COMPUTER VISION
Computer vision solutions that turn images and video into decisions.
We build systems that read images and video to automate inspection, spot events, measure flows, and generate operational alerts.
DIRECT ANSWER
What is computer vision?
Computer vision applies AI to images and video to recognize objects, patterns, text, events, and changes that can support or automate decisions.
Who it is forManufacturing, logistics, retail, security, agribusiness, healthcare, and operations that depend on visual inspection or monitoring.
CONTEXT
What computer vision is and where it creates value
Feasibility depends as much on the camera and the environment as on the model. Lighting, placement, resolution, occlusion, and process variation have to be part of the design from the start.
We build proofs with representative data and metrics tied to the operation. When latency or connectivity require it, part of the inference can run at the edge.
eSight.AI shows what we can do turning video into events and signals wired into dashboards, alerts, and corporate systems.
IN DEPTH · 1/4
What computer vision can do
Detection and classification
Identifies objects, people, vehicles and situations in images and video, and classifies them by type.
Counting and tracking
Follows each item through the video, counts crossings and measures flow, direction and time spent.
Text reading (OCR)
Reads plates, labels, serial numbers and documents in images, with validation depending on capture quality.
Quality inspection
Detects defects, damage or out-of-standard items on production lines and packaging.
Occupancy analysis
Shows when areas and workstations are occupied, to investigate how space is used and how work is organized.
IN DEPTH · 2/4
Computer vision applications by industry
Industry and workplace safety
Protective equipment checks, area monitoring and inspection support, with people reviewing what was flagged.
Events, retail and audience experience
Flow, occupancy, dwell time and engagement, as in the event analytics projects we have delivered.
Logistics, gates and traffic
Vehicle counting by type and direction and plate reading to support access planning.
Mobility and field surveys
Video processing and vehicle classification for mobility surveys and intelligence.
Physical security
AI video security on existing cameras, with rules, alerts and reports defined with the operation.
IN DEPTH · 3/4
Each stage in detail: data, model, validation and monitoring
The result depends more on the scenario and the data than on the model chosen. The process reflects that.
Scenario and success criteria
We define the event to detect, the environment, the camera and how success will be measured, before any model.
Data and annotation
We collect representative samples from your environment and annotate them, because lighting, angle and context change the result.
Model and training
We adapt or train detection models, including the YOLO family, to fit the problem, and measure hits and false alarms.
Validation on your footage
We test on footage from your environment before expanding, and only then set thresholds and alert rules.
Integration and alerts
The result reaches the people and systems that need to act through SMS, email, dashboards or APIs.
Monitoring and tuning
We track quality over time, because the environment changes and the model has to be re-evaluated.
IN DEPTH · 4/4
Cameras, hardware and privacy in computer vision projects
Cameras you already have
It is often possible to use existing cameras. We assess resolution, position, lighting and image stability before recommending any replacement.
Where to process
Analysis can run near the cameras, on your own server or in the cloud, depending on cost, speed and privacy.
Images of people and privacy
Detecting a person is not identifying them, but images of people can still be personal data and need a legal basis, a purpose and defined retention. Identifying individuals only comes into play with a real need and matching controls. This is not legal advice.
Limits and human review
Every model makes mistakes. We keep human review on important decisions and are clear about what the solution does well and where it fails.
For security and operations cameras, see also our AI video security solution, with video demonstrations.
OUTCOMES
What a computer vision solution delivers
Technical goals only matter when they improve security, speed, cost, experience or the ability to decide.
- 01Automated inspection
- 02Object and event detection
- 03OCR and visual reading
- 04Counting and tracking
- 05Real-time alerts
- 06Integration with the operation
WHEN IT MAKES SENSE
When computer vision makes sense
- People review images by hand
- Defects have to be identified
- Events on video go unnoticed
- There are cameras with no operational intelligence
- The response has to be real time
- Off-the-shelf models do not work in the environment
HOW WE WORK
How we build a computer vision solution
Short stages, visible criteria and knowledge transfer at every decision.
Scenario
We define the event, the environment, the camera, and the success criteria.
Data
We collect and label representative samples.
Model
We train or adapt the approach and measure performance.
Integration
We deploy, monitor, and connect to alerts and systems.
DELIVERABLES
What you receive in a computer vision project
- Feasibility study
- Labeled dataset
- Vision model
- Video pipeline
- API or edge runtime
- Dashboard and alerts
TRACK RECORD
Computer vision in real projects
Video analytics for events, mobility intelligence and our own computer vision platform, with more than 170 projects delivered.
- 10+
- years in business and Google partnership
- 170+
- projects delivered
- 97%
- NPS
Engagement intelligence at large-scale events
eSight.AI turned event footage into an objective read on audience attention, distraction and response across the talks.
See the project ↗Hard Rock Cafe CuritibaAnalytics and operational intelligence for events
Cameras that mostly served security now produce indicators for foot traffic, occupancy, dwell time and engagement across event operations.
See the project ↗SAGUIAn end-to-end platform for mobility surveys and intelligence
An ecosystem that brings together offline field collection, origin-destination surveys, video processing, vehicle classification, dashboards and reports.
See the project ↗FREQUENTLY ASKED QUESTIONS
Frequently asked questions about computer vision for enterprises
What is computer vision?
It is the field of artificial intelligence that teaches computers to interpret images and video: recognizing objects, people, text and situations, counting and following items and producing information that supports decisions.
What are enterprise applications of computer vision?
The most common are quality inspection, counting and flow of people and vehicles, reading plates and documents, checking protective equipment and analyzing area occupancy, plus AI video analytics on security cameras.
How much does a computer vision project cost?
It depends on the number of cameras, the scenarios, how much data needs annotating and the integrations. We start with a bounded test on footage from your environment and provide an individual proposal with scope, timeline and investment.
How long does it take to build a computer vision solution?
It depends on data, cameras, hardware and integrations. We start with an initial test on footage from your environment and the production solution evolves in cycles as validation proceeds, with the timeline set after the test.
Do we have to train a model from scratch?
Not always. Existing models can be adapted, but the environment and the classes have to be validated with real data.
Does the solution work in real time?
It can, depending on resolution, number of cameras, hardware, model, and the latency you need.
Can we use our existing cameras?
Often yes. We assess protocol, resolution, placement, lighting, and stability before recommending any change.
How do you avoid false alerts?
We use representative data, context-specific thresholds, temporal rules, validation, and drift monitoring.
FIELD EVIDENCE
AI evaluated rigorously and wired into infrastructure.
Reproducibility, LLM evaluation, data, agents and operations are treated as parts of one system.
Artificial Intelligence
MLRC 2025 at Princeton: five principles for reproducible LLM evaluation
Five EAGLE BS lessons from MLRC 2025 at Princeton AI Lab on benchmarks, determinism, data, transparency, and robust LLM evaluation.Read article ↗
Google Cloud
Google Cloud Next 2025: AI, agents, and the new digital infrastructure
EAGLE BS at Google Cloud Next and Partner Summit 2025: Agentspace, Gemini 2.5, agents, cloud, data, and practical lessons for customers.Read article ↗TALK TO A SPECIALIST
Tell us the situation. We help you see the best path.
A focused conversation to understand context, risk, priority and the first workable step.