Organizations today are surrounded by cameras, namely, CCTV cameras, capturing vast amounts of video data every second. Yet despite this abundance, extracting meaningful video analytics insights remains a challenge. Most systems still require teams to manually review recordings or rely on after-the-fact alerting. But, is it really feasible to manually comb through hundreds or thousands of hours of footage?
The big issue here is the lack of accessibility. Teams can store and monitor surveillance footage, but still struggle to analyze video footage efficiently or turn it into actionable insights. This is where a new shift is emerging and where we coined the term: queryable cameras.
What are Queryable Cameras?
Let’s first break down the concept. Queryable cameras are camera systems that let users interact with video footage more like searchable data instead of passive recordings. Instead of relying only on fixed rules, predefined alerts, or manually reviewing clips, users can search video footage, analyze video content, and retrieve relevant information based on what they want to know. This is a move toward systems that can surface actionable insights from visual data on demand.
Users might ask simple, intent-driven queries or prompts such as “Show all unauthorized access events after 6 PM” or “Where were potential safety risks detected this week?” Instead of navigating complex dashboards, they can prompt the system in plain language and instantly search video footage or analyze video content to get the answers they need.
Powered by artificial intelligence, or Vision-Language Models (VLMs), and increasingly AI-powered video workflows, queryable cameras make it possible to ask more flexible questions of video instead of only receiving static outputs. These systems help users analyze video footage, detect patterns, and uncover potential threats or operational signals hidden across large volumes of surveillance footage.
This is what makes intelligent video analytics feel more interactive: users are no longer limited to what a system was originally configured to detect. They can explore, investigate, and extract video analytics insights in a way that feels closer to video search than traditional monitoring.
At a broader level, queryable cameras represent a shift in how organizations use video security infrastructure. Cameras become part of a more dynamic system for processing video, helping teams analyze visual information faster and make better decisions. In that sense, queryable cameras are not just a hardware upgrade. They reflect a new way of thinking about how video analytics enables people to work with video as an accessible source of intelligence rather than a passive archive.
Do AI Models Still Matter?
In short, outcomes matter more than incremental model improvements. For years, progress in artificial intelligence and video analytics systems has been measured by the performance of AI models: better accuracy, fewer false positives, and more reliable real-time alerts. But as AI-powered video continues to mature, that progress is starting to plateau.
In many environments, the ability to detect objects, behaviors, and potential threats across video footage is no longer the limiting factor. Incremental gains in model accuracy are becoming less impactful compared to the broader challenge of actually using that data effectively.
What matters now is what happens after detection. The real value lies in how quickly teams can analyze video footage, extract actionable insights, and turn them into decisions.
Instead of focusing solely on model performance, users are prioritizing systems that enable users to search video footage, analyze video content, and drive end-to-end outcomes. As a result, the future of intelligent video analytics won’t be defined by better models alone, but by how seamlessly video analytics enables proactive, insight-driven workflows.
Video Without Usable Insights
For years, video analytics systems have been built around detecting predefined events like motion, intrusion, or specific triggers (i.e., PPE compliance or detecting the presence or lack thereof of hard hats and safety vests). In more advanced environments, this extends to capabilities like license plate recognition or anomaly detection. But even with these advances, the underlying limitation remains the same: systems only surface what they are explicitly configured to find.
Organizations feel this disconnect as they collect more video footage than ever before, yet process and extract only a fraction of its value. Even with intelligent video analytics, the process of gaining insights remains reactive. Teams receive real-time alerts, but those alerts rarely provide the context needed to understand broader patterns or make informed decisions.
Across industries, this often results in teams falling back on manual processes just to analyze video footage in full. In practice, most workflows still look something along the lines of:
- Reviewing hours of surveillance footage to find a specific event
- Cross-referencing multiple video surveillance systems without a unified view
- Attempting to analyze video content after incidents have already occurred
The data is there, but teams are still missing the ability to efficiently analyze visual information.
How Teams Gain a Real Understanding
When organizations can move beyond passive monitoring and start actively engaging with their video data, the role of cameras changes entirely. Instead of asking what was captured, teams can begin asking what it means.