AI marketing and reservations for restaurants, built around cover counts, repeat visits, and reservation density. Fill the room tonight, bring the guest back next month. Rhetor wins the "best [cuisine] near me" search and keeps the seats full.
A restaurant is operationally a yield-management business. Your seats are fixed; your schedule is what changes. A house at 80% covers on a Tuesday at six is operationally completely different from one at 40%. Your marketing has to feed your operational target, not just chase awareness.
Most independent restaurants underinvest in visibility because the marketing playbook reads as "drop flyers, run a Yelp ad, hope for the best." The result is unpredictable cover counts and a revenue line at the mercy of weather and weekday rotation. Rhetor builds the system that smooths the curve.
The visibility side leans on local SEO, the Google Business Profile, reviews, and AI visibility work for the buyer searching "best [cuisine] near me" or "[restaurant name] reservations." Most local diners use Google to make a tonight-or-tomorrow decision. The map pack is the menu.
The repeat-customer side is where your operation scales. Restaurant automation covers birthday outreach, anniversary follow-ups, slow-week promotions, and reservation-density tools. Built once, then tuned to the seasonal calendar.
Rank tracked across the cities and neighborhoods your covers actually come from. Profile management, photo cadence, review acquisition, and local schema all shipped on a weekly cadence, backed by neighborhood guide content that AI platforms cite for "best [cuisine] near me" queries.
Slow-night targeting, birthday and anniversary outreach, reservation reminder sequences, deposit and no-show automation. POS integration with Toast, Square for Restaurants, Lightspeed, or Aloha. Booking integration with OpenTable, Resy, SevenRooms, or Tock. Custom work (loyalty-program tie-ins, third-party delivery routing, kitchen-display fire timing) is scoped per engagement.
Schema-tagged menu content (Menu, MenuItem markup), neighborhood guides, and dish-level pages that get cited when AI platforms answer "best Italian in [city]," "where to get [dish] near me," or "[restaurant] reservations." If your group runs multiple concepts (a casual concept alongside a fine-dining concept alongside a bar concept), we add national SEO too.
Post-visit review requests sent through your existing POS or reservation system. Sentiment trends tracked monthly so the kitchen sees what guests actually say. The review surface is built directly into the AI-optimized site on every restaurant we work with.
We audit your cover counts against capacity and visibility, ship the reservation-density automation wired into your existing POS or booking system, and build the slow-night and rebooking campaigns that smooth the cover curve over time.
Integrates with whatever POS or booking system your restaurant runs. Custom integrations built during the engagement when needed.
Both, with different marketing motions. Full-service leans on reservation density and reviews. Fast-casual leans on order-ahead, delivery integrations, and high-volume local visibility.
Yes. Each of your locations gets its own Google Business Profile, its own neighborhood map pack tracking, its own review acquisition. For groups running multiple concepts (a casual concept alongside a fine-dining concept in the same group), each concept's brand identity and search profile stay separate. Your dashboard rolls up performance across the whole group.
We focus on direct-channel revenue first because the margin is materially better. Third-party platform work supported but framed against the goal of growing direct orders over time.
Build fee plus monthly retainer. Retainer scales with location count and channel coverage, not with cover volume on a given week.
Tell us where your business is stuck and we'll walk through how we'd run it. Marketing that gets you found across Google and every AI platform, agents that handle your inbound and follow-up, and engineering when your business needs custom AI built around how it actually runs.
