Pankaj Panwar@PankajP
2
Events attended
2
Submissions made
India
8+ years of experience
About me
IIM & NIT alumnus | Reforge & Stanford-trained | Senior Product Manager with 8+ years of experience building AI-driven decision systems and growth platforms across fintech and marketplace businesses. Proven track record of owning end-to-end user journeys (acquisition → activation → transaction) and improving conversion, reducing friction, and scaling operations through data-driven product decisions.
🤓 Latest Submissions
QuickLens
QuickLens is a real-time q-commerce intelligence system built on two autonomous agentic AI loops — one reactive, one proactive — powered by Bright Data's live web data infrastructure. THE PROBLEM Q-commerce prices on Blinkit, Zepto and Swiggy change multiple times a day. Consumers overpay without knowing it. Brands have zero visibility into competitor moves in real time. THE SOLUTION Consumer Loop (reactive): User asks "cheapest 1L Amul Milk in Indiranagar." The agent plans which platforms to query, calls Bright Data's Search API in one batch call across all 3 platforms, observes results, handles failures, and returns a ranked price comparison with delivery times and savings — in under 25 seconds. Brand Loop (proactive): Runs every 5 minutes without being asked. Monitors competitor SKUs, detects price drops and OOS events, reasons about signal significance, and fires natural language alerts with action recommendations — e.g. "Zepto OOS on Tata Salt 500g — boost Blinkit ad spend for next 2 hours." HOW BRIGHT DATA POWERS IT Direct scraping of q-commerce platforms is blocked by bot detection and JS rendering. Bright Data's search_engine_batch is the only viable path — querying all 3 platforms simultaneously in one call. scrape_batch fires as secondary enrichment when SERP snippets lack prices. Bright Data is the foundation, not an add-on. ARCHITECTURE Both loops share a Bright Data tool layer and Gemini 2.5 Flash Lite as reasoning engine. Shared state.json persists price history, watchlist, and alert log. APScheduler runs the brand loop as a background daemon so Streamlit stays responsive. TECH STACK Python · Gemini 2.5 Flash Lite · Bright Data MCP · Streamlit · APScheduler · Pydantic
31 May 2026
Procurement Intelligence Agent
Procurement Intelligence Agent is an AI-powered contract analysis system built for enterprise procurement teams. It reads any vendor contract PDF, answers specific questions with page-level citations, flags compliance risks across 10 categories, drafts structured approval memos, and researches vendors in real time — all in under 60 seconds. The Problem: Enterprise procurement teams spend 4–6 hours manually reviewing each vendor contract. Risk clauses get missed — auto-renewal traps, IP ownership grabs, inadequate liability caps — and every approval memo is written from scratch. A single overlooked clause can cost the business significantly. Key Features: 1.Conversational Q&A with page-level citations — every answer is grounded in the document 2.AI risk scanner across 10 categories using Gemini response_schema for structured output 3.Approval memo generation with one-click PDF download 4.Multi-contract comparison — side-by-side Q&A or risk scan across 2–4 contracts 5.Live vendor research via Gemini Google Search grounding 6.PDF upload with live ingestion — any contract indexed in under 30 seconds Tech Stack: LangGraph for agent orchestration, Gemini 2.5 Flash for reasoning and structured output, ChromaDB for vector storage, Streamlit for UI, reportlab for PDF export. Gemini Integration: Gemini 2.5 Flash powers all agent reasoning, structured risk output via response_schema, and live vendor web search grounding via the native Google Search tool. Built for TechEx Intelligent Enterprise Solutions Hackathon 2026 on lablab.ai — solo, in 7 days, while working full time.
19 May 2026
