For VPs of Operations, CFOs, and Franchise Leaders

A restaurant-tuned AI analyst on your connected operational data.

Cross-domain root-cause analysis. AI-written daily reports calibrated to role. ML anomaly detection trained on each customer's history. Backed by row-level RBAC and a tenant-specific semantic layer.

The capability

What an AI analyst that knows your restaurants actually does

Every claim below sits on connected operational data — POS, labor, inventory, reviews, weather — normalized into one tenant-specific model. That's what makes the answers right.

AI Chat with cross-domain root-cause analysis

Ask "Why did Store #42 sales drop 25% yesterday?" and OpSage decomposes the change across weather, staffing, menu mix, inventory waste, review trends, and day-of-week patterns — quantifying how much each factor contributed.

Built on connected operational data.

AI Analytical Sandbox

The AI writes and executes Python on your tenant data with read-only safety — statistical decomposition, scenario modeling, ranking models. Capability ChatGPT cannot match because ChatGPT cannot see your data.

Built on restaurant-tuned context.

AI-written daily and weekly reports

A narrative summary of yesterday's performance calibrated to your concept, business priorities, and performance targets — delivered to inbox each morning. Weekly Business Reports auto-populate from every connected system with variance vs. target, AI analysis of what moved the numbers, and PDF/CSV export. No per-user fees for access or distribution.

Built on tenant-specific.

Operational Observations

Managers describe what happened — typed or spoken — in plain English, and OpSage classifies and stores the observation, connected to anomaly alerts and weekly reports. The institutional knowledge your team already has, captured.

Built on restaurant-tuned.

ML anomaly detection trained on your history

The model establishes a baseline for each metric at each location, accounting for day-of-week, seasonality, weather norms, and holidays. Alerts arrive only when actual performance deviates beyond your concept-calibrated thresholds.

Built on tenant-specific semantic layer.

Business profiles and competitive intelligence

OpSage builds an AI-generated identity layer for your brand and discovers competitor profiles from public data — feeding back into every recommendation, benchmark, and anomaly threshold.

Built on restaurant-tuned context.

Why it works on your data

The moat ChatGPT can't cross

ChatGPT is smart about restaurants in general. OpSage is smart about your restaurants — because we connect operational data the general-purpose models never see, enforce RBAC at the row level, and run on a tenant-specific semantic layer so your “lunch” means your lunch and your “Omaha region” means your Omaha region.

Every AI claim on this page is grounded in connected operational data, restaurant-tuned context, and row-level RBAC. That's the moat — not the model.

See it answering a real question on your data

A free anomaly-pilot — gated on a POS connection — that shows the AI analyst answering one real operator question on your connected operational data, with the moat doing the work.

Start the pilot See the platform