We are sAInce.io

Experts in Smart Industry

Who we are.

Founded in May 2020 we are pioneering IIoT with autonomous Smart Devices and Equipment.
Each of us has more than 20 years of experience in automation within Manufacturing.
Over the years we gained experience in IIoT, Micro Service development, DevOps, Big Data and Machine Learning.
We build further on our Network of peers to bring you the best in Smart Manufacturing.
Currently we are looking for seasoned peer experts and junior enthusiasts who blend well with our culture to extend our team of high-end consultants.
See our Job page for more information.
Apply for a Job
Dirk Brys
Data Wizard

Originally I started my career as an expert in OO design and development.

I shifted more than 15 years ago to data warehousing and business intelligence and specialised in big data and data science.

My main interests are in deep learning and big data technologies.

In my spare time I'm very passionate about Salsa dancing. So much that I performed internationally and that I have my own dance school where I teach LA style salsa.

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Geert Van Hecke
IIoT Wizz'kid

I am the sAInce IIoT whizz”kid” and one of our founding fathers and inventor of IIoT smart sensor location ID patent (BE2014/5160).

At the age of 12, I bought my first computer, ... an Apple IIe and started coding. A decade later and passionate about technology, I graduated as electronics & embedded engineer.

Ever since I have been designing and developing mission critical smart distributed monitoring, telemetry and IIoT systems.

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Patrick Hacour
DevOps Guru

I spent many years in pharmaceutical industry, holding positions in maintenance, project engineering, industrial automation and software compliance.

As a consultant in energy efficiency I helped companies in various industries to save on their energy bill.

I've been very successful in rolling out and maintaining continuous improvement programs.

In recent years, I became an expert in test automation and devops.

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Our Services

Edge Devices

We build intelligent edge devices around plain sensors, PLC’s, … and equipment, tailored to your business needs.
Over the air monitoring, configuration, … automatically deployed from the cloud or your own infrastructure. We make any device or equipment smart.
Open up the world of Machine Learning to your devices.

Micro Services

In most cases it’s not enough to make devices smart but you’ll want to incorporate business logic that is tailored to your manufacturing processes.
We build micro services so you can deal with this logic without compromising the smart edge device.
Think about real-time cockpit applications for alerting and responding to process events, anomalies and predictive maintenance.
Act immediately on real-time data streams.
Visualize actual and predicted trends and apply ML models in real-time.
We can help you in building these micro services and applying best practices for modularity, reuse and integration with external applications.

Cloud Applications

We are specialized in AWS Serverless applications
Provide access to all your users on integrated data in the cloud. We build applications in Angular with backends in Node-Js and/or Python through lambda functions.
We develop the AWS architecture and can integrate all your requirements with the full AWS services stack.
We assist you towards a full serverless architecture.

System Integration

We have extended knowledge in the field of application integration and data integration.
We augment historians so they can be enriched with ERP, MES, LIMS, WMS etc data for full insight in the equipment data.
We develop event driven applications that provide real-time ERP, MES etc data to other applications or services so systems remain loosely coupled but enriched with each others data.

Data Engineering

Before you can actually make devices or applications smart you need to train and test large datasets. Enters the world of big data.
We build data pipelines to inject your data into Cloud native platforms (Public, Private or On-Premise) and transform them into fast and usable formats for Data Science and Business Intelligence consumption.
Whether it’s about historical backfill operations or real-time data ingestion, we automate your data lake so that your Data Scientists don’t have to worry about the data and can focus on Data Exploration and ML model training and testing.
We assist you in the selection of the appropriate storage medium (NoSQL), and set up data architectures for optimal loading and retrieval of data.

Deep Learning (AI)

We develop deep learning solutions. Think about object detection, anomaly detection and preventive maintenance.
We use Convolutional Neural Networks, Generative Adversarial Networks, Auto-Encoders and Long Short Term Memory networks.
Models are trained in the cloud on fast distributed GPU-accellerated machines.
We specialize in the development of Deep Learning models with Pytorch and Keras and deployment to Edge Devices.

How we work.



In general, we start with one or more workshops to gather as much info as possible regarding the business case.
The focus is on formulation of clear business goals, business questions, timeline, data sources, owners and availability of resources.
The final result is a definition of success agreed by all stakeholders.

This phase takes a couple of days but could be spread over 1 to 2 weeks.



The next step is to build a Proof of Concept that proves that there is a potential solution to the problem at hand.
Depending on the complexity of the business case, it's possible that this phase first requires extra tasks like data exploration, more in-depth interviews with key stakeholders, investigation of existing technology stacks and/or the available devops environment, etc.

This phase takes approximately 1 - 2 months.


Agile Delivery

Once the result of the PoC receives a GO, we enter in a delivery stage that follows an agile approach. Practical but not over-complicated. You decide what functionality is key and what needs to be developed first.
A backlog is defined and delivery is done in sprints, usually of 2 to 3 weeks. Each sprint cycle is finished with a sprint review and decisions for the next sprints.

This phase can take multiple months.

You, the customer, decide on the scope of the project and what will be delivered each sprint. This gives you the opportunity to remain in budget while still focusing on the key business goals of the project.