NEN 2767 condition scoring from smartphone photos
A portfolio owner with hundreds of buildings uses guided photo capture and AI defect detection to prepare NEN 2767 condition scores, with a certified expert reviewing rather than gathering.
The situation
NEN 2767 assessments need trained inspectors, so the full portfolio can only be surveyed on a long cycle. Maintenance planning is based on data that is often years old.
The manual baseline
- Certified inspectors survey each building on site, which sets a hard limit on cycle time
- Scoring consistency varies between inspectors and between years
- Defect evidence sits in reports rather than in a queryable dataset
What KLIQ changes
- Facility staff follow a guided capture route per building element
- The platform detects defect types and proposes extent, intensity and seriousness
- A certified expert validates or adjusts the proposed condition score
- Every score keeps the annotated source photo behind it
Indicative outcomes
Expert time
Review, not travel
The certified expert validates results instead of visiting each building
Survey cycle
Shorter and repeatable
Capture can be done by existing staff, so buildings can be revisited more often
Consistency
One scoring model
The same detection logic is applied across the whole portfolio
Integration
Condition scores and annotated evidence export to the maintenance management system through the API, keeping the NEN 2767 structure intact.
See NEN 2767 inspectionsRun these numbers on your own volumes
The estimator uses your asset counts and current process to produce an indicative time and cost picture.
Estimate your savings in 60 seconds
Hours saved, integration effort and training needs, based on your own volumes. No email needed to see the result.
Open the estimatorTalk to our team
Where computer vision stands today, the human verification layer, integration patterns and a worked API walkthrough.
Contact usPrefer a conversation? Book 20 minutes.