02 / Intelligence
AI Development and Integration
Connect AI to a working business process.
Discuss your project scope ↗AI Development and Integration: our approach
AI integration makes model output part of a usable workflow. We start with the task rather than a model: what needs to be classified, which data is available and what happens when a result is wrong? Data suitability, evaluation criteria and human review belong in the same design.
Use cases
Illustrative scenarios; the project scope depends on your requirements.
Image classification
Evaluate a model against image quality and the cost of different mistakes. DeepCancer is our histopathology research example; it is not a clinical diagnostic tool.
Operational decision support
Connect results to records for review or prioritisation. Define human review for uncertain results and critical decisions.
Technical scope
- The task the model will perform and the data it will use
- The operational step where the model is integrated
- How inputs and outputs relate to system records
- Model monitoring and retraining structure
- Success and performance criteria
Deliverables
- Developed model and system integration
- Input and output structure
- Execution records and monitoring structure
- Retraining and version management
- Performance and evaluation view
Design considerations
01The model operates as an integrated part of the relevant workflow.
02Generated outputs are directly associated with the relevant system records.
03Inputs, outputs, and execution records are retained to keep model behavior traceable.
AI Development and Integration — frequently asked questions
How much data does an AI project need?
There is no universal number. Task diversity, data quality, label consistency and suitable existing models all matter. Adding more unrepresentative data may not improve the result.
How do you evaluate a model?
Use data withheld from training and scenarios that represent actual use. Evaluate error types and class-level outcomes as well as overall accuracy. Agree on acceptance criteria for the task.
How is the model connected to existing software?
Define input and output contracts, service calls, access boundaries and execution records. Include response time, model versions and failed calls in the integration design.