Our Story
CCTV was already watching. We wanted it to speak up.
A fire can already be visible through a camera while the people responsible still do not know.
At a smaller site, nobody may be watching the screen at that exact moment. The camera records everything, but the footage becomes useful only after someone discovers what happened.
At a larger facility, alarms, cameras and control rooms may already exist. Yet the team can still lose valuable time locating the right camera and understanding where the problem is.
If fire is already visible, it should not remain unnoticed.
Rajkot · A problem we could not ignore
Knowledge created a responsibility to act.
A devastating fire tragedy in our hometown of Rajkot made this problem impossible for us to ignore.
Children and families lost their lives in an accident that felt avoidable. We could not change what had already happened, and we were not in a position to control policy or hold anyone accountable.
But AI and computer vision were within our ability.
Same site · Same cameras · Different outcome
A camera that only recorded yesterday can alert today.
The first working fire detections came surprisingly quickly. The moment that continues to surprise customers is simple.
Visible, but only recorded
A small controlled flame appears within an existing camera view. If nobody is watching at that moment, the footage waits to be discovered later.
Visible, detected and alerted
AI Bot Eye connects within the existing CCTV network, near the DVR, NVR or control-room infrastructure. The same visible event can trigger an alert within seconds.
The shop floor looks exactly the same. What changed is what the system can understand.
Detection was only the beginning
A demonstration model is not enough. Trust has to survive reality.
The system must work inside factories, warehouses, spinning mills, offices and other environments where every camera sees something different.
Every site must be understood
Lights, reflections, machinery, vehicle taillights, dust and other recurring conditions can sometimes resemble fire or smoke.
Before live alerts begin, AI Bot Eye learns the environment of each selected camera. This helps the system become familiar with what is normal for that location.
The same false alarm should not keep returning
No AI system should pretend to be perfect. But when a customer marks an event as false, AI Bot Eye remembers the camera, shape, location and other visual characteristics associated with it.
It does not keep repeating the same known false alarm—because repeated mistakes become noise, and people stop trusting noise.
An SMS alone is not enough
The recipient still needs to know which site, which camera and what exactly was visible.
That led us to image-based WhatsApp and dashboard alerts with the site name, camera name, time and event image through channels the responsible person already uses.
Every organisation responds differently
A large industrial facility may want a hooter immediately. A hotel may want trained review before a loud public alarm. Others may want SMS, WhatsApp, dashboard alerts or senior escalation.
AI Bot Eye is configured around the customer’s actual response process rather than forcing every site into the same workflow.
Trust begins before the fire
AI can only monitor what the camera can see.
During deployments, we repeatedly found cameras that were offline, obstructed, producing blank frames or delivering unusable video—often without senior management knowing.
Clarity before confidence
The dashboard can show when a camera became unavailable, when it recovered and whether its view was obstructed or corrupted.
Availability
Know whether the selected camera stream is actually reaching the system.
Clear view
Identify blank, blocked or obstructed views that compromise monitoring.
Image quality
Surface corrupted or unusable video before it silently becomes a blind spot.
If a camera is expected to act as part of an early-warning system, the organisation should know whether that camera is actually ready.
Built in real environments
The system matured where it had to work.
Early customers gave us the operating conditions, repetition and trust required to move beyond a working model.
Repeated evaluation at Rolex Rings
Their team allowed multiple facility visits, demonstrations and testing under real operating conditions.
Industrial learning in spinning mills
Cotton dust, large operating areas and significant fire risk helped shape a system for difficult real-world conditions.
New modes earned through trust
At Rolex Rings, customer trust in fire detection led to stack-light monitoring for important manufacturing workflows.
Genuine · Unplanned · Visible events
Proven in everyday operating environments.
AI Bot Eye has identified multiple genuine, unplanned flame events at deployed sites.
Sparks and flame from cutting, grinding and other hot work
Trash being burned near warehouse areas
Small outdoor fires visible through site cameras
Roadwork and other unexpected flame activity near monitored premises
Fortunately, these events did not develop into major disasters. But they proved the system was not only working during planned demonstrations.
Designed to remain with the customer
Core video intelligence can operate inside the facility.
AI Bot Eye can operate locally inside the customer’s facility. Core video processing does not need to depend on continuously sending camera footage to an external cloud platform.
Customers can purchase the system as on-site infrastructure, including the required hardware and licences, without being forced into a recurring annual software licence for the core system.
Optional support and maintenance can continue through AMC arrangements.
- Footage can remain within the campus
- The system can continue operating locally
- The investment becomes customer-owned safety infrastructure
- No required per-camera cloud subscription for the core system
A practical path to deployment
From a suitable camera view to live monitoring.
The process starts with the customer’s real risks, real cameras and real response requirements.
Identify the risks
We understand which areas and fire risks matter most to the customer.
Check the existing cameras
We evaluate suitable views, visibility, angles and stream availability.
Let the system learn the site
AI Bot Eye adapts to each selected camera’s environment before live alerts begin.
Test and go live
We conduct controlled demonstrations, configure alert channels and begin monitoring.
Customers may begin with a pilot or directly deploy one device before expanding across additional cameras, areas or sites.
From finding out later to knowing sooner
An additional early-warning layer for visible fire.
AI Bot Eye does not replace certified fire-detection systems, fire panels, sprinklers or emergency-response procedures. It works alongside them using suitable existing CCTV views.
Greater confidence that visible fire is less likely to remain unnoticed.
A system that has been tested and adapted to the actual site.
The site, camera and evidence needed to understand the event faster.
More value from the CCTV infrastructure it already owns.
See AI Bot Eye work at your site.
Request an on-site fire-detection demonstration using suitable existing CCTV cameras. We will review the relevant camera views, conduct a controlled live demonstration and recommend a practical path for pilot or deployment.