Projects using LangChain in Rohtak

Projects using LangChain in RohtakProjects using LangChain in Rohtak

Cover image for Healthcare AI-Based Decision Support System
Architected

Healthcare AI-Based Decision Support System Architected a multi-step LLM-powered clinical decision support platform that transforms patient records and medical guidelines into structured diagnostic workflows. The system combines OCR processing, patient-data ingestion, AI-powered reasoning, and human-in-the-loop validation to improve reliability and consistency of clinical recommendations. Key contributions included: • Designing the LLM decision-engine architecture • Building medical guideline-to-decision-tree workflows • Developing evaluation and review tooling for quality assurance • Integrating OCR and patient-data processing pipelines • Supporting orchestration across multiple AI and healthcare system components Technologies: Python, LLMs, OCR, Workflow Orchestration, Healthcare AI

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Cover image for SmartCart RAG : AI Support

SmartCart RAG : AI Support Assistant on Internal Knowledge Base Built a production RAG chat app over SmartCartCommerce's internal knowledge base - 86 documents, 856 indexed chunks covering policies, support playbooks, seller operations, and the brand admin console. Implemented three distinct AI personas (Customer, Concierge, Brand Partner) each with tailored retrieval and system prompts. Queries are embedded via OpenAI, semantically searched in Pinecone, and streamed back with source citations. Replaced manual doc-hunting for support teams with instant, grounded answers.

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Cover image for AgentOS : Multi-Agent AI Orchestration

AgentOS : Multi-Agent AI Orchestration Platform Built AgentOS, a production-ready multi-agent orchestration platform that enables teams to deploy, monitor, and coordinate AI agents for complex workflows. Designed a modular architecture where specialized agents collaborate - handling tasks like research, code generation, and decision-making in parallel. Implemented real-time agent communication, persistent memory, tool-use pipelines, and a dashboard for observability. The system reduces manual intervention in repetitive workflows by over 80% and scales seamlessly across different LLM providers.

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