Quality Control

Predicting Outcomes Before They Happen

Quality Is a Process, Not a Result

Quality Management Systems (QMS) start at the beginning of production processes with digitalised and connected data collection. Based on quality data it’s simple to automate processes, create root-cause analysis and improve the processes.

Understand What Is Happening, When Its Happening

As the Quality Control processes in manufacturing evolve, there’s a trend toward more inline metrology, or even toward process integrated measuring instruments, minimising control loops. Measurements should no longer be taken in a separate room or area, but directly on the production line. Metrology should be applied in a modularised mode in factories and production lines, that allows to reduce the use of standard measuring instruments.

Self-Learning is Self-Healing

A more optimised quality assurance process could lead to a better-quality product with fewer defects. The self-optimisation is a characteristic of the smart factory, that can predict and detect quality defect trends sooner and also help to identify discrete human, machine or environmental causes of poor quality. That could lead to lower scrap rates and lead times while increasing efficiency and yield.

Tailored Solutions

Every Quality Control Process Takes Individual Care

Machine Learning -See The Unseen

- Discover unknown patterns and relationships that result in quality or lack thereof;

- Analyse data combinations in order to identify quality trends and the key quality drivers;

- Identify the root-causes and contributors to failures, as well as acquire insights for optimising the production lines in order to improve quality.

- Save hours of time performing manual data analysis, enabling Quality Control to be proactive rather than reactive.

The Digital Transformation

Bring QA Into The New Era

Digitalisation is Key for Leveraging Data in Decision-Making

Automation of specific manufacturing processes has brought along a leap in production efficiency worldwide under the term Industry 3.0.
The focus of Industry 4.0 is to automate decision making by using precise data from the entire production process.

Harness All Data In Your Enterprise

Every step of the production process and every decision taken by the management can and should be measured.
Quite often this data is in analogue form, on paper or in the heads of management, or is only kept within the bounds of a specific manufacturing process. By digitalising the analogue and measuring information from every process step of manufacturing, an overall picture of the enterprise appears.
The actual situation awareness is the key that enables fully digitalised enterprises to achieve superior efficiency to their peers.

Our Approach

An iterative 3-step process


1-2 Months

Proof of Concept - Fastest to Goal

To prove the project can be executed a smaller scope quick win will be agreed on. This will enable users to get fast results before committing fully to a large scale project.


2-8 Months

Fully functional software

Main functionality of the software will be developed and made available to the client for testing.


4-12 Months

Increased scope and ML independence

Development of additional functionality, process automatisation and modifications to the machine learning (ML) algorithms will be added. At the end of this phase the software will be fully functional.

The Team

Our QA team is focused
Martin Laid
Chief Engineer
Martin Laid
Chief Engineer
Martin Laid
Chief Engineer
Priit Ulmas
Senior Data Engineer
Priit Ulmas
Senior Data Engineer
Priit Ulmas
Senior Data Engineer

We are developers, technologists, operators and engineers who have worked on over 100 projects.

Deep experience in Big Data, Automation and production.

Get in touch to Arrange a demo

We are involved in projects world-wide, just let us know!