We design and deliver tailored AI solutions that solve complex business problems with precision. Using advanced machine learning (ML) and neural networks (NN), our systems are purpose-built to deliver real-world impact — whether it’s forecasting demand, detecting fraud, or improving healthcare diagnostics.
Generatinve AI and language models often grab headlines, however, they are not the only valuable products in the field. AI brings business value in intelligent automation, predictive, modeling, and data-driven decision-making. Traditional machine learning (ML) systems enable the automation of complex repetitive tasks, forecasting key metrics, anomaly/fraud detection, risk estimation, and predictive maintenance. AI provides even an additional way to analyse data with methodologies like clustering. This kind of tailored AI solutions bring real competetive advantage to companies.
What we build:
AI, particularly through machine learning, adapts and learns from data over time, allowing it to handle complex tasks like decision-making, pattern recognition, and forecasting, whereas traditional automation follows fixed, rule-based logic.
While generative AI excels at content creation, predictive analytics and automation directly improve business performance, reduce costs, and provide tangible ROI. These solutions are often built on your own proprietary data and solve core operational challenges.
You’ll typically need structured historical data from systems like CRMs, ERPs, IoT devices, sensor measurements or other business/production data. We help assess data quality and prepare it for model training and deployment.
We begin with a discovery and planning phase to define your problem, assess your data and infrastructure, and outline the best ML strategy. From there, we move into development, testing, and integration.
Capturing information from documents or files and automating notifications and communication depending on the captured information.
It depends on the data and task at hand. There is a whole discovery process that we implement to find which algorithm can capture a particular problem and what is the best way to fine-tune it.
Predictive maintenance works by capturing and analyzing equipment data in real time to predict potential issues before they lead to equipment failure.
Clustering analysis or segmentation analysis is a versatile and exploratory data analysis technique that identifies natural groupings or clusters in data.
Machine learning costs include production expenses like infrastructure, cloud computing and storage, as well as machine learning integration costs.