<aside> 💡 This document is an explanation of the technical organization of Cozero. It should guide Cozero partners in understanding how the software and people are organized at Cozero’s Tech team.

</aside>

🔋 Technology

Purpose & main challenges

The goal of Cozero’s tech team is to build a SaaS platform that can be adopted at scale by the biggest companies in the world to help them reduce their carbon emissions.

This means some of our main technical challenges are: in the realm of data engineering, where we need to onboard millions of data points per customer; in data modeling where we need to understand how to convert this activity data (i.e. liters of fuel or KwH of electricity) into CO2 emission tonnes; and in data analytics where we need to understand how to provide relevant insights to help them move towards decarbonization. Additionally, we have the challenge in understanding where and how to implement AI tools and processes.

Tech stack

Our tech stack is public and available at Stackshare. We focus on using the best open-source tools in the TypeScript ecosystem that allows us to scale our tech operations with minimum maintenance costs.

Some highlights of our stack:

• Languages & Frameworks: TypeScript, Node.js, React, NestJS
  • We have the privilege to work with some of the most modern tools for software engineering. Our stack allows us to build software with an increased developer experience due to its intrinsic easy maintainability and scalability.
  • We use a micro-service architecture built with NestJS, and its central component is an API Gateway pattern
• DevOps: AWS Lambda, GitHub Actions, New Relic
  • We use state-of-the-art DevOps tools to deploy and manage our environments, which allow us to do zero-downtime deployments and easily roll back changes if needed
  • Our stack is deployed in a serverless environment using AWS Lambda and the Serverless Framework
  • 🌱 Deploying our stack in serverless allows us to serve our customers at a minimal cost for our wallet and for the environment since the electricity consumption is significantly lower
• Data: Postgres, AWS Aurora, AWS Redshift, Cube
  • Our data is stored in relational databases such as AWS Aurora, which uses Postgres as a database engine
  • We use Cube as a semantic layer to help us serve an analytics product to our customer in a way that we can plot massive amounts of information in real-time
  • To help us improve our performance in OLAP use cases, we use the columnar data warehouse AWS Redshift
• AI: Claude Code, Lovable, Cursor, etc.
  • We actively explore and integrate AI tools to boost our productivity and creativity—whether it's writing cleaner code, generating documentation, or ideating new product features
  • Developers at Cozero are encouraged to integrate AI like Claude, and Cursor to speed up implementation and reduce cognitive load on repetitive tasks. Besides that, we prize engineers who find innovative ways to use AI in a way that benefits them and the company
  • We're also looking into ways AI can help improve our internal processes and customer-facing products, from automated QA to data-driven insights and smart recommendations

🙌 People

Team

Our current Product team is composed of 3 Squads, each with an Tech Lead, a Product Manager a Product Designer and 5 Full Stack Engineers. In addition we have a small team of Data Scientists and a small team of Climate Experts who support the teams. We are about to set out building our fourth Squad.

Organizational setup

We follow a Domain Driven Design pattern to organize our teams, as we’ve built multi-disciplinary Squads oriented around specific customer-facing problems of Cozero. This means that every employee has a chance to interact with people from different backgrounds and improve the opportunities for learning and growth in the organization.

Personal development