Deaksh

Beacon - Turn 12-month compliance workflows into weeks with AI agents

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Beacon = agent-first regulatory compliance. AI agents execute compliance work autonomously, not assist humans. Two modules: - AI Compliance: Classify AI systems (EU AI Act), map obligations, generate workflows. 2 min vs 2 weeks. - Biopharma: Automate IND prep - data QC (21 CFR Part 11), CMC generation. 6 weeks vs 3-4 months. Agent infrastructure, not AI features. Content-addressed storage (SHA256) = tamper-proof. Self-improving accuracy. SAP Startup Studio validated. Seeking design partners.

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Deaksh
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Hey Product Hunt! 👋 I'm Deakshanth, founder of Beacon. After 9 months building solo with AI agents, I'm excited to share what we've built. The Problem: AI can design therapeutics in 48 hours. Proving regulatory compliance takes 12-18 months. This bottleneck is killing innovation. Enterprises face the same issue: EU AI Act enforces Aug 2, 2026 (10 weeks away). Manual compliance = spreadsheets, consultants, months of work. What We Built: Beacon = AI agents that execute compliance workflows end-to-end, not assist humans. AI Compliance Module: - Agents classify AI systems under EU AI Act (prohibited/high-risk/limited/minimal) - Map to specific obligations (Articles 9-15) - Generate compliance workflows + audit documentation - Result: 2 minutes vs 2 weeks with consultants Biopharma Module: - Agents automate IND submission prep for biotechs - Regulatory intelligence: sequence → ICH/FDA requirements - Analytical data QC: HPLC/MS validation (21 CFR Part 11 compliant) - CMC generation: ICH M4Q submission format - Result: 6 weeks vs 3-4 months manual What Makes Us Different: 1. Agent-first, not AI-assisted We're not compliance software with AI features. We're agent infrastructure. The agents execute workflows autonomously. 2. Data integrity moat Content-addressed storage (SHA256 hashing) = cryptographically tamper-proof. Regulators trust this more than PDFs. No competitor has this for biopharma analytical data. 3. Self-improving accuracy User corrections → training data → model fine-tuning. Accuracy compounds 5-10% quarterly. After 2 years: 90%+ vs competitors' 60-70%. 4. Platform economics 70% shared codebase between modules. One infrastructure, two markets. Build once, sell twice. Current Stage: - Working MVP (both modules functional) - SAP Startup Studio 2026 validation (Top 100 of 700+ AI startups) - 15+ pilot conversations - Pre-revenue, seeking design partners Try It: Free readiness assessments: - EU AI Act: beaconone.net/eu-ai-act-readiness - IND Readiness: beaconone.net/ind-readiness Looking For: 1. Design partners - Enterprise compliance teams, biotech companies preparing INDs 2. Feedback - What compliance workflows should agents tackle next? 3. Co-founder - Seeking regulatory/domain expert (ex-FDA, pharma CMC, EU compliance) Tech Stack: Built with Claude Sonnet 4.6 API (agentic workflows), Claude Code (development), LangChain (orchestration), RAG pipeline (huggingface AI + pgvector). 1,500+ hours building with AI agents. This is agent-native architecture from day one. Questions I'd Love to Answer: - How do agents handle ambiguous regulations? - What happens when regulations change? - How do you ensure data integrity? - Why dual-module vs single focus? - How do you compete with OneTrust/Vanta? Happy to share demos, architecture deep-dives, or GTM strategy. Thanks for checking us out! 🚀 --- Deakshanth Founder, Beacon beaconone.net | founder@beaconone.net | linkedin.com/in/rgshetty/