01 / SENSE
Detect movement
Depth and thermal sensing identify anonymous movement through an entrance or defined zone.
Privacy-first space intelligence
Know the flow.
Run it better.
Palera is building sensing hardware that anonymously counts people and turns traffic into clear operational decisions—without facial recognition.
No facial recognition
On-device processing
Built for real spaces
PALERA / LOCATION 001LIVE
84
people currently inside
327entries today
243exits today
12:38 PMpeak period
+18%traffic vs. last week
Traffic by hourToday
One system, three steps
From doorway movement to useful decisions.
The product is designed to collect only what operators need: counts, direction, occupancy, and patterns over time.
02 / PROCESS
Count at the edge
The device classifies entry and exit events locally without creating a facial identity.
03 / DECIDE
Act on the pattern
A focused dashboard translates traffic into staffing, attendance, utilization, and peak-period insights.
Overhead installation
Built to observe the doorway, not the individual.
The planned enclosure mounts above an entrance so the sensing field can classify directional movement while staying out of the way of customers and staff.
Concept visualization. Final enclosure and sensor configuration may change after testing.
Initial markets
Start where every person through the door matters.
Palera begins with smaller spaces where installation is practical and traffic data can change a decision quickly.
Restaurant traffic intelligence
Understand when guests arrive, how traffic changes by daypart, and whether staffing lines up with real demand—without putting cameras on customers.
PlanStaff around actual traffic
CompareSee location-by-location patterns
ConnectRelate visits to sales periods
Restaurant deployment concept
Operational analytics
See what happened—and what to do next.
Palera’s dashboard is designed to move beyond a door count by connecting traffic patterns to capacity, staffing, and space decisions.
Midtown RestaurantMain entrance · Device PL-001
Illustrative product dashboardUpdated 2 minutes ago
Current occupancy
84
70% of planned capacity
Total entries
327
+18% vs. last Thursday
Total exits
243
Flow balanced after 1 PM
Peak traffic
12:38
42 arrivals in 30 minutes
Avg. occupancy use
63%
Lunch period at 86%
Estimated visit time
46m
4m above typical
Hourly entry and exit traffic
EntriesExits
Occupancy by zoneCurrent estimate
Dining room
86%
Waiting area
42%
Private room
58%
Performance comparisonvs. last Thursday
Traffic+18%Higher
Peak window12–1 PMSame window
Avg. utilization+7 ptsImproved
Designed around privacy
Measure people without identifying them.
Palera’s product direction is intentionally different from camera analytics. The system is being designed to process anonymous spatial signals at the device and send operational counts—not faces—to the dashboard.
01
No facial recognition
The product does not need to know who someone is to understand how a space is used.
02
Edge-first processing
Raw sensor information is intended to be interpreted on the device before useful metrics leave it.
03
Operational data only
Counts, direction, occupancy, and trends give teams value without building identity profiles.
PLANNED ENCLOSURE / CONCEPT VISUALIZATION
EARLY HARDWARE PROTOTYPE / OCT 2026
Currently building
From components to a field-tested device.
Palera is in active prototype development. The controller and sensor multiplexer are powered, connected, and communicating. The next milestone is integrating the first people-detection sensor and validating entry and exit logic in a real doorway.
ESP32-S3 controllerVerified
I2C multiplexerVerified
People-detection sensorNext
University and restaurant pilotPlanned
Pilot model
Simple hardware. Ongoing intelligence.
A starting pilot combines the installed sensing device with access to traffic and occupancy analytics. Final pricing will depend on doorway width, sensor configuration, and number of locations.
Palera device$600
Standard installation$200
Analytics subscription$400 / month
Build the first proof with us
Turn foot traffic into a decision your team can use.
We are preparing early pilots with restaurants and university spaces that want better traffic data without adding facial recognition.