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Trashu Vashisth's avatar
Trashu Vashisth
Delhi, India
Building Production-Grade AI Agents & RAG Systems
11
Followers
Building Production-Grade AI Agents & RAG Systems
Cover image for The Problem: Sales teams waste 60%
The Problem: Sales teams waste 60% of their time researching leads instead of closing them. The Solution: I built a custom Agentic AI Pipeline that automates deep-dive business intelligence and lead scoring. Key Technical Highlights: Multi-Agent Architecture: Built using CrewAI, featuring a 'Business Intelligence Specialist' (for real-time research) and a 'Senior Sales Director' (for strategic scoring). High-Speed Intelligence: Powered by Llama 3.3-70B  for near-instant reasoning and decision-making. Real-time Web Scoping: Integrated Tavily AI to fetch live revenue data, employee counts, and market positioning. Enterprise Storage: A robust SQLite backend to manage lead pipelines with a sleek Streamlit dashboard. Smart Throttling: Engineered custom rate-limiting and token-trimming logic to ensure 99.9% uptime even under heavy API constraints. How it works: Simply enter a company name and URL. The AI agents scour the web, analyze the company's "AI potential," calculate a priority score (0-100), and even write a personalized sales pitch—all in under 30 seconds.
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Cover image for I built a professional, end-to-end
I built a professional, end-to-end AI Receptionist system designed to automate clinic appointment management. This isn't just a chatbot; it's an AI Agent that can reason, use tools, and manage a live database autonomously. Key Contributions: Agentic Reasoning: Integrated CrewAI with Llama 3.3 (Groq) to enable the agent to understand complex user intents (Booking vs. Cancellation) and relative time (e.g., "next Tuesday at 3pm"). Autonomous Tool Use: Developed custom Python tools that allow the agent to verify real-time availability in a SQLite database and execute atomic transactions without human intervention. High-Performance Backend: Built a robust API using FastAPI to handle asynchronous requests between the AI agent and the database. Premium Dashboard: Designed a modern, Glassmorphic UI using Tailwind CSS that provides a real-time sync of the clinic’s schedule. The Result: A seamless, hands-free system that reduces administrative overhead by 100%, allowing clinic staff to focus on patients while the AI handles the entire scheduling lifecycle. Tech Stack: Python, CrewAI, Groq API, FastAPI, SQLite, Tailwind CSS
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Cover image for Developed a production-grade Retrieval-Augmented Generation
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
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Cover image for Built an intelligent RAG-based chatbot
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
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Python(7)
Manjeet Hooda's avatar
Manjeet Hooda
Delhi, India
Turning complex data into clear, actionable insights.
Turning complex data into clear, actionable insights.
Cover image for Sales Performance Analysis and Prediction
Sales Performance Analysis and Prediction
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Cover image for Customer Churn Analysis and Prediction
Customer Churn Analysis and Prediction
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Cover image for Shipment Price Prediction
Shipment Price Prediction
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Python(3)
Ajay Bidyarthy's avatar
Ajay Bidyarthy
pro
Delhi, India
Python, Machine Learning, Chatbot, SaaS & Automation Expert
New to Contra
Python, Machine Learning, Chatbot, SaaS & Automation Expert
Cover image for I’ve worked on building graph
I’ve worked on building graph database solutions to handle complex, highly connected data that traditional relational databases struggle with. The main goal was to model relationships more intuitively and enable faster, more meaningful queries—especially for use cases like recommendations, fraud detection, and network analysis. Tech Stack ✔Graph Databases: Neo4j ✔Query Language: Cypher ✔Backend: Python (FastAPI / Flask) ✔Data Processing: Pandas ✔Visualization: Neo4j Bloom / D3.js ✔Cloud & Deployment: AWS / Docker
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Cover image for Expert in Full Stack AI-Powered Application Development
Hello, I work across the full stack—designing and developing AI-powered applications from the ground up. This includes everything from user-facing interfaces to backend systems, AI integration, and deployment. My focus is not just on building models, but on delivering complete, usable products. Over time, I’ve worked on: AI-powered chatbots and conversational systems Generative AI applications using LLMs Data-driven automation workflows AI-enhanced search and recommendation systems
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Cover image for Chatbot and AI App Development
Chatbot and AI App Development
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Cover image for I develop and manage databases
I develop and manage databases using Elasticsearch, Neo4j, and Supabase to deliver fast search, powerful data relationships, and scalable backends. I design efficient data models, optimize performance, and ensure secure, reliable data access. I handle everything from setup and integration to deployment and ongoing optimization.
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Python(12)
ANIMESH SINGH's avatar
ANIMESH SINGH
Delhi, India
Data & AI Engineer
Data & AI Engineer
Cover image for Problem: Many organizations still process invoices
Problem: Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals. Solution: This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.
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Cover image for Problem Statement Urban traffic management systems
Problem Statement Urban traffic management systems lack real-time, integrated data combining traffic conditions with weather patterns. This results in poor routing decisions, delayed emergency responses, and inefficient traffic flow management. Solution Developed a comprehensive real-time ETL pipeline that integrates traffic APIs and weather data sources, processes millions of data points, and delivers actionable insights through interactive dashboards for traffic management and route optimization.
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Cover image for A fully local RAG pipeline
A fully local RAG pipeline that transforms your PDFs into a queryable knowledge base using FAISS vector search and Ollama LLMs. No cloud, no API keys - just private, grounded document intelligence running entirely on your machine.
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Cover image for A fully Dockerized real-time IoT
A fully Dockerized real-time IoT ETL pipeline that simulates device telemetry, processes events through MQTT and Kafka, orchestrates workflows with Airflow, and delivers real-time alerts and insights to CRM systems with monitoring via Grafana and Loki.
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Python(4)
Vaibhav Mishra's avatar
Vaibhav Mishra
Faridabad, India
A Professional Full Stack Developer
A Professional Full Stack Developer
Cover image for Rice Grain Species Classification using Machine Learning
Rice Grain Species Classification using Machine Learning
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Cover image for Face Recognition and Verification System Development
Face Recognition and Verification System Development
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Cover image for OCR Python Script Development
OCR Python Script Development
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Python(2)