COMPUTER VISION
Computer vision that turns 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 for enterprises?
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
Technical decisions with an operational view.
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.
OUTCOMES
What the initiative has to deliver.
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
Signs that it is time to act.
- 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
From assessment to operations.
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
Clarity on what gets finished.
- Feasibility study
- Labeled dataset
- Vision model
- Video pipeline
- API or edge runtime
- Dashboard and alerts
FREQUENTLY ASKED QUESTIONS
Straight answers.
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.
EVIDÊNCIA EM CAMPO
AI evaluated rigorously and wired into infrastructure.
Reproducibility, LLM evaluation, data, agents and operations are treated as parts of one system.
Inteligência Artificial
MLRC 2025 em Princeton: cinco princípios para avaliar LLMs com resultados reproduzíveis
Cinco aprendizados da EAGLE BS no MLRC 2025, no Princeton AI Lab, sobre benchmarks, determinismo, dados, transparência e avaliação robusta de LLMs.Ler artigo ↗
Google Cloud
Google Cloud Next 2025: IA, agentes e a nova infraestrutura digital
A participação da EAGLE BS no Google Cloud Next e Partner Summit 2025: Agentspace, Gemini 2.5, agentes, cloud, dados e aprendizados para clientes.Ler artigo ↗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.