problem
A global construction company wanted to be able to predict cost and time for building construction during design time. Using conventional statistics packages, they were only able to achieve 25% accuracy on average.
use case
Using data such as the number of floors, construction materials, construction type, floor area, location, and height of the building, NeoPulse® was able to predict the time taken to construct the building and the approximate cost.
Results
NeoPulse® was able to predict construction time and cost with an accuracy of approximately 90% – in other words, NeoPulse® could predict time and cost to within 10% of the actual numbers! Conventional RPA solutions could only achieve about 75% accuracy.
Using NeoPulse® only 14 lines of code were needed.
.gem-testimonials-navigation{display: block;}
Seong-jin Kim
Hyundai Elevator
CDO
We were able to successfully train an AI model to recognize complex industrial parts using Neopulse 3.0 on AWS. The AI solution was built very quickly and was able to recognize objects in unpredictable, real-world environments with high accuracy.
Serena Miran
Megazone Cloud
Manager
Everybody seems confident in AI, and they actually enjoy solving various AI problems.
Balachandran Rajendran
Dell, Unstructured Data Solutions
CTO
The vendor’s services are integral to providing AI solutions for a wider audience. They had an effective project management style, accented by a quick working style.
