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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Maker
📌
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! 🚀
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Deakshanth
Founder, Beacon
beaconone.net | founder@beaconone.net | linkedin.com/in/rgshetty/