AI in transportation and distribution
AI in last mile-logistics and parcel delivery
AI in warehouses

Software Services for the Logistics Industry

There are a variety of artificial intelligence solutions that can be tailored to different industry needs. To make sure you get the most out of your project, our AI Experts will select the best approach for the specific needs of your business to build resilience and a competitive edge. Nexocode provides a range of services to help logistics companies and supply chain organizations. These include:

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Our Case Studies

nexocode is a company that offers AI development services dedicated to the logistics and supply chain industry and has a broad portfolio of successful projects within these fields. Our AI applications can bring operational success to various logistics and SCM fields. For our clients, we develop solutions that improve efficiency, optimize processes, reduce costs, and increase productivity. Check our portfolio of successful implementations below.

Logistics Sector

Scheduling deliveries by predicting an optimal time of delivery for last-mile logistics

Predictive scheduling for last-mile distribution and route planning based on an AI model that predicts delivery success?
Challenge Accepted!

Predictive Analytics

Logistics Sector

Developing a logistics platform offering real-time visibility and integrations with different carriers

Performing business analysis to create a supply chain management optimization product perfectly tailored to the client’s needs?
Challenge Accepted!


Healthcare sector

Maximizing savings by streamlining the procurement of medication across the hospital network

Creating a new version of an existing stock control solution, to process millions of medical records from external supplying systems within seconds?
Challenge Accepted!


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Artificial Intelligence in Logistics, Transportation, Shipping and Supply Chain Sectors

Technology impacts every aspect of our lives and plays a strategic role in the logistics industry. Applications of AI within the sector are a logical step forward. Artificial intelligence can dramatically improve a business's performance, whether it is a small or a large enterprise. It will become a game-changer for the entire industry.

Within the transportation, shipping, logistics & supply chain industries, AI technology stands out as a solution that can help businesses improve productivity by automating tasks, streamlining processes, increasing resource allocation, and providing better data management through actionable insights.

Given that there are several different AI applications available on the market for logistics companies today, it is essential to understand how each solution works to make a more informed choice about which one is right for a particular business' needs. Below you can find a brief introduction to AI applications for the logistics industry.

AI benefits in logistics and chemical supply chain

Benefits of AI on every step of the supply chain operations flow

Smart Freight Matching

Shippers use various business networks to find the best carrier for their goods. However, most matching is carried out manually by posting and searching through offers within the network. The ever-growing number of offers and needs makes finding a suitable match within a budget challenging. Artificial intelligence can be applied for a more intelligent freight matching with a carrier. Machine learning algorithms identify a carrier for a particular load based on available information - the origin, destination, commodity type, weight, size dimensions. Thanks to that, the model can select a shipper with a carrier that offers service at a time, place, and price suitable for both parties.

Efficient and Sustainable Last-mile Logistics

Optimization is a key element of any logistics network. It helps to deliver goods in the most efficient way, which will result in reduced costs and improved service quality for customers. For example, a company that has a warehouse in two different cities may need to decide between sending a driver from one city to another without any return trips, taking a load directly from the production line in the other town, or even solve a challenge of combining both routes into a single one.

A great example of a logistics task where AI can be used is a routing problem, which involves finding an optimal path throughout a given network that meets required delivery times while following specific rules or restrictions.

Routing optimization algorithms generate a set of solutions (routes) that define the best possible delivery path, considering various parameters like loading times, freight, restrictions on the load or carrier capacities. Route optimization helps choose an optimal way for each vehicle depending on its capabilities, traffic conditions, and the availability required throughout the journey. The model considers all possible variables before coming up with an answer, thanks to historical data that already have been used by thousands of vehicles daily.

Efficient route optimization enables lowering transportation costs through minimal fuel usage, optimized schedules, and efficient allocation of drivers and trucks.

Efficient and Sustainable Last-mile Logistics

The e-commerce industry is a fast-growing market in the twenty-first century. It has a massive impact on the entire logistics sector. Online shops or e-commerce platforms greatly need fast and cost-effective goods delivery to their customers. What is blocking them? It is mainly the challenge of efficient last-mile logistics. Last-mile delivery is a complex choreography of all the participants involved in a supply chain - a fleet operator, a courier, a freelancer who owns a car, or a scooter. The last mile delivery constitutes a good part of the final price due to its high costs, mainly affected by fuel expenses, driver wages, and vehicle wear. AI can help reduce all these factors thanks to many solutions.

AI has a lot to offer for other significant problems and cost drivers in last-mile logistics like failed deliveries, returns, and deliveries complaints. Solutions for last-mile deliveries operate within route and schedule optimization, fleet management, and predictive analytics that enable intelligent planning.

Optimizing Fuel Consumption and Emissions Reduction

Autonomous vehicles can bring a sizable reduction in costs due to a high volume of delivery. It is a crucial factor for last-mile deliveries where a volume is lower and a cost per unit is higher compared with a traditional supply chain. The last mile logistics market has a lot to gain from the use of autonomous delivery solutions. Driverless technology can be used both for shorter range or local distribution which involves a small number of stops, as well as long-distance distribution.

