Machine Learning Consulting

Every day our company works on new machine learning consulting solutions that solve our client’s problems and adapt to future changes.

Our goal is to minimize the need for human engagement and automate time-consuming processes using machine learning and AI.

What is Machine Learning?

Machine learning (ML) enables computers to “think” and learn alike humans, basing their conclusions and future predictions on analysis of historical data and real-time data.

It is a rapidly developing technology that impacts almost every aspect of a business.

Machine learning consulting opens up a number of new opportunities for your company. You can:

  • personalize customer service,
  • automate processes,
  • implement solutions that will change the way customers interact with your product.

What do we do?

We build machine learning solutions for customer predictive analytics, predictive maintenance, computer vision, text processing, and full-stack BI or Big Data implementation.

Which industries can benefit from machine learning?

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Machine learning enables computers to “think” and learn alike humans, basing their conclusions and future predictions on analysis of historical data and real-time data.

The global Machine Learning market in manufacturing is expected to reach $16 billion by 2025. Manufacturing companies invest, among other things, in process automation and reduction of operating costs.

ML algorithms enable companies to take full advantage of the data.

Read our article about Machine Learning In Manufacturing.

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Machine learning and AI tools can potentially have a huge impact on various aspects of the gaming sector.

ML algorithms can dynamically respond to player actions. AI tools such as chatbots can be of great assistance to players, eliminating waiting for an answer, and providing suitable information.

A lot of companies are already investing in data analytics. You can also be ahead of competitors using AI and machine learning technology in the gaming industry.

Learn more about our innovative solutions from one of our case studies – mobile gaming.

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E-commerce – is one of the first industries that started using all the benefits of machine learning.

Recommendation engine and machine learning in the e-commerce industry directly converts into profits and increases companies’ market share with better customer acquisition.

Addepto machine learning consulting team has analyzed which solutions have the biggest potential today. They can help monetize your data and improve customer experiences like Asos and Zalando.

You will find all the information you need here – The Best Machine Learning Use Cases In E-commerce.


We’ve helped a logistics company to make the right business-critical decisions using full range of information.

We integrated data from multiple systems (ETL) into Enterprise Data Warehouse and implemented predictive models on top of that.

Logistics route was optimized and right business decisions were made thanks to deployed analytics systems.

Read case study.


Using the classification machine learning model and self-service business intelligence we helped a financial service company to optimize its pricing policy.

The model takes into consideration customer behavior, payments and in-app activity data and adjusts pricing depending on the customer’s financial profile.

Read case study.

Discover the rest of our case studies from various industries. Find out how Addepto solutions can help your business.

Machine Learning Consulting

Reliable data analysis and reporting will lead you to faster and more consistent development, but they require delivery and support from an experienced partner. This is where Addepto comes in.

We will help you enter the data-driven world and modernize your existing business analysis systems. We will help you extract insight from the collected data and turn it into profits and insights.

Addepto provides businesses with machine learning consulting for data integration (ETL), in-memory analytics, and innovative reporting.

How we work on ML solutions


Business meetings & data analysis

We start our collaboration from open discussions about our customer’s needs and goals. We do our best to understand how an organization operates and which machine learning solution would be most helpful for our client.

The next step is data analysis. Once we are granted access to necessary data, we use ML techniques to process it and determine which sets of data will be crucial for implementation processes.

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Planning & building ML solution

It’s time to turn your idea into reality. We build a prototype that resembles the end product and discuss what we like and what needs to be improved. This is the stage at which final changes can be made.

After we agree on a particular machine learning solution, our team leader will prepare a complex plan and put it into action. Your company will be informed about the progress and our team leader will be available to answer any questions that come up.


Testing & implementation

Once the final ML solution is ready, our team of machine learning experts runs advanced testing to make sure your company will receive a reliable ML tool. After the implementation process, your new machine learning solution will be fully compatible with your existing systems.

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Have you ever wondered why your customers stop buying your products or stop using your services?



Understand how you can use machine learning for small businesses for making it more efficient and profitable.


Machine learning algorithms can dynamically respond to player’s actions, offering a unique user experience.


Fraud detection, high-frequency trading, risk management, investment management – can be transformed thanks to artificial intelligence and machine learning. Find out how!

Technologies that help us build our ML solutions

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Python – a popular programming language, perfect for machine learning projects. It offers a wide community, access to open libraries, frameworks, and many more.

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PyTorch – this technology is mostly used in flexible and modular research.  PyTorch guarantees the stability and support necessary for production deployment.

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Amazon Kinesis – it is used to collect log and event data from various sources. The next step is processing the data, generating metrics, powering live dashboards, and emitting aggregated data into stores.

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TensorFlow – is an open-source library for numerical computation. Machine learning experts use TensorFlow for classifications, prediction, and many more.

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R – this programming language is popular among statisticians and data miners. It is used for developing statistical software and running data analysis.

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Hadoop – is a great provider of massive storage for any kind of data. Moreover, Hadoop has enormous processing power and can handle virtually limitless concurrent tasks.

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MongoDB – it is a particularly useful language for transactional stores where performance is a key. Thanks to schema-less operations you can update the data on the fly.

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PySpark – it is being used for processing structured and semi-structured datasets. Additionally, PySpark offers an optimized API that can read the data from a different data source that contains various file formats.

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Why you should work with us


Save money

You don’t need to invest in your own team of ML experts. We’ll build a custom solution for your business that will help you decrease your operational costs.


