Sigli & Allkind: Unlocking AI to Drive Efficiency and Customer Engagement
Sigli upgraded Allkind’s infrastructure with the latest AI functionality, leading to a sizable boost in backend efficiency, user engagement and positive reviews.
Bolstered by AI, Allkind continues to pursue its mission of creating opportunities for individuals with additional needs through innovative digital solutions.
Snapshot
15% uplift in user engagement
20% more active users
40% increase in positive testimonials compared to previous period
5x boost to core backend performance
Allkind Group: Supporting People With Additional Needs
Industry: Technology services for people with disabilities
Size: Medium enterprise (100+ employees)
Location: Belgium and the Netherlands
Allkind Group is a collection of companies driven by a mission to help people with disabilities and additional needs thrive through accessible digital platforms. Based in Belgium, it has over 1,000,000 users and employs 100 people.
Allkind has three brands, Lexima, Sensotec, and Schoolsupport. Lexima focuses on mathematics teaching for young people, Sensotec provides tools for those with hearing and visual impairments, and Schoolsupport is a school e-learning solution.
The Problem: Missing Out on the Opportunities of AI
Leadership at Allkind was keen to take advantage of the latest AI and machine learning (ML) functionality. They saw its clear potential to drive internal efficiency and improve the customer experience across all of its solutions.
Equally, Allkind knew integrating AI into its services was essential from a competitive standpoint. Even in 2022, AI was being added to educational tools at a rapidly increasing rate. Ignoring this trend would have been a definite path to disgruntled users and a loss of market share.
The Brief: Full Backend and Customer-Facing AI Integration
Allkind approached Sigli to build AI functionality in three areas:
Core backend functionality
AI-based customer features like personalization and smart tutoring
Customer service helpdesk
The brief was ambitious in its scope. Allkind wanted comprehensive AI integration across all of their companies and every product in their portfolio.
At Sigli, we knew this would require a comprehensive strategy. We needed to pave the way for both a complete technical solution and a new style of interacting with and thinking about AI-based workflows.
The Process: Coordinating Multiple Development Projects
The initial development phase started in 2022 and lasted for six months. Our relationship then transitioned into a long-term cooperation centered around refinement and ongoing improvement.
Service Package
Sigli provided the following services:
AI solution architecting
API development
Custom software development
DevOps managed services
Cloud engineering services
ML development
Data analysis services
Data engineering services
MLOps services
Application quality assurance
Tech Stack
Applications and databases were deployed primarily on Azure Cloud Services, with development using .NET, Python, Angular, TypeScript and NodeJS. In addition, we used several data platforms—Azure SQL, Data Factory, Data Lake, Data Bricks, and Synapse Analytics. PyTorch and TensorFlow were the two ML frameworks used.
The Development Team
The team was made up of four AI engineers, three full-stack engineers, three frontend engineers, and two quality assurance specialists, alongside a solutions Architect and Delivery Manager.
Communication
Communication was through a variety of regular in-person and virtual meetings. Email and messaging was ongoing throughout the project.
Project Management
We used the scrum framework for project management with a Product Owner (PO) for oversight and communication. Regular rituals in the form of daily meetings, sprint planning, and retrospectives were a key part of our approach.
The Results: Complete AI and ML Functionality
Here’s what the project looked like at completion:
Integrated business analytics: A central platform that combines data from multiple sources and provides a high-level overview of operational and customer activity. The platform stores, processes and analyses data, providing detail-rich insights that assist with decision making.
AI architecture: Full software architecture that meets the best standards of educational systems in the Netherlands, Belgium, and internationally.
Product ecosystem management: An integrated system that makes it possible for different products to work independently while sharing data.
AI enrichment: AI functionality for every product based on their roles in the customer ecosystem and an understanding of features that research showed would best serve customers.
Full-feature ML solution: A multifaceted ML solution that includes predictive analytics, sentiment analysis, progress tracking, automation of manual tasks for teachers, and more.
Quality assurance: Testing of functionality and precision of ML outputs both throughout the development process and after deployment.
In addition, Sigli was kept on as a partner to maintain and continuously improve Allkind’s solutions, building on the firm bedrock put in place.
The Stats: Improved Efficiency, Engagement, and Satisfaction
“If I have just one Sigli developer on my project, I have all the intelligence I need in the company. Sigli’s deeply collaborative culture means mutual assistance and knowledge sharing is on hand whenever there’s a significant challenge.” William De Prêtre, Head of AI, Allkind group
Sigli saw an array of improvements in key operational and customer metrics. In addition to providing Allkind’s users with AI-enhanced functionality that bolstered engagement and satisfaction, Sigli optimized the speed and precision of internal workflows and core infrastructure performance.
15% ↑
User Engagement
20% ↑
Active Users
40% ↑
Positive Testimonials
5x
Boost to Core Backend Performance
Want to See the Same Results as Allkind?
Are you eager to see the benefits of AI and ML in your own company? Get in touch today to learn how Sigli’s developers and AI specialists can enhance and upgrade your tech infrastructure.
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