Our step-by-step by process is the result of working with data & product teams for over a decade.

Your data lives in silos, stuck inside the different tools that your team uses. We develop the infrastructure to get reliable and combined data together in a central data warehouse.

We start with a kickoff meeting where we go through the different tools that your team is using. A design is created for your data warehouse, pipeline, BI and AI setup.

We execute interviews with key  stakeholders to identity insights that truly answer business needs. Once this is clear, we apply the best suited data infrastructure, modeling & AI technology to get these insights in your team's hands on a daily basis.

We start with a kickoff meeting to go through your internal data and surrounding infrastructure. Then, it’s data crunching time for our team.

During a timespan of four weeks, we execute the assessment, and work with a top-down approach to one by one cover these key questions: 

1. What are the leading platforms in the market to deliver high quality AI capabilities today and tomorrow?
2. What does this mean for requirements in data, structure and technical architecture?
3. Does your current data, structure of this, and technical infrastructure fit these requirements?
4. Where are the gaps and what are steps to get this ready?
5. What is the recommended priority of these steps to get the shortest path towards high quality AI capabilities?

Our AI Readiness Assessment is the starting point here, to clearly identify a roadmap to get your data in shape for the best AI models available today. We execute this roadmap with our engineering team and start to build the full infrastructure to collect, clean, transform, embed your data and to offer this in an AI Content Store.

This AI Content Store is an easy to use database that contains all your data transformed for AI, ready to use by your developers to build amazing AI applications. We also create the integrations with leading AI model vendors, so your teams have flexibility of choice and no lock-in.

Typically we enable and educate internal teams to work with the AI Content Store to build great AI applications at a rapid pace. We know most companies have great development teams that have a deep domain knowledge and it’s our focus to get them access to the data as soon as possible. But depending on the setup of your team, we can advise your team on new AI UX paradigms or even develop the application or prototype.

There are many ways to improve the performance of AI applications. But in most cases, your biggest lever is iterating on the input side. To get the quality of AI generated output to a great level, iterations are needed to optimize how your data is embedded and offered to different models.

But first the question comes; how do you define quality?We have developed a framework to measure how your AI is performing. Using tangible and feasible KPIs that are possible to implement and run on an automated basis.