Plenty Unlimited \USA

Step 2 - AI Recommendations and 10 Customers (Validation): Build the AI recommendation engine using Claude API + RAG. Ingest 500+ agricultural research papers (use Semantic Scholar API to scrape PDFs, convert to embeddings with OpenAI, store in Pinecone). Create a chat interface where customers describe their crop and goals, and the AI suggests optimal light schedules, nutrient ratios, and climate settings based on research. Add computer vision using Roboflow: customers upload photos of plants, and the model detects nutrient deficiencies, pests, or growth stage. Expand to 10 paying customers across 3 verticals: cannabis, gourmet herbs, and research institutions. Charge $500/month for AI features. Goal: Validate that AI recommendations improve yields by 15-30% compared to manual growing, and that customers will pay for software alone (even without buying hardware). Collect data from all 10 grows to train better models.

Step 3 - Hardware Productization and Channel Partnerships (Growth): Design a production-ready modular grow unit with contract manufacturer (consider Fictiv or PCBWay for electronics, local fabricators for enclosures). Reduce unit cost to $3-4K through volume and design optimization. Create a self-serve e-commerce flow on the website: customers configure their unit (size, crop type, budget), checkout with Stripe, and receive hardware in 2-4 weeks. Build channel partnerships with hydroponic supply stores (20% commission on hardware sales) and agricultural extension programs (universities, government ag departments) who recommend the platform to researchers. Launch a referral program: existing customers get $500 credit for each new customer. Goal: Reach 100 units deployed and $50K MRR from software subscriptions. Use customer data to publish case studies showing ROI: a cannabis grower increased yield by 25% and reduced energy costs by 15% using AI-optimized light schedules.

Step 4 - Data Moat and Enterprise Expansion (Moat): With 100+ customers generating grow data, build proprietary AI models that outperform any competitor. Create a data flywheel: customers who share their data (opt-in) get free AI credits and early access to new features. Use this data to train crop-specific models: a cannabis model, a mushroom model, a microgreens model—each fine-tuned on thousands of grow cycles. Launch an enterprise tier ($2-5K/month) for agricultural research institutions and seed companies who want to run experiments: A/B test different light spectra, nutrient mixes, or genetic varieties across dozens of controlled environments. Build an API so enterprise customers can integrate Photon Labs data into their own R&D pipelines. Partner with agricultural input companies (LED manufacturers, nutrient suppliers) who want to validate their products using real-world data. Goal: Reach $2M ARR with 30% from enterprise contracts. Position for acquisition by Bayer, Syngenta, or John Deere who want the AI models and customer relationships. Alternative exit: raise Series A to expand into adjacent markets like aquaponics, insect farming, or cellular agriculture bioreactors—any controlled environment where AI optimization creates value.