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Productizing a

Sensor-led Emission Detection Solution

in EnergyTech


Emission Tracking, Oil & Gas, IoT

Services Rendered

Product Engineering

Product Design

Digital Transformation

Tech stack

React, Apollo Client, Material UI, Spring Boot Java, Drools Rule engine, RabbitMQ, Cesium, Selenium, Cucumber, MQTT, PostgreSQL, Redis


Zemoso helped a Fortune 500 company accelerate development and deployment of a solution to track emissions by processing the variety of data being relayed through a ground-based sensor network and automating time-consuming processes at remote rigs and production centers, leading to immediate, corrective action.


Rising emissions is a major concern for EnergyTech companies around the world. The oil and gas sector is one of the biggest contributors to harmful emissions. Total indirect emissions from oil and gas operations are 15% of total energy sector emissions. Between the Paris Agreement and Biden’s executive order, the pressure is on and intelligently managing emissions has become a top priority for all energy production and processing companies.

Zemoso Labs Partnership Challenge

Zemoso co-create a solution on an expedited timeline that that processes the variety of data.

  1. Agile pods that scaled up or down depending on the skills needed at any given point in the project.
  2. Futureproof the product for faster service deployments in the future with CI/CD best practices.
  3. Connect onsite resources to the cloud infrastructure to easily handle sudden changes in data traffic.

Impact created

Zemoso completed end-to-end product development with on-time delivery. We  worked with live data and generated reports enabling a reduction in error rates and turnaround time.The product ultimately enabled the customer to meet emission reduction goals by reducing emissions by over 20%.


Emissions reduced

What are our clients saying?

Our clients love what we do:

How did we do this?

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We started with our proprietary version of Google Ventures Design Sprint and ran weekly sprints delivering features and enhancements incrementally. Multiple scrum teams worked on different aspects of the project with a Scrum of Scrums to keep things on track. The Zemoso pod was agile and scaled up or down depending on the skills needed at any given point in the project.

Engineering Focus

We used a microservices architecture. Each service is independent. The front-end was built in React and Redux. Local and remote data is managed via Apollo Client. GraphQL, an intermediate layer, was used to make deployments faster and easier. Material UI was used to develop user interface components. The back-end was developed in Spring Boot Java. Drools Rule Engine was used to specify the action that needs to be taken if a particular condition is met. RabbitMQ is the message broker.

APIs used

Third-party integration with Cesium, a geographic information system (GIS) API was used to pinpoint location, which was integral for proactive action.


Test automation was done using Cucumber and Selenium; Cucumber strengthens automation testing. Selenium ensures accuracy and speed. We automated API testing as well.

Computer vision and analytics

Zemoso used message queuing and telemetry transport (MQTT) protocol to connect remote devices. We ensured that the images can be resized and the system can ingest data from globally dispersed devices.


Following DevOps best practices, we implemented CI/CD using Jenkins, and GitHub (both local and remote). We also used containerization to futureproof the product and lay the groundwork for faster service deployments in the future.

Traffic management

We used a content delivery network (CDN) to deliver data and videos fast with low latency. We used Amazon Simple Notification Services (SNS) for messaging, and AWS Lambda to respond to new information and events. We also set up the system in a way that onsite resources were connected to the cloud infrastructure and it could easily handle sudden changes in data traffic (handling millions of requests per second).


We used PostgreSQL, Redis to ensure sub-millisecond response times, enabling millions of requests per second. Amazon S3 was used to store generated reports and images.

NotificationsThe sensors at the node station capture live readings and send the data in seconds to Amazon Web Services (AWS). This data is then sliced, diced, processed, and indexed based on similarities or discrepancies. A supervised learning Machine Learning (ML) model and an unsupervised ML model generated notifications. We also helped deploy rules to classify the notifications based on priority.

P.S. Since we work on early-stage products, many of them in stealth mode, we have strict Non-disclosure agreements (NDAs). The data, insights, and capabilities discussed in this blog have been anonymized to protect our client’s identity and don’t include any proprietary information.


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