Aivis provides an AI‑powered platform that automates video ingestion, processing, and analysis, delivering real‑time structured metadata for use cases such as object detection, activity recognition, and anomaly detection. The solution offers low‑code pipeline configuration, REST/SDK integration, and scalable deployment on cloud, on‑premise, or edge devices, enabling enterprises to replace manual video review with automated insights.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises that rely on video surveillance, production line monitoring, or media analytics often face high labor costs and latency when extracting actionable information from raw footage. Manual review is error‑prone and scales poorly as video volumes increase, limiting real‑time decision making and operational efficiency.
Solution
Aivis delivers an AI‑driven platform that automates video ingestion, processing, and analysis across diverse computer‑vision use cases. The system leverages pretrained and custom deep‑learning models to detect objects, track movements, recognize patterns, and generate structured metadata in near real time. Users can configure pipelines via a low‑code interface or integrate directly through RESTful APIs and SDKs for programmatic control. Processed insights are streamed to dashboards, alerting systems, or downstream data warehouses, enabling immediate operational responses. The platform supports both cloud‑scale processing and edge deployment for latency‑sensitive environments, ensuring flexibility across on‑premise and hybrid architectures.
Target Audience
Primary customers are mid‑size to large enterprises in manufacturing, security & surveillance, retail analytics, and logistics that need automated video insight generation to optimize operations and reduce manual monitoring overhead.
Features
- End‑to‑end video pipeline: ingestion, transcoding, AI inference, and metadata export
- Library of pretrained models (object detection, activity recognition, anomaly detection) with option to upload custom TensorFlow/PyTorch models
- Scalable compute orchestration using containerized microservices on Kubernetes or serverless runtimes
- Real‑time streaming of inference results via WebSocket, MQTT, or Kafka connectors
- SDKs for Python, JavaScript, and C++ plus REST API for batch and streaming workloads
- Edge runtime that runs inference on NVIDIA Jetson, Intel Movidius, or ARM AI accelerators
- Role‑based access control and end‑to‑end encryption for video data in transit and at rest
- Visual analytics dashboard with configurable alerts, heatmaps, and export to CSV/JSON