Computer vision
Detection, tracking, recognition and OCR, running on your hardware.
A working prototype on your own footage in 1 to 3 days. Getting accuracy where a miss is expensive, and deploying to real hardware, runs weeks to months.
Build a computer vision system
Feasibility on your actual footage first, the honest answer is sometimes no. Then model selection and training on your data, a pipeline that holds frame rate on the hardware you have, thresholds tuned to what a miss actually costs, and a human review queue for the ambiguous cases.
The problem
You need to detect, count, read, track or identify something on camera, and the off-the-shelf products either do not fit or want your footage in their cloud.
How we approach it
Feasibility on your actual footage first, the honest answer is sometimes no. Then model selection and training on your data, a pipeline that holds frame rate on the hardware you have, thresholds tuned to what a miss actually costs, and a human review queue for the ambiguous cases.
What changes
A system running on your own hardware with no frames leaving site, and a measured false-positive and false-negative rate on your footage rather than a vendor claim.
The service this falls under
AI and machine learning
Training, fine-tuning, retrieval, agents, and knowing when not to.
A working prototype in 1 to 3 days. Training, evaluation and tuning run weeks to months, and a genuine research problem can run to eight. It depends entirely on your data and the target.
Industries where this comes up
Security and defence technology
Vulnerability scanning that runs offline, cyber ranges, bot and abuse defence, and vision systems for sites where footage cannot leave the premises.
Last reviewed · Site changelog