Our goal: Fill the missing engineering gaps in your team to bring AI/ML to life in your organization, at enterprise scale
We provide the services necessary to help our customers integrate AI/ML, and our Analytic Components and Methods (ACMs), into their existing tech stack and workflows. We have deep expertise in integrating disparate data assets to create analytic data stores fit for business intelligence analytics and for training AI/ML predictive and prescriptive models that convert data to enterprise value
Tech Stack
Our Tech Stack is robust and customizable whether in or outside of the cloud.
Our robust tech stack has handled everything our clients have required from us, from web scraping and AI/ML Modeling to Cloud/Website Infrastructure and even more. Below are just a few of our most common tools used for clients:
- Platform: GCP, AWS, Azure, on-site, Hybrid structure (both on-site and cloud)
- Languages: Javascript, Python, HTML, C++, Java and many other languages
- Business Intelligence: Tableau, PowerBI, Google Data Studio, and many others
- Data Pipelines: ETL tools, scripting, SQL, Cloud Based Querying and Auto Scaling pipelines
- Database Technologies: MongoDB, Firestore, Redshift, SQL Server, PostgreSQL, Oracle, MySQL, Google Big Query and many others
- Orchestration: Kubernetes, Cloud Run, ECS, Docker, Apache workflow and more
- Dev-ops: GitLab, GitHub, Terraform and more
Case Study: Data Pipes and architecture to support AI/ML
Create a dataflow architecture to support B2B Sales Solutions. Incorporate unstructured data from MongoDB and numerous Google Sheets applications to create an analytic data store and integrate AI/ML components to create a disruptive solution.
Case Study: BI for Inventory Management, Forecasting and Pricing
Customer desired to deliver AI/ML outputs in an existing BI platform to support Inventory Management and Pricing workflows for ocean cargo.
