Design and implement new analyses

Many of our customers come to us with both important business problems and the data needed to solve these problems. We help by designing and implementing analyses that transform data into actionable insights.

Depending on the use case, our analyses may include:

  • regression modeling and machine learning
  • correlation and conjoint analyses
  • classification, segmentation, grouping or cluster analysis
  • prediction modeling and time series forecasting
  • NLP and sentiment analysis
  • scaling for big data
  • interactive visualization

Migrate to the cloud for affordable scalability

Some of our clients have developed their own custom analyses in scripting languages like RPythonMatlab, or even in Excel. When it comes time to scale up, we help customers migrate their analytic workloads into the cloud using Amazon Web Services.

Depending on the use case, our solutions include:

  • architecting a secure and scalable VPC infrastructure with multi-AZ redundancy
  • importing and normalizing data into AWS RDS
  • provisioning a managed Hadoop EMR cluster
  • optimizing analysis code for speed and memory management
  • wrapping analysis in portable Docker containers
  • deploying workloads on AWS EC2 using ECS and spot fleets for cost efficiency

Case Studies


Our client helps both US and international companies comply with provisions of the Affordable Care Act. The client processes company payroll data and applies complex algorithms to produce timely compliance reports. We transformed the client’s Excel-based tool into an interactive Ruby on Rails web application. We provide 24/7 managed hosting services, with a resilient, fault-tolerant, and scalable deployment model built on AWS Elastic Beanstalk. We also continue to develop new analyses and reports to support our client’s customers.


Our client is an investment management firm registered with the US Securities and Exchange Commission. We work together to research new data-driven trading strategies that manage risk and deliver growth. Proof-of-concept analyses are first validated in Excel and re-coded in Python. Data and results are stored in a high performance and scalable AWS Aurora database. For large-scale analyses, we deploy Docker containers on an EC2 Spot Fleet using the EC2 Container Service. Our client benefits from elasticity, at a fraction of the cost of owning their own dedicated hardware.

Real Estate

Our client is an early-stage company providing customized data feeds to real-estate investors. The client asked us to help deploy their custom Ruby on Rails application in a secure environment on AWS. We configured a high-availability, scalable, VPC architecture in CloudFormation, for both production and testing environments. The web application was deployed with Elastic Beanstalk using Elasticache and RDS data stores. We used the AWS CodePipeline service to provide continuous integration and deployment from GitHub.