Our process - How we work

We measure our success based on our customer wins.


Discovery is the first stage in our process, where we get to know each other and define the scope, goals, and expectations of the project.

Discovery is a crucial step that lays the foundation for a successful collaboration and outcome. During discovery, we may:

  • Conduct research and analysis of the client’s industry, market, competitors, customers, and challenges.
  • Identify and prioritize the client’s needs, problems, opportunities, and objectives.
  • Define the project’s vision, scope, deliverables, timeline, budget, and success criteria.

Included in this phase

  • In-depth questionnaires
  • Feasibility studies
  • Proofs-of-concept


Based off of the discovery phase, we develop a comprehensive roadmap for each product and start working towards delivery.

The build process of an analytics engineering studio typically involves the following steps:

  • Define the data sources and the business logic for the data transformations. This can be done using SQL or other languages that are compatible with the data warehouse.
  • Use a tool like dbt to write, organize, and document the data transformation code. Dbt is a popular open-source tool that helps analytics engineers apply version control, testing, and modularization to their analytics code.
  • Set up a workflow for deploying and running the data pipelines. This can be done using a tool like dbt Cloud, which provides a web interface and a scheduler for managing dbt projects.
  • Monitor and troubleshoot the data pipelines using logging, alerting, and debugging tools. This can be done using a tool like dbt Docs, which generates interactive documentation for the data models and their dependencies.
  • Collaborate with other data team members and stakeholders using tools like GitHub, Slack, or email. This can help ensure the quality and reliability of the data pipelines and foster a culture of data-driven decision making.


The delivery phase of a data project is the stage where the data analysis and insights are presented to the stakeholders or clients. It is also the stage where the data project is evaluated for its quality, impact, and value.

  • Design/develop: In this sub-phase, the data project team creates the deliverables based on the project scope, requirements, and specifications. The deliverables can include reports, dashboards, visualizations, models, algorithms, or applications that showcase the data analysis and insights. The team also documents the data sources, methods, assumptions, limitations, and recommendations of the data project.
  • Testing and readiness: In this sub-phase, the data project team validates the accuracy, completeness, and functionality of the deliverables. The team also conducts user acceptance testing (UAT) with the stakeholders or clients to ensure that the deliverables meet their expectations and needs. The team also prepares for the deployment or implementation of the deliverables in the target environment or platform.
  • Cutover and post go-live: In this sub-phase, the data project team deploys or implements the deliverables in the target environment or platform. The team also monitors the performance, usage, and feedback of the deliverables after they go live. The team also provides support, maintenance, and updates for the deliverables as needed.

Customer success and impact

  • The delivery phase of a data project is crucial for demonstrating the value and impact of the data analysis and insights. It is also important for ensuring that the deliverables are reliable, useful, and user-friendly. The delivery phase requires effective communication, collaboration, and feedback between the data project team and the stakeholders or clients.

Tell us about your data project.