Combine historical patterns with orders, kitchen activity, labor, and supported camera context.
THE GRUBIQ PLATFORM
One operating decision. A verified result.
Find the opportunity. Make the decision. Execute during the shift. Verify the result. Learn what works—with margin improvement and protection as the focus.
ONE CLOSED EXECUTION LOOP
See the pattern. Follow the result.
Explore an illustrative enterprise scenario—from an emerging condition to a controlled test and verified operating response.
LIVE EXECUTION · THE CORE EXPERIENCE
Clear actions. Reviewable evidence.
Turn the current operating state into one clear next action while the shift is happening. Operating improvement and financial attribution stay distinct.
Give the right leader or manager a bounded best next action with supporting context. The manager decides whether to act and whom to assign.
Check whether the action happened and monitor its progress. Managers control physical execution.
Compare expected and actual results. Verify whether the action happened and what changed in throughput, labor efficiency, timing, and margin. A modeled result is not a verified outcome.
Connect the operating state, decision, execution, and measured response. Use that restaurant-specific evidence to inform the next move—not to claim every intervention succeeded.
CAMERA INTELLIGENCE
See what's happening beyond the POS.
Orders, kitchen activity, and labor data reveal part of the operating picture. Compatible existing cameras can add visual context about queues, handoffs, and station activity—structured as evidence for the same GrubIQ decision loop.
ILLUSTRATIVE OPERATING SCENARIO · NOT A CUSTOMER RESULT
- 01
Observe
Compatible camera observations show a queue building at handoff.
- 02
Understand
GrubIQ considers that signal alongside orders, kitchen flow, and staffing context to identify a possible constraint.
- 03
Act
A bounded next action suggests temporary handoff support. The manager decides whether to make the move.
- 04
Verify
Check staff deployment and the later queue response separately. If evidence is insufficient, the result stays inconclusive.
An observed improvement does not, by itself, prove the GrubIQ action caused it. Verification keeps the action taken, operating response, and remaining uncertainty distinct.
UNDERSTAND · HISTORICAL ENGINE
Day-one opportunity starts with your history.
The Historical Engine reconstructs past restaurant operations to find recurring constraints, operating patterns, and potential margin opportunities. It identifies opportunity; it does not manufacture ROI.
Connect available orders, kitchen activity, labor deployment, timing, and supported camera context. Keep missing data and reconstruction assumptions visible.
Look for patterns in throughput, handoff, labor efficiency, prep, and demand pressure. Distinguish what was observed from what is inferred.
Prioritize a bounded operating change with a baseline and success measures. Estimated opportunity is not achieved improvement; prove the value through execution and verification.
MODEL · TIME MACHINE
Explore a different decision. Before the next shift.
Start from a real operating moment and model how different management decisions could affect the next 5, 10, 15, and 30 minutes. Time Machine supports the decision; live execution proves the result.
Anchor the scenario in available operating evidence. Preserve the observed history rather than rewriting it to fit a preferred outcome.
Explore moving available labor to bagging, adjusting handoff timing, or acting earlier on prep. Model possible effects on throughput, labor efficiency, and flow—not guaranteed improvements.
Observed history is what the records show. Assumptions are inputs to the scenario. Simulated outcomes are possible results under those assumptions—not historical facts or measured ROI.
LEARN · RESTAURANT-SPECIFIC EVIDENCE
Real decisions create the evidence for better next actions.
Verification turns a proposed action into reviewable evidence. Record what was expected, whether the action was taken, and what actually changed—including when the expected improvement did not happen.
State
The operating conditions and available evidence at the decision moment.
Action
The best next action, the manager’s decision, and whether and when it was executed.
Outcome
The measured response and its limitations—not a simulated result presented as fact.
Each verified decision adds State → Action → Outcome evidence, helping future models and best next actions become more restaurant-specific. More context can inform better decisions; it does not guarantee an accurate forecast or margin improvement.
Built around your existing systems.
Review integration fit, privacy, and the controlled enterprise pilot.
Explore enterprise readinessSEE GRUBIQ IN ACTION
Bring your operating question.
Explore the product and discuss a controlled enterprise pilot.