Measure Every Step of Restaurant Service
NAARU uses existing restaurant cameras to measure service times, identify operational bottlenecks, and give managers visibility into what happens across every shift and location.
Know Where Service Slows Down Before It Becomes a Complaint
From the first greeting to the final table reset, NAARU turns everyday restaurant activity into measurable KPIs. Managers can review averages, identify outliers, and understand where service falls behind expectations.
Guest arrival and wait time
Track observable guest arrival and seating events to understand wait times and identify recurring delays.
Greeting, food arrival, and service visits
Measure time to first greeting, food arrival, and employee service visits throughout the dining experience.
Table occupancy and turnover
Track table occupancy, guest departure, and reset times to understand how efficiently tables become available.
Labor coverage versus guest demand
Compare observable employee activity with guest demand to identify potential coverage gaps during busy shifts.
From Camera Footage to Actionable KPIs
NAARU identifies observable service events from existing camera footage and organizes them into measurable dining sessions. Managers can review performance against configured targets and investigate exceptions using the associated footage.
- Select a restaurant and shift
- Review service KPIs and averages
- Identify delays and outliers
- Review the associated video evidence
- Address recurring issues with the team
What Your Team Can Measure
- Time to first greeting
- Measure how long guests wait before their first observable interaction with a server.
- Food arrival time
- Track the time between the start of a dining session and the observed arrival of food.
- Service visits and gaps
- Measure service frequency and identify unusually long periods without server attention.
- Table occupancy and reset
- Track dining duration and the time required to clean and prepare a table for the next party.
- Shift averages and outliers
- Compare performance against configured service targets and highlight sessions requiring review.
- Coverage versus guest demand
- Compare observable employee activity and guest volume to help managers identify staffing mismatches.
Beyond Table Service: Custom Kitchen Workflows
The same computer vision approach can be adapted to other observable restaurant workflows, including kitchen preparation timing.
For example, if a kitchen procedure typically takes 15 minutes, a custom workflow could measure observable start and completion events, compare durations against a benchmark, and highlight outliers for review.
These capabilities require customer-specific development and validation. Starting with a limited number of well-defined activities helps establish accuracy before expanding to additional kitchen processes.
Frequently asked questions
Can restaurant cameras measure service times?
Yes, cameras can support service-time measurement when the relevant events are visible. NAARU measures intervals such as first greeting and food arrival within table sessions and lets managers review the supporting footage.
Can computer vision track table turnover?
Yes, observable occupancy, departure, and reset events can support table-turnover tracking. Camera coverage and consistent event definitions determine which intervals are measurable.
Can restaurant security cameras measure food preparation time?
Potentially, if both the preparation start and completion are visible and clearly defined. Kitchen-preparation tracking is a custom workflow requiring development and validation, rather than a standard deployed feature.
Can preparation times be compared against standard operating procedures?
Yes, a validated custom workflow could compare an observed duration against a configured SOP benchmark. Managers still need to review context, and timing cannot verify hidden conditions such as internal food temperature.
Can restaurant groups compare operational performance across locations?
Yes, NAARU supports reviewing service performance across locations. Use consistent event definitions and account for camera coverage and data quality when comparing shift averages and exceptions.
Does NAARU require new cameras?
NAARU is built around existing camera infrastructure. Onboarding checks camera access, positioning, and coverage; some larger deployments may need a small on-site processing device.
Can employees and managers review KPI averages and exceptions?
Yes, service analytics present averages and exceptions with reviewable table-session context. Access depends on the user's assigned role and location permissions.