Device queues
Shift changes create a single chokepoint and slow down entry.
For workplaces with 20–500 employees and existing CCTV cameras
Your cameras are already watching. Now make them mark attendance.
AI analytics on your existing entry and exit cameras — real-time attendance, a live who's-in view, and tailgating alerts. No new biometric device. No morning queue.
About 2 minutes · You get your result before we ask for your number.
What the AI layer adds
Live who's-in viewSee entry and exit events as they happen.
Existing camera feedsAssess first; replace only what fails the readiness check.
Tailgating signalsFlag mismatches between people crossing and marked events.
The attendance device trap
A device can record a tap. It cannot always explain what happened at the door.
A low-cost machine looks efficient until queues, missed events and disconnected records start deciding payroll.
Buddy punching
A credential can be shared. A door event should be independently visible.
Fingerprint failures
Dust, wear and wet fingers can make daily attendance unreliable.
No real-time truth
The biggest gap: not knowing who came, who left, and who is still inside.
AI Attendance Readiness Check
Can your existing cameras support live attendance?
Answer five practical questions. The result changes with your camera setup, current process and business need.
0out of 100
Indicative readiness result — not a technical site survey.
INDICATIVE MONTHLY ATTENDANCE LEAKAGE EXPOSURE
Get my Camera Readiness Report
Your useful result is already visible. Add your details only if you want the structured report and assessment follow-up.
UTM values and a generation timestamp are included to give the Micro Solutions team the context behind your assessment.
Camera readiness, without the jargon
Three things decide whether the current camera can do the job.
A logo on the camera does not decide AI readiness. The view does.
01 · FACE DETAIL
Enough pixels on the face
A high headline resolution is not enough if the person appears tiny in a wide frame. The face must be large and clear enough at the crossing point.
02 · ANGLE
Near-frontal entry view
A ceiling camera looking at the top of a head is a poor attendance camera. The site check looks for a consistent, near-frontal face view.
03 · LIGHT
Stable lighting
Backlight, deep shadow and sudden glare reduce reliability. Lighting should be checked at the actual morning and evening shift times.
Face recognition
Removes the need to stop and touch a device, but performance depends on camera view, enrolled images, lighting and operating conditions.
Fingerprint
Can work well at controlled points, but creates a physical queue and may fail for worn, dusty or wet fingers. It does not independently explain a tailgating event.
Solution architecture
From existing cameras to live attendance.
The product is not a camera. It is an assessment, analytics layer, workflow and support system.
STEP 01
Camera readiness check
Verify angle, light, crossing path, network and useful face detail.
STEP 02
AI layer
Select edge or server processing based on camera count and site constraints.
STEP 03
Detect + recognize
Match usable face events against an authorized enrolment set.
STEP 04
Live dashboard
Show who's in, who left, and which events need attention.
STEP 05
Exception alerts
Flag possible tailgating, unmatched people and unusual event patterns.
STEP 06
HRMS + reports
Map verified events to attendance workflows, payroll and review reports.
See it in action
See It in Action
Simulated product demos — see how Micro AI Box works on your existing cameras.
ENTRY LINE
IN 09:02
IN 09:02
IN 09:02
REC · CAM 01
LIVE OCCUPANCY42 in premisesEntry feed · online
Real-time attendance on your existing cameras
SIMULATED DEMOWatch face recognition mark attendance live on an entry camera — no new device.
VERIFIED
REC · CAM 02
Tailgating alerts in action
SIMULATED DEMOSee how the AI flags two people exiting on a single authentication.
Simulations are illustrative of the product experience.
Compare the decision, not just the device
Biometric device vs new AI camera vs existing camera + AI layer.
| Decision factor | Biometric device | New AI camera system | Existing camera + AI layer |
|---|---|---|---|
| Hardware cost | New devices at each point | New cameras and recorder/server | Reuses compatible cameras; compute may still be required |
| Installation disruption | Wiring and device placement | Camera and network replacement | Lower when current views pass readiness |
| Morning queue | Likely at shared device | No stop required | No stop required |
| Proxy-proofing | Better than card; still event-only | Visual identity event | Visual identity event, subject to conditions |
| Real-time visibility | Usually device log | Available with analytics platform | Core dashboard outcome |
| Tailgating detection | Not independently visible | Possible with correct view and logic | Possible after readiness verification |
| Multi-location scaling | Device-by-device | Platform dependent | Central analytics pattern, network dependent |
“No new hardware” applies only where existing camera feeds, network and compute meet the readiness requirement. The assessment must verify this before a proposal.
Why Micro Solutions
Start with the cameras you already have.
Micro AI Box adds AI video analytics to compatible CCTV infrastructure, backed by site assessment, implementation and support from Micro Solutions in Nehru Place, New Delhi.
What Micro Solutions provides
- AI video analytics for compatible existing CCTV cameras
- Site assessment before the solution is specified
- Implementation and support
- Based in Nehru Place, New Delhi
How we approach the assessment
- No fake discounts or urgency
- No absolute “AI recognizes everyone” claims
- No overselling where cameras are absent or unsuitable
- Indicative estimates and assumptions remain clearly labeled
Camera readiness assessment
Start with one entry and exit view.
Identify the first technical risk and decide whether a pilot is justified. Compatible cameras are reused; replacement is recommended only when the existing view fails the readiness check.
Frequently asked questions
Questions a responsible AI attendance proposal should answer.
Where is face data stored?
The final design must document whether face templates are processed at the edge or on a server, who can access them, retention periods, encryption and deletion procedures. The appropriate storage model is determined during the site assessment.
How accurate is it, and how does lighting affect results?
Accuracy cannot be promised from a web page. It depends on face size in frame, angle, lighting, enrolment quality, occlusion and threshold settings. A site pilot should measure actual false matches and misses before rollout.
Will old or low-resolution cameras work?
Some may; some will not. The important measure is usable face detail at the crossing point, not only the camera's stated resolution. A readiness check should inspect sample footage.
How is tailgating detected?
The analytics compares the number and identity of people crossing a monitored line with recorded attendance events. A mismatch becomes an exception for review; it is not automatically proof of misconduct.
What about masks or side faces?
Occlusion and side profiles can reduce recognition confidence. The design should improve camera position and use an exception workflow rather than pretend every event will match.
How does HRMS integration work?
Verified events are mapped to employee records and shift rules through an approved integration method such as an API or scheduled export. The exact scope depends on the HRMS.
What happens if the internet goes down?
An edge-based design may continue local processing and sync later, but this must be engineered and tested for the selected hardware and software. It is not assumed by default.
Why is this better than biometric attendance?
It can remove the device queue and add visual, real-time entry/exit context. It is not universally better: a well-run biometric system may be sufficient when there is no visibility or tailgating problem.