EarthCam, the New Jersey company that rents and installs construction cameras, announced on October 20, 2026, a set of connectors that pass its jobsite detections to general AI assistants. A project team can ask ChatGPT or Claude about deliveries, safety conditions or site activity on a job, and the assistant answers from analysis EarthCam’s own software has already run on the camera imagery. The release also adds seven new detection models, several of them aimed at the safety review and delivery log.
What it does
On many jobs, camera footage gets reviewed after something happens. A superintendent or safety manager scrubs through footage when an incident, a disputed delivery or an owner question comes up, and the answer depends on someone finding the right hour of video.
EarthCam’s software analyzes the imagery as it comes in. According to the release, its computer vision (software that recognizes objects and events in images) runs on EarthCam’s servers and in some cases on the camera itself. The detections land in Control Center 9, EarthCam’s existing project platform.
The connectors are the new piece. They are built on the Model Context Protocol, an open standard that lets an AI assistant request data from outside software. EarthCam says the connector is available as an app in ChatGPT and as a connector in Claude, and that it can be added to other assistants that support the protocol, naming Gemini, Copilot and Perplexity. The assistant reads EarthCam’s finished detections, with links back to the high-resolution images, timestamps and metadata behind each answer.
EarthCam says teams can combine that camera data with weather and IoT sensor readings, plus schedules, budgets and specs from other connected systems. Where the assistant supports scheduled tasks, a team can set it to produce safety reviews, delivery summaries and executive updates on a recurring basis. The people most likely to use it are the superintendent, the safety manager and the project executive who writes the owner update.
The new models, as EarthCam describes them:
- AI Trench Safety Analytics records workers and equipment entering and leaving defined excavation areas.
- AI Housekeeping Hazard Detection flags visible debris, clutter and trip hazards.
- Thermal AI Analytics pairs thermal imaging with object recognition to spot people and activity in darkness.
- AI Delivery Verification recognizes boxes and pallets as they arrive and keeps time-stamped evidence.
- AI Dump Truck Analysis counts truck movements and reads load status.
- Visual Occupancy Analysis and Retail Signage Detection are the other two.
The release also mentions expanded schedule automation that compares planned milestones with observed site conditions.
Evidence so far
The evidence so far is EarthCam’s October 20 announcement, its product pages and one customer comment. These claims come from EarthCam:
- Its AI analyzes jobsite imagery ten times faster than a customer’s own AI agent can interpret the raw images.
- Agents working from EarthCam’s results use fewer AI usage credits than agents processing the images themselves.
- In EarthCam’s benchmark testing, its models identified construction assets and objects more accurately than leading general-purpose models on the same images, with no hallucinations, the AI term for output a model makes up, such as a reported pallet that was never delivered.
- Its models were trained on billions of construction images from its own cameras over three decades.
The image set, the scoring method and the accuracy rates from that benchmark are not yet public.
The customer comment comes from Scott Travis, director of northern operations at GBR Roofing. His crew used EarthCam’s detection to set exclusion zones around fall-through hazards while replacing rusted roof panels, and got an alert when someone crossed into one so they could check that fall protection was in place. “AI will alert you immediately when someone approaches a fall hazard,” Travis said in the release. His example describes the exclusion-zone alerts inside EarthCam’s platform.
How to measure it on your projects
Start by testing whether the detections are correct, since every answer the assistant gives depends on them. Track the confirmed alert rate: the share of EarthCam alerts or detections that a person, checking the linked image, agrees are correct. Run it per model, because trench entry and delivery recognition can perform differently on the same job.
For a time measure, track the hours a project executive or superintendent spends preparing the weekly owner or safety update. Capture the baseline before turning on scheduled summaries:
- For four weeks, log the start and end time of each weekly update, including time spent pulling photos and footage.
- Count the camera-related questions from the owner or the safety manager that week, and how long each took to answer.
- For delivery verification, pull the delivery tickets for two of those weeks so you can later check each one against a camera detection.
During the pilot, log every summary the assistant drafts and every correction made before it goes out. Earlier posts covered viAct’s PPE detection on Alliad’s site cameras and Miter’s safety reports built from crew photos, and the confirmed alert rate works for comparing camera tools side by side.
A first step is to pick one camera on one job, pull a week of its detections and check twenty of them against the images.
Availability and cost
EarthCam says the connectors are available now to its customers through ChatGPT and Claude. Pricing is not listed on EarthCam’s product pages, which direct buyers to schedule a demonstration. Custom detection models are available on request from its data science team.