Freelancers using LangChain in IndiaFreelancers using LangChain in India
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Anmol Baranwal's avatar
Anmol Baranwal
India
Technical Writer (1.5M+ reads) & Open Source Developer
$10k+
Earned
23x
Hired
53
Followers
BUILDSHIP_EXPERT

expert

HIRED_BY_YC_EXPERT

Hired

Technical Writer (1.5M+ reads) & Open Source Developer
Cover image for Technical Writing for Copilotkit | 40k+ views
Technical Writing for Copilotkit | 40k+ views
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79
Cover image for Technical Writing for Encore | 30k+ views
Technical Writing for Encore | 30k+ views
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15
Cover image for Technical Writing for Encore | 30k+ views
Technical Writing for Encore | 30k+ views
0
6
Cover image for Technical Writing for Opire | 60k+ Views
Technical Writing for Opire | 60k+ Views
0
19
LangChain(1)
Trashu Vashisth's avatar
Trashu Vashisth
Delhi, India
Building Production-Grade AI Agents & RAG Systems
14
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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36
Cover image for Developed a full-stack RAG-based E- Commerce
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.
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Cover image for Built a highly scalable Retrieval-Augmented Generation
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.
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295
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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3
297
LangChain(5)
Anurag Nagare's avatar
Anurag Nagare
Mumbai, India
I’m an AI & Machine Learning engineer with expertise in deve
I’m an AI & Machine Learning engineer with expertise in deve
Cover image for Most AI research tools are
Most AI research tools are just a chatbot with a search button. I built something different. Every time you ask an AI to research something, you're getting one model, one pass, no quality check. It writes confidently, cites poorly, and you have no idea if what it produced is actually accurate. For anyone making real decisions from AI-generated research, that's a silent risk most people ignore. The problem gets worse at scale the longer and more complex the question, the more a single model hallucinates, misses sources, and loses structure. There's no one checking its work. So I built ResearchOS a 5-agent pipeline where each agent has one job. A Supervisor breaks down your question. A Researcher runs parallel searches across 22+ sources. An Analyst extracts data and auto-generates charts. A Writer synthesises a cited report. A Critic fact-checks it and sends it back for revision if anything is wrong. The loop runs up to 3 times before the report is approved. One question in. A full cited report with charts and PDF export in under 10 minutes. I tested it live by watching the Critic catch a missing citation mid-run and send the Writer back to fix it before approval. That's the part that makes this actually usable for real work. Built on LangGraph, Groq, Tavily, ChromaDB and runs entirely on free tiers.
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71
Cover image for HybridAlpha (Hybrid RAG) : One tool
HybridAlpha (Hybrid RAG) : One tool digs into actual SEC filings, not just static documents. From EDGAR, it grabs 10, Ks and 10, Qs fresh each time. Sections like MD&A or Risk Factors get split out by name during parsing. Storage happens two ways at once: words go to ChromaDB, numbers land in SQLite. When a question arrives, the router decides, tone, driven, number, heavy, or both. Depending on that choice, the query moves to one place, sometimes both. Context flows forward only after sorting is done. Answers come from Llama 3.3 70B via Groq, always tagged with sources. Each output ties back to where the data lived. Start by asking, What risks did Apple highlight regarding AI rivals? Out comes exact quotes pulled straight from official documents.
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Cover image for I created WealthWise Agent, a smart personal finance planner...
I created WealthWise Agent, a smart personal finance planner designed to craft personalized budget plans and investment strategies. This app takes into account user inputs like salary, expenses, and financial goals, and then uses a Large Language Model (Gemini) to analyze these factors based on the 50/30/20 budgeting rule. It offers a clear step-by-step reasoning log, a detailed JSON-structured financial plan, and an interactive visualization of budget allocation, empowering users to make informed choices to reach their financial goals. 💻 Tech Stack Used: Frontend/UI: Gradio (custom themed with CSS, Orbitron font) AI/Logic: Google Gemini (gemini-1.5-flash) with LangChain agents Data: yFinance API for real-time stock/ETF data, Pandas & NumPy for calculations Visualization: Plotly Express for interactive charts
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Cover image for Everyone's talking about quantum computing.
