Your cameras are already recording everything.
Nobody's watching it.
Brelisk watches your security footage and tells you what happened and why, in plain language, so nobody has to sit and scrub through hours of video.
Where this stands today
An early-stage R&D project, not a shipped product.
Brelisk pairs deterministic computer-vision tracking with a reasoning layer to turn ordinary security-camera footage into structured, explainable operational insight for physical retail. We're building and testing this incrementally, starting with one well-defined use case, checkout queue and dwell-time analysis, before expanding to the full metrics set further down this page.
Right now it's a working prototype, tested on recorded retail footage. This isn't a live deployment. The waitlist is for people who want to follow progress or talk about an early technical pilot, not sign up for a live product.
Sales dropped.
Why?
No one knows.
Customers entered.
Where did they lose interest?
Unknown.
Checkout lines formed.
How much revenue was lost?
Unknown.
Store A outperforms Store B.
What operational difference explains it?
No evidence.
Traditional analytics measure activity. BRELISK explains it.
How it's built
Two layers, split by how fast each one needs to run.
Perception layer
Fast, continuous: runs on every frame
- YOLO-family object detection finds people and relevant objects in each frame.
- A tracker from the ByteTrack / BoT-SORT family assigns each person a persistent ID across frames.
- Zones are defined per camera (checkout lanes, product displays, entrances) so dwell time, entry/exit, and occupancy can be measured directly from tracked positions.
Reasoning layer
Slow, selective: triggered only on flagged events
- Doesn't run on every frame. That would be too slow and too costly.
- The perception layer flags a moment worth explaining (an unusually long dwell time, a queue building up) and crops the image around it.
- A vision-capable language model turns that crop (raw geometry, boxes, tracks, durations) into a plain-language explanation of what's likely happening.
How BRELISK Thinks
Observe
Detect people on every frame and track each one with a persistent ID.
Understand
Measure dwell time, queue length, and movement inside defined zones.
Explain
Flag unusual moments and describe, in plain language, what's likely happening.
Recommend
Turn that explanation into one specific, actionable next step.
Where we are
A build log, not a features list. Here's what's actually working today versus what's still ahead.
Sample output format: recorded test clip, illustrative only
people_tracked : 42
walking : 31
standing : 11
zones.checkout.dwell_avg : 47s
zones.checkout.dwell_max : 3m 12s
zones.apparel.occupancy_peak : 6
[reasoning] flagged: prolonged dwell, zone=checkout, track_id=17
[reasoning] explanation: "Person paused near the register for an
extended period, consistent with queue hesitation."What Brelisk is designed to measure
This is the long-term direction. Active work is focused entirely on the two categories below.
Zones
How long customers linger in a zone
How many people are in a zone right now
Rate customers enter a specific zone
Queue
Number of people currently waiting
How long customers wait in line
Share of customers who leave the queue
Every metric above describes what happened. Brelisk is being built to go further: a plain-language explanation and a specific recommendation.
The goal
Most tools tell you what happened. The goal here is to explain why.
Common questions
Do I need new cameras?
No, Brelisk is designed to run on your existing IP camera feeds. Right now we're validating detection, tracking, and reasoning on recorded footage; live camera integration is next on the roadmap.
How is this different from a heatmap tool we already have?
Most heatmap tools show where people walked. Brelisk is built to track individual behavior over time (hesitation, queuing, abandonment) and turn it into a plain-language explanation and a specific recommendation, not just a colored map.
What about customer privacy?
Brelisk tracks movement and behavior, not identity. Privacy is a core design principle we're building in from day one, not yet an audited or certified guarantee, but a constraint the architecture is designed around.
When can I actually use this?
We're not yet deploying to live sites. Brelisk is currently a working prototype, tested on recorded retail footage. If you'd like to be an early technical pilot partner, reach out through the waitlist form below.
BRELISK is building the intelligence layer for physical retail.
Starting with the cameras you already have.
For people following our progress or exploring an early technical pilot, not a signup for a live product.