Advisory & Consulting

We perform two kinds of individualized services for businesses and academics alike: Consultancy and Advisory. Consultancy focuses at helping solve specific problems over relatively short periods, while Advisory looks at the big picture by addressing broader issues over a longer time frame. Both our Consultancy and Advisory work are custom, one-on-one approaches tailored to clients’s specific needs.

The main broad areas where we provide our expertise are the following:

  1. Artificial Intelligence & Data Strategy

What is your Data Strategy? Do you intend on using Artificial Intelligence, Data Science, Statistical Learning, Machine Learning, Deep Learning, or Big Data, to achieve your organization’s goals?

If you find the terminology confusing, you are not alone and you are in the right place to find light to shed onto the darkness; if you already understand the terms, you will also understand that this is a huge area of active research, application, and innovation arising from it, with few companies being able to navigate it all on their own.

We can help you find the answer questions like:

Whatever your industry and size of your company, department, or research group, we can help you assess your data needs, and assemble the tools required to take advantage of these technologies.

  1. Data generation & management

Whatever data you have, it comes from somewhere and is generated somehow; however, not all data is created equal. Experimental design is the science behind science; as the name suggests, it allows researchers to control what their data sets look like. For non-experimental researchers, it is also essential to control several aspects of how data is generated or collected, organized and categorized – metadata is half of the data and data management is non-optional.

If you already have a stream of data that is used in your organization, we can also streamline and optimize its generation, pre-processing, and data “wrangling” in general for it’s future use with statistical, machine learning, or AI models as well as guide you through formalizing a Data Management Plan that fits your needs.

  1. Statistical Modeling

If you are already in the weeds of quantitative methods, you have been likely faced with choices of statistical tests for an analysis, Machine Learning methods for an application, or more generally, with different alternatives of how to implement a statistical model. The already large field of statistics has grown exponentially with the explosion of the use of Machine Learning and the subsequent AI hype – the good news is that you do not need a Ph.D. in statistics or computer science to benefit from this, that’s what we are here for.

Whether you are a scientist yourself requiring general input from experts in quantitative techniques, or a professional forging ahead who needs assistance with a specific implementation, we can support you in choosing, implementing, and justifying the use of your statistical methods.

  1. Scientific & Mathematical Computing

For some organizations, a basic (but solid) implementation will be the most cost-effective way of achieving its goals; for others, high-performance computing tools may be required. Testing, validating, benchmarking, and optimizing may come at the tail end of the entire process of implementing an algorithm; however, these choices should not be an afterthought – writing proper reproducible code is costly, time consuming, and error prone. It is important to plan ahead, think these decisions through and make timely decisions for a project as a whole. Choices abound: