How we work?

We use AI and data to make your operations smarter, and your employees more empowered. We believe that better decisions lead to better outcomes, and we’re here to help you make those decisions.

Your data

Our approach helps organisations gain new insights from data, enabling them to define actions within core operational processes. By combining human and machine efforts, we provide actionable recommendations based on employees’ experiences and knowledge. Employees can accept or reject these recommendations, allowing the model to learn from their input. This empowers employees, builds trust, and fosters a collaborative learning environment.

Better choices

Convert insights from existing data into concrete actions to improve an organisation’s core processes. “Data-driven” means more than just building a dashboard and extracting insights from it. We go further, offering people within the company action-oriented recommendations and helping them perform their day-to-day activities.

What does it mean to be data driven?

How our solution works?

To create the best possible fit for your specific needs we go through the following steps in the process.

Analysis phase

The analysis phase involves a comprehensive examination and evaluation of data, information, or a situation to gain deeper insights and make informed decisions or recommendations.

  1. Business context & end user analysis
  2. Data analysis
  3. Analysis of current state
  4. New insights and current model identity features
  5. Process analysis: link model output with workflow of end user

Proof of concept

The proof of concept phase focuses on validating the feasibility and viability of a concept, idea, or technology through a small-scale, experimental implementation to assess its potential for larger-scale development.




  1. Decision on type of product
  2. AI model development (collaboration with internal data team)
  3. Model performance – evaluation on test data set
  4. User interface development
  5. Demo

Live test

The “live” phase is when the solution is actively used, while the “training” phase involves educating users to maximize its benefits. Both phases are essential for successful implementation and ongoing efficiency.

  1. Minimal viable product (MVP)
  2. Live test with end users
  3. Analysis of results
  4. End user feedback from live test

Operationalisation

The operationalization phase involves translating plans, strategies etc into practical and actionable steps for implementation, ensuring the successful integration and deployment of a project.

  1. Integration with internal systems or implementation of front-end digital assistant
  2. Final solution live
  3. Feedback processings
  4. Run & improve

Increased efficiency

Streamlining Processes for Success

Optimize revenue

Harnessing Data for higher revenue

Increased employee satisfaction

Empowering Employees for a Happier Workplace

Our experience so far

We are well versed in optimizing operational efficiency in insurance, transport & logistics, food industry, and healthcare.

We’re eager to hear your data question!

Every day, our team of passionate data junkies is waiting to solve your data problems, no matter how complex they may be.

So, don’t hesitate and drop us a line. We’ll get back to you faster than you can say ‘artificial intelligence’.

Promise, we won’t have Chat GPT come up with the answer