We assess whether a model can address the requirement and what data is available. A measurable objective and a simple baseline are defined before model development.
Data is prepared and models are trained or adapted, then evaluated on a separate dataset. Deployment includes quality monitoring and update procedures; limitations, errors and human-review cases are documented explicitly.
Artificial intelligence and machine learning
Who it is for
- Companies with recurring tasks and usable datasets
Artificial intelligence and machine learning
Challenges we address
- Manual processing scales poorly and automation suitability is unproven
Artificial intelligence and machine learning
What is included
- Data assessment and measurable task definition
- Model training, comparison and evaluation
- Deployment, monitoring and quality maintenance
Artificial intelligence and machine learning
What you receive
- Validated prototype or deployed model
- Quality, limitations and maintenance report
How we work
- 01
Discovery and scope
We assess the current environment, requirements and constraints. Priorities include: Manual processing scales poorly and automation suitability is unproven. We agree on scope and acceptance criteria.
- 02
Design and implementation
We design the solution and carry out agreed activities: Data assessment and measurable task definition; Model training, comparison and evaluation; Deployment, monitoring and quality maintenance. Changes are checked before entering the production environment.
- 03
Validation and handover
We validate agreed scenarios, record limitations and hand over documentation. Project timing follows discovery; support hours and response targets are defined in a separate agreement.
How pricing works
Costs depend on data, labelling, models, computing, integration and quality requirements.
Get a consultationCommon questions
Can target accuracy be promised before reviewing the data?
No. Achievable quality is evaluated using data and agreed metrics. A pilot tests suitability before a larger implementation commitment.