AI Agent Engineer Projects in IndiaAI Agent Engineer Projects in India
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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 PromptOT – AI Prompts Get
PromptOT – AI Prompts Get Refined, Versioned, Evaluated & Shipped PromptOT is a prompt management platform designed to help AI teams treat production prompts as production code. It lets teams author prompts in structured, typed blocks, version every change with full history and rollback, evaluate versions against saved test cases across multiple models, and deliver the compiled, variable-driven prompt to their application via a single API call or native MCP integration, with no redeploy required. We built a compilation engine solid enough for production use, an AI co-pilot for conversational prompt editing with inline diffs and scoring, and native support for the tools AI teams already use daily - Claude Desktop, Cursor, ChatGPT, Codex CLI, Windsurf, and Zed. Key Features - Typed Prompt Blocks Semantic Versioning with Rollback Evaluations Across Models API & MCP Delivery AI Co-Pilot for Prompt Editing AI teams often struggle with - Prompts scattered across a Google Doc, a Slack thread, someone's Notion, and hard-coded strings in the codebase No version history, no diffs, no way to know which version is actually live No way to evaluate a prompt rewrite before shipping it to production Legal and brand review happening informally in DMs, if at all PromptOT delivers a single source of truth for every production prompt, shipped by API or MCP. It bridges the gap between prompt experimentation and reliable, production-grade delivery, turning prompts from fragile prose into managed, versioned infrastructure.
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Cover image for Case Study: How One AI Agent Saved a Founder Weeks of Resear...
Case Study: How One AI Agent Saved a Founder Weeks of Research Hey founders! A few days ago, a client reached out with a new requirement. He said: “I need a full due diligence report Agent. Research everything news, social media, the whole internet and give me a clean PDF.” (for same report Messari.io (https://messari.io/) charge $4000/year) No big research team. No weeks of waiting. I built a smart AI agent that handled the entire job automatically. Here’s exactly what happened: The client simply uploaded the white paper. The AI read it carefully, then went out and searched the internet, news sites, and social media for real-world insights, risks, and opportunities. It pulled everything together and delivered a professional, ready-to-read PDF report. All done in hours instead of weeks. I used the latest AI tools behind the scenes a simple chat interface on Slack, multiple smart agents working together, and powerful search capabilities. But the founder didn’t need to know any of that. He just uploaded the document and got his report. This kind of AI is perfect when you have a consistent workflow things you do again and again, like due diligence, market checks, competitor reviews, or investment research. It gives you accurate, thorough results every single time without hiring extra people or spending endless hours. For non-tech founders, this is game-changing. You stay focused on building your business while the AI does the heavy research lifting. Would you use an AI like this for your next big decision? Drop a comment I’d love to know what kind of research you hate doing manually! 👇
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Cover image for We recently built an AI
We recently built an AI Customer Support Agent, and one thing surprised us... The chatbot wasn't the hardest part. Getting the AI to understand the business was. The quality of an AI assistant isn't determined by the model alone. It's determined by the information you give it. During development, we found that connecting the AI to the right documentation, FAQs, business rules, and workflows made a far bigger difference than switching between models. Once the AI had the right context, responses became more accurate, consistent, and genuinely helpful. It reinforced something we've been seeing across recent AI projects: Businesses don't need another chatbot. They need an AI assistant that actually understands their business. That's where the real value is. Curious to see how other teams are approaching AI knowledge management. What's been your biggest challenge when building AI products? #AI #ArtificialIntelligence #CustomerSupport #AIAgents #SaaS #ProductDevelopment #Automation #OpenAI #Claude #BusinessAI #TvaronTechnologies
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Cover image for I built FlowLancer — My
I built FlowLancer — My End-to-End Autonomous Freelance AI Agent As a freelancer, I was spending way too many hours on admin work — managing clients, proposals, time tracking, invoicing, and follow-ups. So I created FlowLancer, my custom AI agent in Notion that handles almost all backend operations automatically. Here’s what it does for me: • Processes new client emails and turns them into proper projects + proposals • Creates my daily plan every morning with prioritized tasks • Logs time automatically when I tell it • Tracks project progress and creates next tasks • Prepares invoice drafts when milestones are complete • Sends polite payment reminders • Gives me a full weekly summary every Sunday Now I spend 90% of my time on actual client delivery work instead of operations. 🔗 Agent: FlowLancer (https://www.notion.so/marketplace/custom-agents/flowlancer)
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