Which task repeats?
Describe what a person looks at or classifies and what they do with the result.
Applied AI · Computer vision
Useful AI starts with a specific task.
We explore where artificial intelligence and computer vision may support a specific task, considering available data and operating conditions.
01Task and decision defined
02Data and limitations reviewed
03A clear next technical step
The opportunity
AI creates value when it improves a decision or a process.
We start with the task and the decision it should support. We review the context, data and how to assess whether an approach is suitable.
01 Evaluation in context
Computer vision
Image analysis for identification, classification or inspection tasks, depending on available data and conditions.
02 Evaluation in context
Data and model evaluation
Review of data, criteria and limits to determine whether a model can support the defined task.
03 Evaluation in context
Process integration
Technical design to connect an AI-based solution with existing processes and systems.
How a model reads an image
From pixels to a prediction.
A conceptual journey: inspect the input, extract signals, process them in a network and compare the output probabilities.
Input
Pixel matrix · 3 × 3 window
Features
Signals the model can process
Neural network
Propagation through connected layers
Prediction
Output distribution · softmax
Simplified schematic using simulated data. Probabilities illustrate model output, not a guarantee of accuracy.
How we move forward
Assess the problem before automating it.
Each stage helps clarify feasibility, limitations and the work needed for a possible implementation.
- 01
Feasibility
We review data, process, constraints and success criteria.
- 02
Focused evaluation
We compare the approach with agreed data and criteria to understand its limits.
- 03
Next step
We define any additional integration, human review or operational work the case requires.
A practical question
What happens when the model gets a case wrong?
An AI solution needs a way to evaluate errors and decide how to respond. With your team, we define what can be automated, what needs review and what evidence will support the next step.
What your team can review
Deliverables are defined by the agreed scope.
- 01
Feasibility and evaluation criteria
A focused task, a review of available data and an agreed way to examine results.
- 02
Models and evidence of limitations
When development is in scope, we work with models, evaluation results and error examples the team can review.
- 03
An integration path
Defined inputs, outputs, human review and conditions for using and maintaining the solution.
When it makes sense
Best for processes with repeatable signals and costly decisions.
AI may be relevant when there is a defined task, useful data and an agreed way to assess results and limitations.
- 01
A repetitive image analysis task
- 02
Available data that may support a decision
- 03
A process with defined review criteria
- 04
A team that needs to assess feasibility before implementation
Before we talk
A useful starting point.
You do not need to arrive with a model selected. Start with the task and some representative examples.
What examples exist?
Identify images or data, their source and who understands the categories. We can start with a description of the sources.
Which errors matter?
Explain what happens if a case is missed or an incorrect alert is raised, and who could review it.
Let's talk
From technical challenge to a solution your operation can use.
We review the context, feasibility and best starting point in an initial conversation.
Book a diagnostic session directly with the partners