Everyone's talking about quantum computing. Nobody's using it to feed farmers. India loses 20–30% of its crop yield every year to diseases and pests. Not because farmers don't care — but because early detection is hard, expensive, and inaccessible to the people who need it most. The existing solutions? Either a basic image classifier trained on lab-perfect photos that fail in real field conditions, or an agronomist visit that costs time and money most small farmers don't have. So I built QuantumEdge AgriGuard — a hybrid Quantum Neural Network app where a farmer can photograph a diseased leaf on their phone and get an instant diagnosis in under 5 seconds. Here's what makes it different from just another plant disease detector: Instead of a pure classical CNN, I built a hybrid architecture — a ResNet/EfficientNet backbone extracts visual features, then passes them into a Variational Quantum Circuit (VQC) for the final classification. The quantum layer uses angle embedding + StronglyEntanglingLayers, which gives it a measurable edge on small, noisy datasets — exactly the kind of data you get from Indian field conditions. The app doesn't just tell you what disease it is. It gives you: → Confidence score → Organic + chemical remedies (India-specific) → Yield impact estimate → A live classical vs quantum accuracy comparison so you can see the difference yourself I tested the quantum advantage claim honestly — ran both models on the same downsampled PlantVillage dataset and tracked accuracy, F1-score, and inference time side by side. The results are on the dashboard. No hand-waving. Built with PennyLane + PyTorch + Plotly Dash. Designed to run on simulators today and on QpiAI-Indus 25-qubit hardware tomorrow.
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LangChain(3)
Mehul Sethia | Senseibles's avatar
Mehul Sethia | Senseibles
proIndia
Design, Build, AI, Automation
$50k+
Earned
17x
Hired
5.0
Rating
59
Followers
Design, Build, AI, Automation
Cover image for DepX: AI-Powered DevOps Copilot
DepX: AI-Powered DevOps Copilot
2
78
Cover image for DailyCue — AI-Powered Decision Support
DailyCue — AI-Powered Decision Support for Recruitment Teams URL: https://dailycue.tech One-liner Shipped a recruitment SaaS MVP in 3 days — AI that tells recruiters exactly who to call today, and why. Project Overview Most recruitment teams don't have a lead problem. They have a prioritisation problem. CRMs are full, but consultants still rely on gut feel to decide where to focus each day — which means high-value opportunities get buried and revenue stays unpredictable. DailyCue connects to a recruitment CRM and uses AI to surface daily, prioritised actions for each consultant: who to contact, what the context is, and why now. It replaces the daily morning scramble with a clear, data-driven cue. What I Built CRM data ingestion pipeline with normalisation and deduplication AI prompt layer (GPT-4) generating personalised daily action lists per consultant Tiered Stripe billing in GBP with seat-based pricing JobAdder marketplace OAuth integration for one-click CRM connection Serverless architecture on Vercel with Inngest for background job processing Landing page, onboarding flow, and trial signup How I Shipped It Initial version went live in 3 days. I then iterated directly with real recruitment users, refining the AI prompts based on actual usage feedback until the output matched how experienced consultants naturally think about their pipeline. The product continued to evolve through multiple rounds of user testing and positioning pivots. Tech Stack Next.js · Supabase · PostgreSQL · OpenAI API · Stripe · Inngest · Vercel · JobAdder API
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Cover image for Converc — Turn Website Visitors
Converc — Turn Website Visitors Into Live Conversations URL: https://converc.com One-liner Built a real-time browser-based calling platform from scratch — MVP live in 5 days. Project Overview Converc solves a problem that costs B2B companies revenue every day: high-intent visitors land on your site, can't get a human immediately, and leave. It embeds a call widget that connects visitors to sales reps in real time — no booking links, no forms, no friction. I architected and built the full product: a Next.js frontend, Supabase backend with Row Level Security, and WebRTC-powered peer-to-peer audio calling that works entirely in the browser with no native app required. The stack was chosen for performance, low latency, and the ability to ship fast without sacrificing production quality. What I Built WebRTC call engine with real-time signalling via Supabase Realtime Embeddable call widget (drop-in script for any website) Agent dashboard with live call status, queue management, and session history Webhook integration layer for CRM and Slack notifications on call events OAuth-based authentication with Google and dev/prod environment separation Supabase RLS policies enforcing strict data isolation between workspaces How I Shipped It The core MVP — widget, signalling, agent dashboard, and working calls — was shipped in 5 days using Cursor with Claude as the AI pair programmer. Post-MVP work covered Google Safe Browsing clearance, Slack App submission, analytics scoping, and hardening the auth and security model for production. Tech Stack Next.js · Supabase · PostgreSQL · WebRTC · Vercel · Tailwind CSS
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49
Cover image for eSIM Platform Development for Alosim
eSIM Platform Development for Alosim
0
8
LangChain(1)
Wahid Ali's avatar
Wahid Ali
proGhaziabad, India
Full Stack Developer | AI Tool, Web App, MVP & SaaS Products
$10k+
Earned
4x
Hired
4.9
Rating
21
Followers
Full Stack Developer | AI Tool, Web App, MVP & SaaS Products
Cover image for AI Search Optimization Platform
AI Search Optimization Platform
1
14
Cover image for Flight Booking Platform
Flight Booking Platform
1
13
Cover image for Innovation Intelligence Platform
Innovation Intelligence Platform
1
14
Cover image for Digital Business Card Platform
Digital Business Card Platform
1
10
LangChain(1)