Tenzo
AI restaurant analytics that unifies POS, labour, inventory, and reviews, queryable in plain English.
About
Tenzo is a restaurant analytics and AI forecasting platform that pulls data from POS, labour, inventory, reservations, and review platforms into one real-time dashboard. Operators get automated daily reports, drag-and-drop reporting by site, shift, item, or channel, and an AI forecasting layer that combines historical sales with weather, seasonality, holidays, and local events to predict demand. The vendor claims forecasting up to 30-50% more accurate than baselines and COGS reductions of 3-5% for operators who use it for ordering and staffing.
Tenzo's differentiator is its AI Connector built on the Model Context Protocol (MCP): teams can query their normalized operational data from Claude, ChatGPT, or another LLM in plain English and get joined-up answers across sales, labour, and inventory without manual uploads. The company says it is trusted by 250 companies across 1,500+ locations, with integrations including SevenRooms, Centegra, and Restaurant365. Accuracy and adoption claims are vendor-reported; the MCP approach and integration catalog are documented on its site and in user case studies.
What it can do
- Real-time dashboards across all locations
- AI demand forecasting with weather, events, and seasonality
- Automated daily reports to phone or email
- Drag-and-drop custom report builder
- MCP AI Connector for plain-English LLM queries
- Review and sentiment aggregation (Google, Yelp, Tripadvisor)
- Forecasts pushed into labour scheduling and stock ordering
- Integrations: SevenRooms, Centegra, Restaurant365, and more
Setup
- Book a demo and connect your systems (POS, labour, inventory, reservations, reviews).
- Tenzo normalizes the data into a single real-time view.
- Schedule automated reports and set target alerts per site.
- Connect the MCP AI Connector to Claude or ChatGPT for plain-English queries.
Prerequisites
Use cases
Works with
Trust score — 8/10
Scored 8/10 (TIER verified, metrics-v1). Evidence: 250 companies / 1,500+ locations claimed and consistent across the vendor's site; documented MCP AI Connector with a public user case study (McManus Pubs, July 2026); published integration catalog. Strengths: open MCP approach, multi-system normalization, active 2026 development. Docked for: (1) adoption and accuracy figures are vendor-reported; (2) pricing not published; (3) independent review coverage is limited. License: proprietary.
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 7.0/10
- Maintenance · recency, releases, issue load
- 8.0/10
- Openness · license + docs
- 7.0/10
- Manual review · sanity read + red-flag check
- 7.0/10
- · 250 companies / 1,500+ locations claimed consistently on vendor site
- · MCP AI Connector (plain-English LLM queries) with public user case study (McManus Pubs, Jul 2026)
- · Accuracy/adoption figures are vendor-reported; limited independent coverage
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.