Why I built Klarix
The path from agricultural computer vision to competitive intelligence — and why it's the same problem.
Shipping production AI at John Deere
I work as an AI engineer on FurrowVision — one of the largest precision-ag programs in the world. Real-time computer vision on A10 GPU clusters, 38M+ sensor images, F1 from 0.44 to 0.92 across eight months of dataset curation and model compression. That's where I learned how to make AI actually run in production, not just in notebooks.
Three years of building startups in my free time
Since 2023 I've spent every night and weekend building. First came Speaksense — a multi-tenant LLM platform with English-to-SQL co-agents, pgvector embeddings, agency-scoped RBAC, and Stripe billing. That taught me what it takes to ship an AI product end-to-end: auth, billing, observability, and the operational glue most founders skip.
Sales teams flying blind
Every B2B team I talked to had the same complaint. They were paying $5K+/month for Apollo, ZoomInfo, Clay, Gong — getting raw data they couldn't use. Reps spent 20+ hours a week researching prospects and still walked into deals blindsided by competitors they'd never heard of.
Intelligence as a deliverable, not a dashboard
I built Klarix because the market didn't need another tool — it needed an outcome. Dossiers, battle cards, SWOTs, outreach sequences. Ready to use. Delivered in 3–7 days. Backed by an agentic pipeline that orchestrates 7 AI providers with cost-optimized routing, 3-tier failover, and AI quality gates. The same engineering rigor I apply at the day job, pointed at competitive intelligence.