Insights, guides, and tools to help you plan, deploy, and scale video-to-data systems.
[01]
knowledge
Perspectives on computer vision infrastructure, AI deployment, and real-world video-to-data systems.
Deploying AI-powered video analytics requires more than a model. Use this practical AI readiness checklist to evaluate your infrastructure, data governance, performance expectations, and operational alignment before turning video into structured data.
Turning video into structured data is harder than it looks. Here are the most common video analytics pitfalls — from model drift to infrastructure cost overruns — and how to design systems built for real-world deployment.
DDI bridges computer vision and GIS, transforming static maps into real-time spatial intelligence layers—without the compute and storage burden of traditional video analytics.
Parallax vision rethinks traditional video analytics by extracting structured intelligence at the source. By eliminating continuous video processing, it reduces compute, storage, and energy costs while enabling real-time, privacy-first computer vision systems.
[02]
guidance
DDI Eidetic is designed to integrate seamlessly into modern AI and geospatial data pipelines by converting raw video into structured, machine-readable data. The platform extracts geospatial and object-level information from video streams and can deliver that data in real time to mapping platforms such as Esri ArcGIS or to databases like PostgreSQL, where it can be accessed by applications such as QGIS.
Eidetic also produces AI-ready outputs that can be used to train and enhance computer vision models, including open-source frameworks like YOLO. This allows organizations to continuously improve object detection and classification using real-world video data.
By streaming structured data from live camera feeds into mapping systems, databases, and AI models, Eidetic enables organizations to analyze and visualize real-world activity in real time—making video a scalable, actionable data source for analytics, automation, and decision-making.
Yes—Eidetic is built to scale.
Each instance of Eidetic processes video streams to extract structured data, and additional instances can be deployed easily to support more cameras or feeds. This modular architecture allows organizations to scale from a single video source to large networks of cameras without redesigning their infrastructure.
Eidetic instances can also switch between an unlimited number of video connections to extract data sequentially when needed. With sufficient computational resources, the platform can be configured to process multiple video streams simultaneously, enabling real-time data extraction across large video environments.
In short, Eidetic scales with your infrastructure—whether you’re analyzing a handful of cameras or thousands of live feeds across a distributed network.
DDI is designed with privacy in mind. The platform does not collect, store, or process personally identifiable information (PII). Instead, Eidetic analyzes video from publicly available sources—such as traffic and infrastructure cameras—and converts visual information into structured, anonymized data about objects, movement, and environmental conditions.
Because the system focuses on object-level data rather than individual identities, the outputs contain no personal data. This approach allows organizations to generate valuable insights from video while maintaining strong privacy protections and minimizing regulatory risk.
Additionally, Eidetic can be deployed within secure environments and integrated with existing data governance frameworks, ensuring that organizations maintain full control over how video data is processed, stored, and accessed.
DDI delivers a simpler, more practical approach to computer vision. While many AI solutions rely on complex, resource-intensive pipelines, DDI focuses on turning video into structured, real-time data that organizations can immediately use for analysis, automation, and decision-making.
By converting live video streams into geospatial and object-level data, DDI enables organizations to unlock the value of existing camera infrastructure—without the need for complicated AI deployments.
The result is faster implementation, lower operational complexity, and more scalable insights from video.
This approach allows industries such as transportation, infrastructure, and smart cities to transform everyday video feeds into actionable intelligence, powering everything from real-time monitoring to long-term data analytics.
[03]
assessment
A questionnaire to help you evaluate and choose the right computer vision & data partner for your AI goals
This guide is designed to help you assess key capabilities, alignment, risks, and success factors when selecting a computer vision partner — with a focus on real-world deployments, performance, and trust.
