Serendeepia Playbook

Projects & products

Either we are working on consultancy projects for other companies or internal products, we think all products must follow the following phases in their life cycle. Most client projects fall under either Envisioning, Conceptualization or Minimum Viable Product.

Main Phases of a project


The Envisioning of a product asses the initial idea for a product or solution. During this phase the next questions need to be answered:

  • What are the problems or pain points the idea tries to solve?
  • What is the root use case?
  • What are the alternatives? What is currently being done to solve the pain points?
  • What is the economical impact of the idea?
  • How the idea affects the business/market?
  • What is the state of the art?
  • Is the development viable?


During the Conceptualization we try to create an initial prototype to test our hypothesis during the Envisioning while we keep updating the previous questions with more details.

Go / No Go Decision

At the end of the Conceptualization we should get a better idea of the viability of the project or product and we decide whether it is worth to continue with the Minimum Viable Product or stop.

Minimum Viable Product

During this phase we build the Minimum Viable Product (or MVP) and we release it to the market. The MVP should be the minimum product that fulfill the solution described during the Conceptualization.

Go / No Go Decision

Once the MVP is launched we follow its impact in the target market over a period of time. If the initial hypothesis of the solution are accomplished we decide to continue adding features and scaling the product.

Scale-up and improvements

We can scale up the product in two ways:

  • vertically: we keep on adding new features so we cover more use cases with the same product.
  • horizontally: we keep improving the use case of the product so it does better what it is suppose to do.

Mature operation

In a normal product we reach a point of maturity when we only keep track of the operations of the product. In case of Machine Learning products we keep track of the metrics of the models to know that they keep performing as they should be. We also keep track of metrics for usage and performance for the same reasons.