The Company

 

Our History

With over fourteen years of experience developing cloud based software in the area of predictive intake modelling, we have gathered unique data sets on consumer habits, practices, consumption and chemical occurrence from all over the world. These data sets have been installed and validated in our regulatory accepted and employed models. Our experts in mathematical modelling, statistics, software development, food science and nutrition develop and deploy our software models for consumer intake modelling, product safety assessment, regulatory affairs and innovation projects.

We work in areas including:

  • Data Exploration & Strategy
  • Data Collection & Analysis
  • Data Science and Analytics Projects
  • Data Analytics Model Creation
  • Data Curation and Integration of Data Sets with Models
  • Production Deployment of Models
  • Consumer Intake Assessment
  • Total Aggregate Exposure
  • Custom Software Application Development

Our Mission

At Creme Global we think about data, models and software from a 10,000 foot strategic view. Here are some of the concepts that focus our thinking:

Enhanced decision making: Our solutions are often about making better decisions. We acknowledge that successful solutions include human experts somewhere in the workflow. We design for that. We think of our solutions as a combination of human agency and quantitative computing.

Value Machines: Our solutions give ongoing returns to our customers. Data analytics solutions often become more useful over time as historical data gathers.

Network Effects: We create network effects with our solutions, where the value increases for a client as more users adopt our products.

New domains: When we enter new domains we often find our clients take a leap forward in their capabilities. We guide and prepare our clients for this new capability. 

Sectors we have worked in

  • Food Safety
  • Nutrition
  • Care and Cosmetics
  • Crop Science
  • Chemical
  • Retail
  • Information Technology
  • Hardware

Some of the tools we have used

  • Predictive Analytics
  • Cloud Computing
  • Sentiment Analysis
  • Environments
  • Clustering
  • Classification
  • Regression
  • Deep Learning
  • Bayesian Statistics
  • Supervised Learning
  • Unsupervised Learning
  • Time series analysis
  • Big Data
  • Natural Language Processing (NLP)