Trashu Vashisth
Building Production-Grade AI Agents & RAG Systems
New to Contra
Trashu is ready for their next project!
Developed a production-grade Retrieval-Augmented Generation (RAG) system specifically designed to automate the analysis of complex Environmental, Social, and Governance (ESG) reports. This tool bridges the gap between static LLMs and the dynamic, data-heavy requirements of legal and sustainability compliance. [1 (https://www.youtube.com/watch?v=wkYPcMtwlN8)]
Key Features & Capabilities
Intelligent Document Processing: Automatically handles large, unstructured PDF/Word ESG reports, extracting critical clauses and metrics in seconds.
Fact-Grounded Q&A: Uses a RAG architecture to ensure all answers are strictly based on the uploaded documents, virtually eliminating AI hallucinations.
Compliance Mapping: Cross-references internal company data with global frameworks like CSRD, GRI, and TCFD to identify gaps or inconsistencies.
Audit-Ready Traceability: Every insight generated includes direct citations and excerpts from the source files, providing a clear "paper trail" for legal teams.
Automated Drafting: Capability to draft legal summaries, notices, or internal policy updates based on analyzed ESG risks
Note: The 'Slaughter and May' branding in the sidebar is for UI/UX demonstration purposes only, showcasing how the tool integrates into a top-tier law firm's environment.
#AI #RAG #LegalTech #ESG #Python #LangChain
1
36
Developed a full-stack RAG-based E- Commerce AI chatbot using React.js and Tailwind CSS that suggests the perfect laptop from a live catalog. Integrated ChromaDB with BGE Embedding models to provide highly accurate, context-aware product recommendations and instant technical support."
Key Highlights:
Smart Laptop Recommendations: Uses Semantic Search to match user needs (gaming, coding, etc.) with real-time specs.
Advanced Tech Stack: Powered by LangChain for orchestration and BGE models for superior data retrieval.
Modern UI/UX: Built a responsive, clean interface using React.js and Tailwind CSS.
Zero Hallucination: Ensures all suggestions are strictly grounded in the available product inventory.
2
1
147
Built a highly scalable Retrieval-Augmented Generation (RAG) chatbot designed to interact with private datasets/PDFs. Unlike standard LLMs, this system minimizes hallucinations by retrieving real-time context from a local knowledge base before generating responses.
Key Features:
Semantic Search: Implemented Vector Embeddings to perform high-speed similarity searches across thousands of document chunks.
Smart Retrieval: Integrated a retrieval pipeline using LangChain to fetch the most relevant context for user queries.
Source Citation: Configured the bot to provide source references from documents, ensuring data transparency and accuracy.
Optimized Performance: Used FAISS/Chromadb for efficient vector storage and retrieval.
4
222
Developed a high-precision Resume Parser using a custom-trained RoBERTa model, specifically fine-tuned for Named Entity Recognition (NER) tasks. This tool automates the extraction of critical information from unstructured resumes with deep learning accuracy.
Key Features:
NER-Based Extraction: Accurately identifies entities like Name, Experience, Skills, Education, and Contact Info.
RoBERTa Architecture: Leverages Transformer-based embeddings for superior contextual understanding compared to traditional parsers.
JSON Output: Seamlessly converts complex resume layouts into structured JSON format for easy database integration and ATS (Applicant Tracking System) workflows.
High Accuracy: Trained to handle diverse formatting and professional jargon.
3
190
Developed an automated pipeline to handle bulk recruitment workflows. This AI-powered tool processes multiple PDF resumes simultaneously, using advanced parsing to extract candidate details with high accuracy. It intelligently structures unstructured resume data into a clean, downloadable CSV format, capturing essential metrics like skills, experience, and contact info without manual data entry.
4
268
Built an intelligent RAG-based chatbot designed to simplify complex financial analysis. In the project demo, the AI deep-dives into Apple’s annual reports, extracting key fiscal metrics and providing real-time insights through natural language queries. It transforms dense financial filings into actionable data using advanced document retrieval
2
3
256