Botkernel provides an AI‑driven computer‑vision platform that extracts structured insights from video and image streams. It offers Automatic Number Plate Recognition for security and law‑enforcement, brand identification from photos, and retail analytics such as footfall counting, dwell time, heatmaps, and demographic inference, all deployable on edge devices or scalable GPU servers via APIs and dashboards.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations that rely on visual data—such as surveillance cameras, storefront images, or user‑generated photos—often lack automated tools to extract actionable information in real time. Manual review is time‑consuming, error‑prone, and limits the ability to respond quickly to security incidents, understand shopper behavior, or identify brands in images.
Solution
Botkernel delivers an AI‑driven computer‑vision platform that turns raw video and image streams into structured insights. Using pretrained deep‑learning models, the service performs Automatic Number Plate Recognition for law‑enforcement and security use cases, identifies brands from any photograph, and generates retail analytics—including footfall counts, dwell times, heatmaps, and demographic attributes such as age, gender, and emotion. The solution runs on edge hardware like NVIDIA Jetson Nano for low‑latency processing and can scale to multi‑stream GPU servers for larger deployments. Processed data are exposed via APIs and dashboards, enabling customers to integrate insights directly into existing workflows and decision‑making tools.
Target Audience
Primary customers include law‑enforcement and security agencies needing automated plate detection, retailers seeking in‑store shopper behavior analytics, and brands or e‑commerce platforms that want instant visual brand identification from user‑generated content.
Features
- ANPR engine that detects and reads vehicle license plates from live video with high accuracy
- Brand‑recognition model that matches logos and product visuals from a single image snapshot
- Retail analytics suite offering footfall counting, dwell‑time measurement, heat‑map generation, and demographic inference (age, gender, emotion)
- Edge‑optimized deployment capable of running on devices such as Jetson Nano, with optional GPU‑server scaling for multiple concurrent streams
- RESTful APIs and web dashboards for real‑time monitoring, reporting, and integration with third‑party systems
- Continuous model updates powered by machine‑learning pipelines to improve detection rates across diverse environments