What is EVA?
EVA is a vision AI platform that transforms standard network cameras from simple video capture devices into intelligent AI cameras capable of understanding and making decisions about on-site situations. By orchestrating Vision Models (VM), Vision-Language Models (VLM), and Large Language Models (LLM) within a unified runtime environment, EVA enables users to build purpose-driven vision AI services without having to design complex AI pipelines themselves.
The most distinctive feature of EVA is that users only need to describe what problem they want to solve in natural language—EVA automatically takes care of the rest. Instead of writing code or manually selecting models, users simply explain the detection targets, conditions, and scenarios through a conversational interface. EVA then automatically designs and executes the appropriate model combinations and inference flows.
Through this approach, EVA goes beyond basic object detection to deliver a field-oriented AI system capable of situation understanding, behavior analysis, and context-aware decision-making.
For more details, visit the EVA Overview Page.
Key Features
The core value of EVA lies in dramatically simplifying the deployment and operation of vision AI.
Users can define detection targets and conditions using natural language commands such as: “Detect people in this area” “Notify me when a worker is not wearing a safety helmet in this zone”
EVA interprets the intent of each request, selects the appropriate vision models, and automatically configures the necessary inference steps and decision criteria.
EVA also continuously optimizes its behavior in response to changes in the on-site environment. False positives and false negatives caused by camera angles, lighting conditions, or workflow changes are improved through user feedback. This process does not require large-scale data collection or complex labeling, allowing accuracy to improve incrementally over time.
Rather than relying on a single model, EVA dynamically combines object detection, tracking, scene understanding, and language-based condition interpretation as needed. This flexible approach enables stable and reliable vision AI services across a wide range of industrial environments.
EVA can be applied to various use cases—including safety management, quality inspection, anomaly detection, and physical security—and is designed to scale flexibly according to on-site requirements.
User Scenario
The process of deploying and using EVA is straightforward. Operators simply register the network camera address and basic information, and EVA automatically recognizes the device and converts it into a smart AI camera.
Users then interact with EVA through a conversational interface by submitting requests such as: “Detect people not wearing safety helmets in this area” “Notify me when abnormal behavior occurs in this space”
Based on the request, EVA proceeds as follows:
- Detects objects and situations using vision models
- Interprets scenes and behaviors using VLMs
- Aggregates conditions and context using LLMs to reach a final decision
If the results do not meet on-site expectations, users can provide simple feedback. EVA incorporates this feedback to refine future inference processes and decision criteria, gradually forming AI behavior optimized for the specific environment.
With this structure, EVA enables organizations to operate advanced vision AI services directly on-site—without requiring AI specialists or complex development workflows.