The AI workforce for a food company.

Why F&B Codex exists

Every mid-market food brand we've looked at runs on the same stack: PDFs, spreadsheets, email threads, and one overworked QA or regulatory lead. The enterprise platforms built for this category (TraceGains, SafetyChain, FoodLogiQ, Specright) are 6–12 month implementations at $50K–$250K ACVs. The R&D / nutrition tools (Genesis R&D, FoodLabelMaker) are database-first products built for a human formulator. Neither tier sells the thing a Director of Quality at a $40M brand actually wants: a senior regulatory professional, on call, at FTE-fraction pricing.

That's what we sell. We don't ship modules; we ship named agents. You don't buy a "document control module" — you hire a Quality Specialist. You don't procure a "regulatory intelligence platform" — you wish you could afford a Regulatory Affairs Specialist. F&B Codex builds exactly that, on Claude, with an audit-trail-first discipline borrowed from regulated industries.

What we believe

• Agents named after roles, not features. The org chart is the right unit of sale for the AI workforce. Each agent has a job title, a résumé, a price, and a portfolio of work product.
• Trained on your data is the moat. Every food company operates differently. Your SOPs, your spec templates, your release criteria, your brand voice — these are the guardrails that make an agent yours and a competitor's tool generic. We ingest them per-tenant. We don't share them. They are the switching cost.
• Cite or strike. Every regulatory claim ships with a primary-source citation and an access date. No claim, no ship.
• Human checkpoint on every regulated output. No BLOCKER finding or claim rewrite is closed without a named human signing it off in the audit log.
• Per-tenant isolation from Day 1. Customer content stays in customer-tenant directories. No cross-tenant training. No surprises.
• Defer features ruthlessly. We will not ship the next agent before the current one has paying customers.

The team

F&B Codex, Inc. is a Delaware corporation founded by Venkata Pamu, operating the AI-native model: a small human headcount with AI agents doing the production work, and humans approving the high-stakes decisions. The intentional plan is to stay lean through the first paying pilots and then evaluate additional hires against a defined MRR trigger.

Where we are

June 2026. Codex Reg v1.0 is live and accepting pilots. Five design-partner slots open this quarter. Codex Quality is in scope for Q3 2026. Codex R&D is scoped for Q4. Codex Label and Codex Supply follow in 2027.