Adhipatya Saxena@adhipatya_saxena

PitchScout is an AI-native mission control agent that turns raw spoken or uploaded startup pitches into structured, actionable market validation reports backed by live web data. Instead of filling out long forms, users speak their pitch out loud for 30–90 seconds or upload an existing audio recording (.mp3, .wav, .m4a, .webm). PitchScout's agentic pipeline transcribes the audio, searches the live web for real competitors, identifies market gaps, and synthesizes a complete validation report containing: - Competitor Landscape: Direct named rivals, positioning, strengths, weaknesses, and market gaps. - Target Customer Persona: Demographic profile, core pain points, and jobs-to-be-done. - 5 Customer Interview Questions: Open-ended questions to pressure-test demand with real prospective users. - Lean Go-To-Market (GTM) Plan: Key launch channels, target first 10 customers, and marketing messaging. Built using native.builder for scaffolding, Speechmatics for speech-to-text, Bright Data Remote MCP for live web competitor search, and AI/ML API (gpt-4o-mini) for report synthesis. It features a cinematic 3D WebGL hero orb (Three.js), Lenis smooth scrolling, and Framer Motion micro-animations.

RegPilot AI is a dual-agent compliance copilot built for founders who can't afford $10,000/hour legal review or 3-6 months of manual regulatory research before shipping. Paste a startup idea, select a target region, and RegPilot AI retrieves the most relevant regulatory clauses using semantic search over Fireworks AI-generated embeddings, then runs two chained agents: a Compliance Agent that identifies specific gaps against GDPR, DPDP, CCPA, and the EU AI Act with severity ratings, followed by an Action Agent that converts those gaps into a prioritized remediation roadmap with immediate, 30-day, and 90-day action items. Every inference call in the pipeline — both the embedding generation and the LLM reasoning — runs on AMD Instinct MI300 GPUs through Fireworks AI, with zero local or third-party GPU usage. Built with FastAPI, Next.js, and a lightweight in-memory vector search layer. Fully deployed and live, not a local demo. Target market: startup founders, indie SaaS builders, and compliance-adjacent consultants navigating a $1B+ legal-tech gap with zero AI-native multi-region competitors.

StudyBand is a multi-agent educational platform built for Track 1 (Internal Enterprise Workflows). It replaces the slow, manual process students go through to study a topic — researching, rewriting notes simply, creating practice questions, and checking answers — with four specialized AI agents that hand off work to each other automatically through Band.ai. The Researcher agent gathers structured study notes on any topic. It passes these to the Simplifier agent, which rewrites them in clear, education-level-appropriate language. The Quiz Master agent then generates multiple-choice questions from the simplified notes. Finally, the Evaluator agent grades the student's answers, gives encouraging feedback, and — if the score is below 80% — automatically triggers the Quiz Master to generate a shorter remedial quiz on the weak topics, creating a real feedback loop between agents rather than a one-way pipeline. All agent-to-agent communication happens inside a shared Band.ai room using @mentions, the same way a human team would hand off tasks in Slack — Band is the actual coordination layer, not a wrapper around a single LLM call. Built with Band.ai, Groq (Llama 3.3 70B for low-latency inference), AI/ML API (for switching between GPT-4o, Claude, and DeepSeek), LangGraph, Python, and Streamlit. Deployed live on Render with both the UI and all 4 agents running together. Beyond the hackathon, StudyBand has a clear path to revenue: a low-cost monthly subscription for individual students, white-label licensing to coaching institutes, or direct adoption by universities as an internal learning tool.

Six Matches. One leaderboard. Build on AMD, earn points and compete for prizes.

🌎 Online Hackathon | 💻 Month-long challenge | Dates: Sep 1–30, 2026 | 🏆 $10,000 Prize Pool ($5k cash + $5k in AAI credits) A month-long online challenge run by lablab.ai together with AssemblyAI, the Voice AI infrastructure company for builders. Every participant builds on AssemblyAI. You can join at any point — registration stays open for the whole build window, so you can start on day one or pick the project up halfway through and still submit. 🌍 Fully online hackathon. Join and build from anywhere in the world.

🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.

To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. ADP members can access AI tutorials, experts, and community support. In addition, new members can claim a free month of DeepLearning.ai pro, $100 in AMD GPU cloud credits, and $50 in Fireworks AI API credits. All hackathon participants will receive on hackathon launch additional compute and API credits for their projects. Ready to build what's next on AMD? Sign up with AMD below to secure your spot.