companies have reached a new level of business operation and reduced costs by 2-3 times
our clients saved by cooperating with us
hours of manual labor replaced by AI ( 2 years 9 months)
A Canadian engineering company: optimizing the design of metal piles using an ML model.
Goal: To create an ML model that simulates the work of the engineering department in designing steel piles
Budget: 400 000 $
Result: Developed a series of designs that provide the same technical capabilities while consuming 25% less steel.
Savings for the business: 5M $ per year
An alternative energy producer in Canada/USA (Wind energy): creating an ML model to improve forecasting of electricity generation from solar and wind power.
Goal: Develop a more accurate forecast of electricity generation based on weather models.
Budget: 70 000 $
Result: Developed and tested an ML model that allows savings of $5-10 million per year through more efficient pattern forecasting.
Savings for the business: 5-10М $ per year
A Canadian engineering company: reducing the time required to analyze datasets affected by multipath propagation, shortening the analysis duration.
Goal 1: Optimize tracking of multipath probability.
Goal 2: Optimize the signal as it passes through the atmosphere. The information is transmitted via satellites at a speed of 1200 bit/second at a cost of $250-500 per bit/second.
Goal 3: Identify the positioning engine that is precise at the required time and continues in space.
Budget: 360 000 $
Result: Built unsupervised identification models with 80% accuracy.
Savings of 10-20% on data processing: Developed an improved signal optimization and modeling mechanism with an accuracy of up to 90%
Data transfer savings of $50 per second: Created a GNSS mechanism optimized and adapted to the client’s needs
Savings for the business: 1M $ per year
A Canadian electrical engineering company: precise forecasting of electricity production structure.
Goal: Forecast daily electricity prices based on production coefficients from various resources (Coal/Gas/Wind) +reduce the forecasting period.
Budget: 100 000 $
Result: Developed a successful Deep Learning Ensemble model that predicts the mix of electricity producers one day ahead for the large U.S. market. This is extremely beneficial for traders, enabling them to make informed pricing decisions.
Discussion of challenges and desired business outcomes
Analysis and preparation of a clear plan to achieve the goal
Selection of the most profitable solution based on business challenges and creation of a working AI prototype
About 3 months
Testing the functionality, design, and user-friendliness of the product
About 3 months
Testing and implementation of artificial intelligence into work
About 3 months
Effective solutions for complex tasks in the engineering, oil & gas industries using quantitative analysis, statistical methods, and geological data processing
Helps to obtain reliable information on parametric knowledge, unlike other advanced language models
Accurate classification of zoning and exploration data remotely through machine learning
Detailed geological research—satellite imagery analysis, seismic data, and sonar analysis
Collaboration with professionals in the relevant field to gain invaluable experience and apply practical knowledge
Our company is an advocate and driver of AI progress (XAI) and interpretable machine learning. We possess professional expertise and years of practical experience in solving complex problems and simplifying business processes
Replacing manual labor with automated systems to save resources
Optimization of internal systems to improve the quality of provided services
Implementation of ML as a forecasting tool to significantly increase profits
Implementation of ML as an analytics tool for making more accurate strategic decisions
With us, you get high-quality, simplified business processes with less time and resource consumption, unlocking new opportunities for financial growth and development.
