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Plug & Play solutions for scaling Enterprise AI

CSP roulette (

Cloud-agnostic analytics

Agile is important, but so are hybrid / cloud-agnostic solutions, multiskill, T-shaped 
capabilities, and results-based delivery.


Azure, AWS, GCP, Heroku and IBM Cloud connected solutions from an Azure-certified AI Engineer & Data Scientist

PyVista animation

Full Stack Data Science Apps

IDC predicts demand for technical expertise to develop, implement, and manage AI apps is expected to grow at a CAGR of 18.4%. And that can't be just a messy python script no-one understands.

From Python Notebooks hooked to cloud and front-ends built with react.js, Streamlit or Dash and hosted on Heroku to ensure a full stack data science experience

engineered features for month ahead electricity pricing forecast (RNN) model

Automated Data Ingestion

Data ingestion has gone way beyond importing data from a SQL database or a csv file. adopt DataOps continuous integration and continuous delivery (CI / CD) practises to ensure data pipelines don't break the moment an app is deployed. 


Architected solutions with automation and data drift in mind


Kaizen-infused Design Thinking

Forward planning in AI plans for plenty of iteration. invests time at the start in brainstorming the problem context and potential solutions before delivering a project lifecycle roadmap. kaizen then kicks-in, with  progress monitoring.


ROI-focus, UX and workflow-driven design and implementation coupled with continual improvement

We Integrate With Your Ecosystem

People, processes, and tools are the three cornerstones of any best practice IT framework - the same goes for productionizing an AI application stack. 


From the outset, we identify go-to stakeholders and MVP requirements to speed up (and secure) API connection to your ecosystem.


And whether the AI solution is served by a thick client application (local install or Jupyter notebook) or a thin client (web browser, Google Colab, React UI) we work with key data storage (Redshift, BigQuery, NoSQL databases etc.) and compute (EC2 instances, Azure VMs, Apache Spark etc.) to serve the underlying AI deployment. 

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