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
59
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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Cover image for Technical Writing for Encore | 30k+ views
Technical Writing for Encore | 30k+ views
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Cover image for Technical Writing for Encore | 30k+ views
Technical Writing for Encore | 30k+ views
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12
Cover image for Technical Writing for Opire | 60k+ Views
Technical Writing for Opire | 60k+ Views
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22
LangChain(1)
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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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 One Gate: Voice-controlled Gmail, Calendar,
One Gate: Voice-controlled Gmail, Calendar, and Drive that can't fire the wrong thing Every "AI agent" demo shows a voice command turning into a sent email like magic, but almost nobody shows the part that actually matters: what stops it from sending the wrong thing. I built proof: an agent that runs your Gmail, Calendar, Contacts, and Drive by voice reply to a thread, schedule a meeting, archive an email, find a file hands-free end to end, but never fires anything irreversible without you saying so. The honest hard part isn't getting an LLM to sound smart, it's this: a transcript goes to LLM against a strict JSON schema and comes back as an ordered plan, every step tagged reversible or not and exactly one thing in the whole system is allowed to check that flag. Finding a thread, drafting a reply, creating a calendar event: those just run. The result covers real ground without ever feeling like it's guessing: forward or reply to email with the original quoted underneath, archive/label/trash, resolve a name to a real address through your Contacts first and your mail history as fallback, schedule an event that creates quietly and only emails the invite after a second confirmation, answer "what did John say" or "what's on my calendar Thursday" grounded in content actually fetched from your account not hallucinated. No backend, no server anywhere in the loop
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10
LangChain(3)
Suyash Dubey's avatar
Suyash Dubey
Delhi, India
I build production AI agents that automate real workflows
I build production AI agents that automate real workflows
Cover image for Built an LLM-powered question-answering application
Built an LLM-powered question-answering application that lets users ask natural-language questions over large document corpora and get accurate, grounded answers, instead of manually searching through documents for the right section. Designed and built the full RAG pipeline independently, from document ingestion through to answer generation, as a technical demonstration of production-grade retrieval-augmented generation using AWS-native tooling. Key Challenges: Documents exceeding token limits: Large source documents couldn't be fed directly into the LLM's context window, so they had to be broken down without losing meaning or context across chunks. Finding the right context: With a large corpus, the system needed to reliably surface the specific chunks relevant to a given question, not just the most textually similar ones. Grounded, accurate answers: Answers had to be based on the actual retrieved content, not the model's general knowledge, to avoid confidently wrong responses. Working within a managed AWS ecosystem: Embeddings, storage, and generation all needed to work together cleanly using Bedrock-native models rather than a patchwork of external services. Approach: Document loading and chunking Processed large documents into manageable chunks sized to stay within model token limits while preserving enough context for coherent retrieval. Vector embeddings with Amazon Titan Generated vector embeddings for each document chunk using Amazon Titan, capturing semantic meaning rather than just keyword overlap. Vector storage and retrieval Stored the embeddings in a vector database, enabling fast similarity search to pull the most relevant chunks for any given question. RAG-based answer generation with Claude on Bedrock When a question comes in, the system retrieves the relevant chunks and passes them as context to Anthropic Claude via Amazon Bedrock, which generates an answer grounded in the retrieved content rather than relying on parametric memory alone. Results & Impact: Accurate, source-grounded answers over document corpora too large to fit in a single context window. A scalable retrieval architecture that separates document processing, embedding, and generation, so any of the three can be swapped or scaled independently. A fully AWS-native RAG pipeline, demonstrating fluency with Bedrock's embedding and generation models working together in production patterns. Tech Stack Python · LangChain · AWS Bedrock · Amazon Titan · Anthropic Claude · FAISS DB
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Cover image for Built and maintained a HIPAA
Built and maintained a HIPAA compliant production medical AI scribe system that uses multi-step LLM agents to extract clinical entities directly from physician-patient conversations and turn them into structured medical notes, cutting down the manual transcription work clinicians used to do after every visit. Multi-stage clinical workflow: Documentation, coding, and review each have different logic and conditional paths, and the system needed to branch correctly between them without losing context. Clinical accuracy: Generated notes had to be grounded in real patient history and clinical guidelines, not just plausible-sounding text. Production reliability: As a live system handling real conversations, every agent run needed to be observable, debuggable, and monitored for cost and latency in real time. Non-technical requirements gathering: Clinical needs had to be captured accurately from stakeholders without a technical background and translated into precise agent logic. Approach: Stateful agent orchestration with LangGraph Designed LangGraph-based agent workflows with conditional branching, allowing the system to move correctly across documentation, coding, and review stages based on conversation content. Context-grounded note generation with RAG Built a RAG pipeline on AWS Bedrock with embeddings, so every