Leveraging Machine Learning Success Story
Modernizing Construction with Machine Learning
Learn how a construction company leveraged Jalasoft's expertise in machine learning to enhance their platform.
Summary: Key Insights and Achievements
The company turned to Jalasoft to replace their former provider and take their preconstruction platform to the next level. By leveraging Jalasoft's technical expertise and collaborative approach, the partnership led to platform optimization, a better user experience, and accelerated development cycles.
Industry
Construction
Partnership length
3 years
Engagement model
Dedicated Team
Team size
60 engineers
A Machine Learning Challenge
The company faced the challenge of replacing their former provider while adhering to a strict transition plan. They needed a partner with a proven track record, experienced engineers, and the ability to scale quickly to meet their evolving needs. The challenge was not only about maintaining continuity but also about pushing the platform forward with enhanced capabilities.
Jalasoft’s Approach: Scalable Delivery with Built-In Agility
Onboarding training sessions
Living documentation
Mob programming sessions
One-week agile sprints
Each new team member participated in a structured onboarding process, including walkthroughs of the environment, product architecture, and development workflows. This enabled fast ramp-up without compromising quality.
Engineered for Excellence: The Team Behind the Work
Jalasoft deployed a team of 60 highly skilled engineers:
Machine learning experts
Automation engineers
DevOps
Python engineers
C# engineers
.Net engineers
Google Cloud Platform experts
Platform Enhancements and Key Outcomes
Faster validation cycles with AI automation
Accelerated software development
Rapid MVP delivery with full team setup
Flexible tech stack adaptability
Bridging knowledge gaps through documentation
Enhanced image processing with Machine Learning
By integrating AI-driven testing strategies, Jalasoft enabled quicker validation of new implementations and rapid detection of bugs post-merge, significantly reducing manual effort and accelerating delivery cycles.