combining both modalities.
Use case examples:
Technicians searching repair guides by uploading a component image.
Customer support agents finding similar past cases using symptom descriptions and screenshots.

Learning Outcomes for Intern:

Hands-on experience with modern NLP techniques like Dense Passage Retrieval, RAG, and transformer-based re-ranking.
Understanding of multimodal learning and embedding strategies.
Practical exposure to building production-grade semantic search systems.

Exposure to domain-specific challenges in the MedTech aftermarket services space

C5i

Station Overview

Project 1

To develop a Semantic SuperSearch Engine leveraging advanced deep learning methods