Validate your Product
Prove quickly and without wasted effort that your product meets its required specifications, with evidence that holds up to any scrutiny.
Book your R&D call or send us a messageWhat we do
Validation should confirm your product's quality requirements, not eat up budget and schedule. We set it up to be efficient and convincing.
- We tie your validation strategy directly to the specifications.
- We design focused studies that use no more experiments and time than needed.
- We deliver sound statistical evidence and the matching documentation.
- In regulated areas, we build in the applicable standards from the start, as planned.
How we work
Together with you, we build a lean validation pipeline: grounded in your specifications, the applicable regulatory requirements and standards, and statistically sound, sparing experiments and analyses. The validation analyses are automated and validated, so they run quickly and efficiently.
What you get
- An efficient validation that protects budget and time.
- Evidence that your product meets the specifications.
- Documentation that stands up with reviewers and regulators.
Frequently asked questions about validation
What does validation mean, and how does it differ from verification?
Verification checks whether a product was built correctly, that is, whether it meets the specification. Validation checks whether it does the right thing, that is, whether it meets the actual purpose and quality requirements. We tie validation directly to your specifications and deliver the sound evidence that your product does what it should.
How does AI make validation more efficient?
AI can evaluate process data continuously, predict deviations early and focus the testing effort where the risk is. That lowers the effort for recurring checks without lowering safety. The analyses run automated and validated, so they are fast and reproducible.
How do you validate products that contain AI themselves?
AI components cannot be checked with rigid, static methods, because they behave differently from classic software. Instead of exact target values, you work with statistical acceptance ranges, for example a defined minimum accuracy, and with targeted test methods. We treat an AI model like a component of its own: a clearly defined purpose, verified performance, complete documentation, guided by recognised guidelines.
What is model drift, and why does it matter for validation?
Model drift means an AI model loses accuracy over time because the real data moves away from the training data. For validation this means a model is not finished once and for all, but must be monitored and retrained when needed. We plan this ongoing monitoring in from the start.
How do you ensure sound statistical evidence in regulated areas?
We tie the validation strategy directly to your specifications and design focused studies that use as few experiments as possible. You get statistically sound evidence with matching documentation that holds up with reviewers and regulators. In regulated areas the applicable standards are built in from the start.
Validation is only useful if it is well documented.
Book your R&D call now or send us a message