It's hard to fully trust a model

Complexity grows with the number of cells and Excel formulas are tricky, making it difficult to fully audit a model. 

Your worst nightmare is your boss or client discovering an error during final decision meeting.

More sophistication, more errors

Every request to add a refinement to the model gives you cold sweat when you think of the effort to re-check everything. 

You frequently push back since it would take too much time and add too much risk.

Validation and handover are illusions

Fully explaining the model to a colleague would require to verify every formula, which is impossible when the model is large. 

Despite their best efforts, most models are abandoned when their creator hands them over.

Centered on the business professional 

Many domains have their specialized modeling software, but not corporate decision-making. How many times have we collectively recreated a valuation model or a project ROI model? 

A business-oriented approach is needed to program our models using a more concise and efficient language.

Transparency at the core

The logic of a sophisticated model is highly non-linear, with many branches that split and merge. 

This needs to be represented visually with information that can be understood fast. Formulas do not offer that type of transparency, so this requires an innovative approach.

Errors minimized at the source

Typical errors in data processing have been known for decades. A formula not properly dragged to the bottom of the column, duplicate records in a table joined using VLOOKUP, all these have led to catastrophic consequences.

We believe the software should prevent these errors or at least warn the user if they're at risk.