From advice to delivery.

Independent review of data and AI solutions

OccasionYour vendor is introducing AI or far-reaching automation, and you want to know what it means for your organisation.

I assess vendor solutions and in-house developments on architecture, data quality, transparency, responsible use and value. You receive a well-founded assessment and the questions that matter, so you can engage as a full discussion partner. Because I deliver such solutions myself, I look beyond the presentation.

Data strategy and data architecture

OccasionYour information landscape has grown without coherence, and you are looking for direction.

I give an objective picture of where you stand today. On that basis we draw up a feasible roadmap, with a data architecture that grows with your ambitions. I translate reference architectures such as CORA, VERA and NBility into your system, process and data architecture, and make sure they are followed in practice. That way you stay in control of your data and remain agile.

Example: From reference architecture to meaningful enterprise architecture

AI exploration

OccasionYou want to know whether AI demonstrably delivers for your question.

Together we determine where AI adds value, and where good data or classic analysis is sufficient. I map out the preconditions, such as data quality, unambiguous definitions, governance and the human dimension. You receive a well-founded choice and an approach to start in a focused way.

Machine learning and predictive models

OccasionYou have plenty of data, but still prioritise on experience and intuition.

Using classic machine learning and forecasting methods, I build predictions that support planning and prioritisation, for example of tenancy turnover, vacancy duration or maintenance budgets. The models also reveal the trends and drivers behind your costs. That gives you control over budgets and a well-founded plan. I work closely with your domain specialists. They bring the domain knowledge, I bring the data and the model.

Example: Forecasting for control over budgets and planning

AI applications and proofs of concept

OccasionYou want to see what AI delivers in your own practice before you invest.

I deliver applications with language models, AI agents and computer vision. Validation is part of the design from the start, because the outcome must be verifiable. You receive a proof of concept that shows what AI delivers, and which preconditions are needed to scale up.

Example: AI with verifiability as the starting point

Data platforms and management information

OccasionStaff collect and check figures manually, and an overview is missing.

I design and deliver data warehouses, data platforms and dashboards, with attention to data quality and transparency. Think of data lineage, data models as code and measurable quality. The result is reliable management information, and a solution your organisation can maintain independently.

Example: Management information that builds itself

Anomaly detection for data quality

OccasionYour data quality checks only catch the errors you already knew about.

Traditional data quality relies on fixed business rules, and those only catch errors that were thought of in advance. Anomaly detection looks at the context of the data. Does a value fit comparable objects, its history and related data? That surfaces remarkable, exceptional and possibly incorrect data that rules miss. The signals feed into your existing data quality process.