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How can AI help you grow your business?
Artificial intelligence (AI) solves various industry problems, offering innovative solutions to complex challenges.
- Increase revenue
AI helps you analyze social media, automate ads, and make smart sales choices. - Ensure data security
Machine learning detects fraud, such as credit card scams and identity theft. It saves companies money and protects customers. - Analyze large amounts of data
Machine learning algorithms analyze data to predict trends. They help companies make decisions about product development and sales. - Improve process productivity
Machine learning automates processes, saving time and costs. It predicts machine failures and recommends maintenance, avoiding downtime. - Improve customer satisfaction
Predictive analytics finds customer trends and suggests products. AI chatbots speed up resolutions. - Optimize supply chains
AI optimizes supply chains by predicting demand and managing inventory in real-time.
Our artificial intelligence (AI) capabilities
We provide consulting services and AI solutions to accelerate your business goals and enable sustainable growth.
Natural Language Processing
NLP solutions enable machines to understand and generate human-like language. Our NLP applications include chatbots, language translation, text content analysis, and sentiment analysis.
IoT and device automation
We innovate for companies through the use of IoT (Internet-Of-Things) in Industry 4.0 and the application of digital systems based on artificial intelligence.
Computer Vision
We help implement and integrate computer vision-based algorithms to train AI solutions and automate tasks such as voice recognition, image processing, object detection, and more – the way human visual systems can.
Predictive Analytics / Data Science
Utilizing Artificial intelligence, predictive analytics analyzes historical data to identify trends and make informed predictions about future performance. This capability is valuable for finance, healthcare, and manufacturing industries.
Generative AI
We use Generative AI systems to develop mathematical models and create different types of content, such as text, images, audio and synthetic data.
Chatbots
We understand the business need to offer dedicated 24/7 support, so we build chatbots based on artificial intelligence and integrate them into the client’s software – be it websites, mobile apps, etc.
Implemented projects in
/ Projects we are proud of
80% Increase in Detecting Suspicious Claims with ML Algorithms
Industry:
Fintech Technologies:
Python, R, Docker Description:
Learn how we built ML models that would make it easier to check a high percentage of scams in the insurance industry.
Model:
Team Outsourcing Duration:
4 months 
DL Model to Limit Customer Loss for One of the Largest Banks in Europe
Industry:
Fintech Technologies:
FastText, Keras, DyNET, Python Description:
For the client, which is one of the five largest banks in Europe, we built a deep learning model to predict customer churn.
Model:
Team Outsourcing 
Operational Efficiency for Debt Collection Companies
Industry:
Fintech Technologies:
R, Docker, Python Description:
For one of the largest debt collection companies in Poland, we have developed ML models for the valuation and purchase of debt packages.
Model:
Team Outsourcing Duration:
6 months 
ML in Automating the Risk Assessment Process in the Financial Industry
Industry:
Fintech Technologies:
R, Python, Docker Description:
For a client dealing with the financial security of companies in the transportation industry, we built a machine learning model for assessing the reliability of end customers.
Model:
Team Outsourcing Duration:
5 months 
Utilizing Machine Learning (ML) in the Optimization of Energy Use
Industry:
Energy Technologies:
Python Description:
For the client, a UK–based company providing solutions to optimize energy consumption, we designed a solution capable of building predictive models tailored to each institutional client.
AI Development Process – an iterative approach
When making a machine learning model, we go through many phases. They start with data collection and preparation. Then, we move to data training, evaluation, and continuous iteration.
This is a unique approach. It sets us apart from other companies because we repeat the cycle many times until we get good results.
We work closely with your team to create AI models and provide training for better understanding and efficiency.
I – Free consultation
The first step to understanding your problem is a free consultation, in which we first discuss your challenge and determine whether the implementation of an AI model would be effective and possible.
II Workshop – Define your needs and AI strategy
The second step is to conduct workshops online or on-site at the customer’s premises. They usually last 1–2 days. We’ll identify your problem, your data, and how it affects your application or company. Then, we’ll determine when your AI model is good enough.
Only after the workshop can we decide whether the project is worth pursuing—and then comes the second offer.
III Project – Feasibility study
(POC)
The third step is the implementation of the PoC. And here we can differentiate between elements such as:
1. Data acquisition and preparation
During this stage, we help collect relevant data from various sources, organize it, and prepare it for use as training data.
2. Model development
We choose the right model for the problem and train it using a training dataset, identifying the most relevant variables.
3. Evaluation and refinement of the model
We evaluate the performance of a trained model using a separate data set (test/validation set) that it has not seen before. Typical evaluation metrics include accuracy, precision, and score.
What will you get working with us?
01
Interactive PoC for your AI software.
02
Data-driven predictions that instill confidence in high return on investment.
03
In-depth understanding of methodologies to avoid common mistakes and maximize the data.
04
Consulting and advising on alternative business opportunities, architecture, and technology.
05
Choosing the right approach for your needs. You can cut costs by combining classical stats with AI / ML / DL.
What are the risks of not applying AI correctly?
01
85% of AI projects will produce flawed results due to biases in the data, algorithms, or the teams responsible for managing them.
02
Only 53% of projects go from prototypes to production.
03
Only 15% of AI projects will be successful.
How will VM.PL help you overcome these challenges?
- 95% of our AI projects are successful –achieving the set goals, on time and within budget.
- the remaining 5%, we put a quick and reliable diagnosis possible for missed targets to avoid unnecessary costs.
- 85% of projects go into production.
Technologies we use in AI solutions
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