generated note is grounded in the patient's actual history and relevant clinical guidelines rather than generic output. Full production observability Integrated Langfuse across all agent runs to track token usage, latency, and model KPIs, giving the team visibility into system health and cost in production, not just at build time. Clinical stakeholder collaboration Ran requirements sessions directly with clinical staff, converting their documentation needs into concrete agent behavior specs and validation criteria. Results & Impact: ~40% reduction in manual transcription time for clinicians using the system. Clinically grounded output, with notes tied to real patient history and guidelines instead of unsupported generation. Full production observability, with token usage, latency, and model performance tracked continuously. A workflow clinicians could trust, built through direct collaboration rather than a black-box handoff. Provided Services & Solutions: 📌 AI Agent Development (LangGraph) 📌 RAG Pipeline Development (AWS Bedrock) 📌 LLM Observability (Langfuse) 📌 Cloud Infrastructure (AWS Lambda, S3, DynamoDB) 📌 Stakeholder Requirements Translation 📌 Production ML Systems Tech Stack Python · LangChain · LangGraph · AWS (Bedrock, Lambda, S3, DynamoDB) · Langfuse · TypeScript · REST APIs
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Cover image for Overview 📖 Built an end-to-end agentic
Overview 📖 Built an end-to-end agentic content creation pipeline for a fast-growing AI-powered SEO platform. The system chains multiple LLM agents together to research, draft, and optimize content automatically, replacing what used to be a manual, multi-step editorial process with a single automated workflow. Collaboration 🤝 Partnered directly with the platform's engineering team to design and ship the automation layer that now sits at the core of their content operations, turning a bottlenecked manual process into a scalable, always-on pipeline. Key Challenges 🤔 Multi-step content logic: Research, drafting, and optimization each require different context and tone, but had to feel like one coherent pipeline, not three disconnected tools. Consistency at scale: Every piece of generated content had to match brand voice and pass compliance checks, without a human reviewing each one manually. Orchestration complexity: Content jobs needed to trigger reliably from webhooks and third-party APIs, run through multiple agents in sequence, and fail gracefully without stalling the whole pipeline. Performance under load: The backend had to stay fast and stable as content throughput scaled up. Approach 🔍 Agentic content pipeline design Designed a multi-step LangChain agent chain with tool-calling, where each agent (research, drafting, optimization) has a clearly scoped role and hands off structured output to the next. Workflow orchestration with n8n Built n8n automation workflows to handle webhook triggers, third-party API integrations, and job routing, removing the need for manual intervention at almost every stage. Brand voice & compliance enforcement Layered in structured prompting and validation steps so generated content stays on-brand and passes compliance checks automatically, at scale. Backend performance tuning Optimized FastAPI endpoints and managed Azure-hosted PostgreSQL databases to keep latency low under high content-throughput conditions. Results & Impact ✨ ~60% reduction in manual intervention across the content pipeline, freeing the team to focus on strategy instead of babysitting workflows. Consistent brand voice at scale, with compliance checks running automatically instead of manually. Reliable, low-latency infrastructure validated under real content-throughput loads. A reusable agentic architecture the platform can extend to new content types without rebuilding the pipeline. Provided Services & Solutions ✅ 📌 AI Agent Development (LangChain) 📌 Workflow Automation (n8n) 📌 LLM Integration (GPT-4, Claude) 📌 API Development (FastAPI) 📌 Cloud Database Management (Azure, PostgreSQL) 📌 Architecture Design & Consulting Tech Stack Python · FastAPI · LangChain · n8n · GPT-4 · Claude · Azure · PostgreSQL
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Cover image for My submission for replitbuildathon. Features
My submission for replitbuildathon. Features an AI agent that answers inbound leads in seconds, qualifies them against your rules, and books the appointment — rebrandable for a new client in about a minute. Most small businesses lose leads to silence. Someone lands on the site at 9pm, fills nothing in, and leaves. Frontdesk is the agent that catches them. It greets the visitor, works through the qualifying questions you defined, scores what it hears out of 100, books the appointment, and hands the team a lead with the transcript and a follow-up email already drafted. The reason it's a template and not a product: it's built to be rebranded. Set a logo and two colours in Brand Studio and the entire app, the chat widget, and the design system documentation retint together, because they all read the same tokens. Agencies fork it once per client. Applying a preset doesn't recolour the same install: it opens a different one, with its own agency name, business name and greeting. Checkout the app here: https://frontdeskzip--SuyashDubey3.replit.app
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LangChain(3)
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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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 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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LangChain(5)
Mehul Sethia | Senseibles's avatar
Mehul Sethia | Senseibles
proIndia
Design, Build, AI, Automation
$50k+
Earned
19x
Hired
5.0
Rating
68
Followers
Design, Build, AI, Automation
Cover image for DepX: AI-Powered DevOps Copilot
DepX: AI-Powered DevOps Copilot
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Cover image for Revolutionize Your Sales Process with Instant Deal Closures
you have been WARNED. I am going to make every contact form, every Calendly link, and every "we'll get back to you" completely irrelevant. Conversations close deals. I made them instant. converc.com (http://converc.com)
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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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LangChain(1)