Always-on describes availability. Not proactivity. AskColette is always available to be asked. It does not ask on your behalf. It does not act on signals. It waits. The broadcast keeps going.
A conversational AI interface with deep restaurant operations domain knowledge. The knowledge base is genuine. The answers are accurate. The experience of asking and receiving a useful, informed response is genuinely better than a manual or a search.
The broadcast is live every service. AskColette waits to be asked. At 8pm on Friday the operators who most need the intelligence are not asking. They are executing. A results engine does not wait to be invoked. AskColette does.
THE ROOM IS BROADCASTING.
HERE IS WHAT ASKCOLETTE SEES.
What you ask it. When you ask it.
Everything happening in the room in real time. AskColette reads nothing proactively. No WiFi signals. No camera feeds. No device identification. No heatmaps. It responds to queries. The room broadcasts whether or not you query.
The broadcast is continuous. The questions are intermittent. At 8pm on Friday, the operator does not have time to query. The signals fire. The windows open and close. AskColette waits.
could build this.
Conversational AI platforms answer the questions operators remember to ask. The deeper question is what they are trained on. A model trained on restaurant domain knowledge — documentation, best practices, operational guides — knows what the industry has written down. It does not know what 600 million consumer purchasing decisions across stadiums, theme parks, and mass retail actually show about how human beings behave in rooms. That knowledge was never written down. It was observed. And it is not in any dataset a conversational AI platform can access.
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.
AskColette ships with a playbook: a guide to asking the right questions to get the right answers. The playbook exists because the right questions are not obvious. A product that requires a guide is a consulting methodology with a chat interface.
WHAT THEY CLAIMED.
WHAT THE SCORECARD SAYS.
AskColette is a GPT-4 wrapper with a restaurant operations prompt. That is not a criticism of the product. It is a description of it. Any operator could replicate its core functionality this afternoon with a ChatGPT subscription and an hour of prompt engineering. The value they provide is the curation. The curation is a system prompt. System prompts are not a moat.
QUESTIONS FOR YOUR
ASKCOLETTE ACCOUNT REP.
You positioned AskColette as an always-on AI operations expert. Before you commit, these questions are worth taking to your account representative. These are not gotcha questions. They are questions whose honest answers will tell you what you are actually buying.
“Do you have usage data from live Friday dinner services at your deployments — specifically how many queries are submitted between 7pm and 9pm? We want to understand how operators actually engage during service.”
“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 an operator does not ask Colette a question during a service, does the platform initiate any actions or surface any alerts on its own? Or does it wait to be invoked?”
“You ship a playbook for getting value from the platform. Why does the playbook need to exist? What does it enable that the platform does not do on its own?”
“If a guest at Table 9 is disengaging — showing behavioral signals of a deteriorating experience — can the platform detect and surface that without the manager asking about it first?”
“You describe it as like having a consultant on call 24/7. During a live service, is the value in the platform being available, or in the platform acting? Are those the same thing for your operators?”
“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, AskColette 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.