Acting on your behalf means the AI makes the decision and it is made. In Harri, the AI suggests and a human approves. One requires the human present and informed. The other does not. Harri built the first one and marketed the second.
A comprehensive workforce management platform with AI-assisted scheduling, demand forecasting, and predictive labor optimization. The predictive models are trained on substantial data. The suggestions are better than unassisted human judgment.
Acts on your behalf describes an AI that makes decisions. Harri AI makes suggestions. A manager approves the schedule. The approval step is the human act that acts on your behalf was supposed to eliminate. Fifty million dollars was raised to remove that step. The step is still there.
THE ROOM IS BROADCASTING.
HERE IS WHAT HARRI SEES.
Scheduling records, labor data, availability patterns, historical shift performance.
Everything happening in the dining room. Guest WiFi behavior, device activity, physical presence, camera signals. Harri intelligence is entirely in the labor layer. The room is invisible.
An optimized schedule is an input to great service, not the output. The schedule determines who is on the floor. What they do on the floor — what they see, what they respond to, what they miss — is governed by information Harri never had.
could build this.
Predictive scheduling models — demand forecasting, shift optimization, labor cost modeling — are built on well-established ML approaches. The question of architectural defensibility in this category is not whether the predictions are accurate; many platforms produce accurate predictions. The question is whether accurate predictions delivered to a human for approval are materially different from accurate predictions that execute autonomously. In our assessment, the approval step is where the value of the prediction is most often lost.
You are not buying their intelligence. You are buying their access to someone else's intelligence. That access costs $20/month. They charge significantly more. The gap is a system prompt and a restaurant logo.
You bought a retainer.
After GM turnover, their CS team runs re-onboarding to align AI models with the new operator context. Fifty million raised. Sixty percent annual GM turnover. A consulting reset every time the person who uses the platform leaves.
WHAT THEY CLAIMED.
WHAT THE SCORECARD SAYS.
Harri raised $50M on AI workforce intelligence that acts on your behalf. The AI suggests. A human acts. The ML models are not proprietary in any defensible sense. Any competitor with equivalent data can build equivalent predictions. The moat is integrations and switching cost. The AI is the marketing.
QUESTIONS FOR YOUR
HARRI ACCOUNT REP.
You raised $50M+ on AI workforce intelligence that acts on your behalf. These are the questions worth putting to your Harri account representative before renewal. These are not gotcha questions. They are questions whose honest answers will tell you what you are actually buying.
“When your AI produces an optimized schedule — who publishes it? Is there a manager review and approval step, or does the schedule go live automatically?”
“Is the behavioral data that powers your intelligence available from any data broker? Could a competitor with sufficient funding purchase equivalent training data and build a competing model? If the answer involves restaurant transaction records, labor logs, or review scores — that data is broadly available. We want to understand what in your intelligence layer cannot be purchased or replicated.”
“If the AI-optimized schedule contains a predictive scheduling violation for one of our markets, does the platform block publication or flag it for human override? What happens if a manager overrides the flag?”
“Your positioning is that the platform acts on your behalf. Can you give me three specific examples of actions the platform took in a live deployment without a human deciding to take them?”
“You know our industry has 60% annual GM turnover. When a GM leaves, what is your re-onboarding process? Is there a timeline before the platform returns to its prior performance level?”
“If we used a basic scheduling platform — 7shifts, HotSchedules — plus your demand forecasting as an add-on, what specifically would we lose? What is the AI layer doing that a simpler tool with your forecasts cannot?”
“Walk me through exactly what data your platform reads from my dining room in real time during service. Specifically: do you read guest WiFi traffic, device identification, physical heatmaps, or camera feeds? Or does your intelligence derive primarily from POS and scheduling system integrations?”
If the answers to these questions satisfy you, Harri may be the right platform for your operation. If the answers surface gaps you had not considered, you now have the information you need to make the right decision. Either outcome is a good one. More questions for every category →
THE EXECUTION LAYER
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AND COULD NOT BUILD.
THE OPERATORS EVALUATING
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WHILE YOU READ THIS.
Every week in the evaluation is another Friday the execution layer does not run. Apply. We review individually. We move fast for operators who are ready.
The assessment on this page represents superGM.ai opinion based on publicly available information, including each company's own published marketing materials, product documentation, and publicly disclosed claims. We do not assert knowledge of any company proprietary codebase, internal contracts, or undisclosed technical architecture. Where we describe category-level architectural patterns, we describe patterns observable across the industry, not necessarily confirmed specifics of any individual product. For each company's actual capabilities, consult their own documentation.