Team of experts

Addepto team consists of passionate ML consultants, highly educated and with years of experience in the field.


Increase customer satisfaction

Consumers tend to choose companies that invest in development and implement high-tech solutions.

Discover our machine learning use cases

The logistics optimization system has been additionally enriched with predictive modeling. A system for forecasting the market price of transport services has been implemented. The model was based on historical data (trend, seasonality) and macroeconomic information that had a direct impact on sales.

Due to the fact that the business has to plan the delivery several months in advance, the price forecasting algorithm allows to make more accurate business decisions, eg later but cheaper delivery of orders.

predictive maintainance with machine learning consulting

For a parcel delivery company, the main need was to create an algorithm that, based on historical data, would be able to forecast the daily sales volume for a specific customer (parcel lockers and courier). Currently, forecasts are made on the basis of calculating the dynamics for a given cohort (in excel), then it is assumed that all clients in a given cohort will have the same dynamics.

We have built a predictive machine learning model that is based on historical data analysis, macroeconomic factors, the impact of Covid and other third-party data. External data sources are integrated. The solution automatically forecasts monthly and weekly sales of individual products/companies. The models achieved up to 97% accuracy.

The solution helps managers in better and more accurate planning, saves manual work time, eliminates constant forecasting in excel and takes into account Covid-19 and macroeconomic factors that had a large impact on sales.

The logistics optimization engine was implemented using open source technology.

The main functionalities of the system are:

  • group orders into batches,
  • plans a calendar two months in advance,
  • updates the plan on a daily basis if orders information change or new ones are added,
  • choose the best day of dispatch for each batch of orders,
  • considering deadlines, incoterms types and client preferences,
  • calculates the loading speed based on historical information.

Minimize most important logistics costs:

  • transportation cost,
  • stock cost (loading and destination),
  • fuel consumption,
  • demurrage cost.

The user interface has been created at the top of the ML system.

The user interface enables quick and correct business decision-making by analyzing several logistics optimization scenarios. The user interface also provides the ability to manually manage batches of orders with automatic calculation of the delivery date, price, and other indicators.

For each scenario the most important department KPI’s are calculated:

  • Total transportation cost
  • Transportation cost per ton
  • Stock cost (loading and destination)
  • Orders delivered on time
  • Total volume for each delivery window

The system can also be used to perform ad-hoc simulations with manually entered logistic parameters.

machine learning consulting, user interface, data

machine learning consulting, user interface

Discover machine learning use cases for retail & eCommerce

Get a free copy of our ebook!

What will you learn from it:


  • How ML technologies can help your business
  • ML solutions that have proven successful in the eCommerce business
  • The most popular machine learning solutions for retail

To receive a copy of our ebook, please complete the form on the right

You will receive your free copy immediately.

Companies that trusted our ML solutions

“Addepto offered an individual approach to our needs and high-tech solutions that will be efficient in the long term. They conducted a detailed analysis and were open to trying out innovative ideas.”


Przemysław Piekarz

Sales Analysis Manager, InPost

“Addepto has an individual approach from the very beginning. They are open to change and ready to face difficulties. The effect of their work was satisfactory and ready on time.”


Bobby Newman

VP Engineering, J2 Global

“Based on our business requirements and available data, their team created innovative solutions from scratch. Addepto analyzed business processes, enabling them to build a solution perfectly suited for our company.”


Paweł Żelazko

Head of IT, SMEO S.A.

We support companies from various industries all around the world with machine learning consulting solutions

FAQ about machine learning consulting

What is machine learning (ML)?

Machine learning (ML) is an AI-based technology that uses data analysis to automate analytical model building. Systems driven by machine learning can identify patterns, learn from data and based on that make predictions and decisions without a need for human supervision.

How machine learning works?

Machine learning process starts with formulating a question. That’s the first step.

Next, is to find an understanding of available data which is followed by cleaning the data and feature engineering.

What are the steps in the machine learning process?


  1. Obtain the data.
  2. Preparation and cleaning of the data.
  3. Train your machine learning model.
  4. Test ML model.
  5. Introduce changes, improve your machine learning solution.

What business problems could we solve with ML?
  • Spam identification
  • Product recommendation engines
  • Customer segmentation
  • Image recognition
  • Demand predictions
  • Virtual personal assistants
    … and many more.

What changes in machine learning can we expect in the future?


In the future, we can expect significant development in ML-driven technologies such as computer vision and natural language processing (NLP).

The progress we’ve made from 26% error in 2011 to 3% error in 2016 is hugely impactful. The way I like to think is, computers have now evolved eyes that work. — Jeff Dean

We can expect that machine learning solutions will soon offer 100% accuracy and support humans in various areas, both in business and everyday life.

Which programming languages are used to build ML solutions?


  • Python
  • R
  • JavaScript
  • Julia
  • LISP
    … and more.

What are various commercial applications of machine learning?

Demand Pricing

Demand pricing is a machine learning-based solution that enables companies to flexibly price products taking into consideration factors such as demand at the very time of the purchase, the level of interest of the particular client, customer engagement with marketing campaigns, etc.

The price optimization strategy is an effective way to increase revenue.

Recommendation engine

Recommendation engines are one of the most popular machine learning applications. An ML-driven software suggests to users different content that will best fit their interests, based on the data about their past choices.

Recommendation engines are proven to be effective in increasing customer engagement and satisfaction which results in reduced churn.


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