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Gezora.ai

Turn what your cameras see into something you can act on

Gezora builds computer vision systems that read images and video from your own operation, so inspection, counting, and monitoring stop depending on someone watching a screen.

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From camera to alert

  • Camera placement review
  • Image dataset assembly
  • Model training
  • Object detection
  • Image classification
  • Counting and measurement

What a vision system does

  • Models trained on your footage
  • Continuous detection and counting
  • Testing in field conditions
  • Results your team can act on

What the system carries for your team

  • Coverage on every shift

    Counting and monitoring run continuously, so coverage does not depend on who is on shift.

  • Usually no new cameras

    We start from the footage and hardware you already have, and recommend changes only where quality limits results.

  • Accuracy in your conditions

    Models are trained on your footage and tested in the lighting and weather onsite.

  • Findings reach your team

    Alerts reach your team the moment something needs attention, and results feed your systems.

From camera to alert

  • Camera placement review
  • Image dataset assembly
  • Model training
  • Object detection
  • Image classification
  • Counting and measurement
  • Field conditions testing
  • Anomaly detection
  • Defect classification
  • Zone and boundary rules
  • Text extraction from images
  • Alerting rules
  • Operational dashboard
  • System integration

What a vision system does

Every vision system is built from your own footage, your own categories, and the conditions your cameras actually work in.

  • Models trained on your footage

    We build the image set from cameras already running on your site rather than a public dataset. Training and validation use your own conditions, so the model is judged on the footage it will actually see.

  • Continuous detection and counting

    The system finds what is in frame, sorts it into the categories your operation cares about, and counts or measures as it goes. That runs continuously, so quality inspection and presence checks no longer wait for a spare pair of eyes.

  • Testing in field conditions

    A model that only performs in ideal light is not a working system, so we test against the angles, lighting, and weather the cameras face. Where image quality genuinely limits what a model can do, we say so and recommend the change.

  • Results your team can act on

    Alerts go out the moment something needs attention, and an operational dashboard shows the trend rather than only the incident in front of you. Results feed back into the systems your team already works in, so findings land where the work is already happening.

From footage to live detection

Work moves from the footage you already have to a system your team relies on, with each step proving itself before the next one starts.

  1. 01

    Step 1, Footage review

    We look at the cameras and footage you already have and what they can realistically support.

  2. 02

    Step 2, Dataset build

    Images from your site are assembled and labeled into the categories your operation needs to distinguish.

  3. 03

    Step 3, Training and testing

    Models are trained on that set and tested in the lighting, angles, and weather onsite.

  4. 04

    Step 4, Integration

    Detection results are wired into your alerts, your dashboard, and the systems your team already uses.

  5. 05

    Step 5, Live review

    Once running, results are reviewed against real cases and detection and alerting are adjusted as conditions change.

Who is checking by eye today

Vision work suits teams where someone is currently checking, counting, or reading values off a screen.

  • Quality teams inspecting parts by eye

    Inspection depends on who is on shift and how much attention is left by the end of it. Detection and defect classification run continuously against the categories your team already uses, so the check keeps running rather than covering whatever sample someone had time for.

  • Operations teams counting stock or movement by hand

    Counts are made by walking the floor or scrolling back through footage, and the figure is only as fresh as the last person who looked. Counting and measurement run from the cameras already on site, and the dashboard shows the trend rather than a single reading.

  • Safety teams watching a wall of feeds

    Watching every feed at once is not possible, so incidents are usually found after they happened. Zone and boundary rules raise an alert the moment something crosses where it should not be, and that alert reaches your team instead of sitting in a recording nobody reviews.

  • Back office teams keying data from images

    Someone reads labels, plates, or paperwork off a photo and types the values into a system. Text extraction pulls those values from the image and feeds them into the systems your team already works in, so the record arrives with the work instead of after it.

What lands with your team

What lands with your team is the work itself: the images, the model, the rules, and the screens your team watches.

  • A review of your cameras and footage
  • A labeled image set from your site
  • The trained model built on that set
  • Test results from the conditions onsite
  • An operational dashboard your team watches
  • Alerting rules wired into your systems

Every item above is built from your own cameras and your own categories, and once detection is live we review results against real cases with your team and adjust as conditions change.

FAQ about Computer Vision

Straight answers on scope, timelines, and what running Computer Vision asks of your team.

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Stop paying people to do what an agent can

Tell us what you want to automate. We will map the workflow, deploy the right agents, and train your team to run them.

  • Every agent is trained on your own workflows, never a generic template
  • Most deployments are live within two to four weeks
  • SOC 2 compliant, with a complete audit trail on every deployment
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