For investors
The AI-native operating layer for independent restaurants.
We embed with independent restaurants the way a forward-deployed engineer embeds with an enterprise — and that's the entry point, not the business. Every deployment produces reusable integrations, restaurant knowledge, and agent capabilities, so the human work per new location falls continuously. The service wedges us in; the compounding system is the company.
400,000+independent US restaurants — 3–5% margins, platform-captured, no tech team
2 of 5founding pilots signed (Spring, TX) — six weeks after going full-time
Live nowAI phone agent on a real line, closed-loop growth engine, POS analytics — on our own restaurant first
The problem is knowledge, not tools.
Restaurant tools exist in abundance — 100+ across every category. Independent owners don't know what exists, what fits their specific operation, or when they genuinely need something custom. So they run on defaults: ~30% delivery-app commissions, calls dying in every rush, invisibility in AI search (22% of US diners already ask ChatGPT or Gemini where to eat), and a dozen disconnected logins. At 3–5% net margins, recovering $1–3k/month of leakage is a 25–100% profit swing. Enterprises solve this with embedded engineering teams. Independents get nothing — that gap is the market.
The model: diagnostic → standardized agents → compounding deployments.
1 · Repeatable profit diagnosticEverything starts from the restaurant's own POS: where the next dollar is, ranked by measured impact — not a consultant's opinion.
2 · Standardized agents & integrationsThe proven stack deploys as data + config: an AI phone agent that writes orders into the POS, AI-search visibility, a self-optimizing website loop, guest memory, owner command-and-control by email.
3 · Forward-deployed engineering — only where it compoundsCustom work is the last resort by policy, and when it happens, the capability joins the standardized stack. Each restaurant makes the next one cheaper and faster to deploy.
4 · Outcome pricingFounding pilots: free 3 months, then a share of measured savings/earnings against pre-us benchmarks — provable because we run the measurement layer. Converging to simple per-location plans plus scoped custom work.
Proof — engagement #0 is our own restaurant.
Founder-owned Wok & Karahi (Spring, TX): 4.6★ across 872 Google reviews (roughly doubled in a year), sales 2× in 18 months under our family, and the full stack in daily production — the phone agent is live (call it: (903) 602-4012), the growth loop ships owner-approved website changes weekly, and the analytics engine steers everything from real POS data. Use the product in three minutes → · the full deployment walkthrough →
Why this wins.
The closed loop nobody hasThe AI answers the phone and writes into the same POS the analytics read; the data steers the website, marketing, and menu; results are measured against real revenue. Toast is building this — locked to Toast. The majority of independents have no one.
Trust rails as IPLetting AI act on a live business is gated: dry-run first, double-switched writes, everything revertible, owner approval on anything risky, loud failures. Paid for on our own P&L before any client trusted it.
Operator-founder distributionEx-Google engineer who led Hard Rock Cafe's first AI-agent deployment — and comes from a 15+ year restaurant family. Sells operator-to-operator; the pull came before the pitch.
The economics anchorOwner.com built ~$80M ARR and a $1B valuation on ~10k locations selling one slice of this stack. We operate the whole stack per location, with a wider claim on each restaurant's spend.
Raising our first round.
Live product, two founding pilots deploying, and a founder who is the customer. If you invest at the intersection of AI-native services and hospitality, let's talk.
Email Abbas →
Abbas Zoeb, Founder & CEO · Houston, TX · LinkedIn