Self-driving cars or trucks are a solution that can significantly reduce the number of accidents with no or a low number of casualties. They also can be used to carry dangerous materials like flammable cargo which requires a special permit and a higher level of safety before being allowed on public roads without a human driver.

Autonomous Ships and Port Operations

Just like for land transportation, autonomous vessels and port machinery are a big area of ​​interest for a number of large shipping companies. They are a key element to improve productivity, efficiency, and safety in shipping and port operations. AI may assist a ship captain with docking a vessel at the pier, monitoring risks of collision or grounding, handling cargo operations that require a high level of precision, and much more.

AI-enabled devices can also be used to reduce costs and save time during container loading and unloading processes. A significant part of activities at a terminal is automated with the use of various types of equipment like cranes or reach stackers that can easily be operated by an AI system as well as machine vision technology such as Automatic Guided Vehicles (AGVs).

Automated Warehouses

Another example of an excellent benefit for the logistics sector is a fully automated warehouse. Logistics companies worldwide are facing a lot of challenges in warehouse management. It is a costly business, and a simple mistake can be a reason for a considerable loss. In addition to that, it is becoming increasingly difficult to find a skilled workforce that will support warehouse operations, especially with growing demand and the seasonal aspect of the workforce needs.

That is why more and more logistics managers decide to move towards warehouse automation by using robotic devices which operate with no human intervention. The most common use cases include automatic storage and retrieval systems (AS/RS), autonomous mobile robots, order picking machines guided by laser or RFID technology. AI systems can be used to navigate a warehouse space, identify a needed good in an inventory, pick it up and place it in a storage location or a delivery vehicle with no human intervention. A task like this currently requires a person to be present, which makes it risky and time-consuming.

AI-enabled warehousing solutions are expected to help companies decrease their expenses related to activities performed by human specialists working in warehouses, including employees' salaries, physical risks due to human error, fatigue resulting from long periods of standing on picking positions or operating a forklift, or other types of equipment that can be automated.

Inventory Management

A solution that many companies are looking forward to getting is a machine learning-based inventory management system. It can increase a volume of a product a warehouse can hold by accurately predicting demand for specific products and trigger a restocking process before a good becomes obsolete.

AI-powered warehouse management may greatly benefit a company by several key factors such as a quick response to a customer by providing a required good and fast reordering process. This is a critical success factor for a supply chain management sector because an out-of-stock situation can cause a loss of customers and revenues. An intelligent inventory system could also forecast stock levels or even identify products that require a replacement or an update. A data feed from other business units within the same company may help with this process, so it would not only provide information about sold goods but also unnecessary stocks, which can, later on, be used as a source for new items throughout the entire supply chain performance.

Integrating AI technology in warehouses can also help in quality control by supporting various operations with an automated visual inspection, a quality check for many products in a warehouse. Machine vision can also be used to monitor a product's condition in a way that cannot be done manually.

Dynamic Pricing Models

Prices of logistics and transportation services are fluid, and accurate price setting is a must whether you're a service provider or a buyer. The prices for a given route or a service depend on a number of factors such as a level of demand, time, weather conditions, and even geopolitical issues.

AI-powered solutions can help not only with adjusting prices automatically but also provide insights about reasons behind them, like demand changes, delays, disruptions, and overcapacity, which will assist managers with making decisions about future business plans and price settings.

Dynamic pricing of logistics services is an excellent example of a domain where AI can be a game-changer. A company that wants to implement a dynamic pricing solution needs data about performance, both historical and recent. It should also consider seasonal variations or current demand for a good and competitors' prices to create demand forecasting models. All these factors combined should help an intelligent system improve its price estimates to provide a customer with the best offer for their order. If artificial intelligence models are used, it may even find patterns within this data, thanks to which it can constantly adjust its recommendations depending on specific preferences of individual customers, like seasonality or location.

Planning Distribution

Planning a distribution of a large number of goods between several warehouses is a time-consuming task handled by many distribution specialists and fleet managers supported by complex IT platforms. The distribution process can be improved with the help of AI, which will allow a company to get insights about a demand for a given product, desired schedules, possible routing, and fleet, analyze data and plan distribution more precisely and in a shorter amount of time.

It is one of the most complex tasks in supply chain management because it requires multiple resources, constraints, and other factors to be considered. A classic optimization problem for this domain would involve defining needs and schedules for freight, fleet, people, demand, and other multidimensional factors.

AI models used for this problem operate within predictive analytics and prescriptive scheduling supported by other, more-targeted solutions. An algorithm for this type of process should be able to prepare a plan taking all possible interactions into account. It means a model should consider not only a sequence of a planned activity but also a way they are connected together within the supply chains. Technology can help with this process by providing an AI solution that can learn from historical facts and make predictions about future events. ML models can do a great job finding patterns within a massive amount of data that may be used as actionable insights or statistics to run simulations for various scenarios. They can be optimized for multiple targets like cost-effectiveness, timely deliveries, warehouse space reduction, or a mix of all, depending on the needs of particular business specifics.

From descriptive to predictive and prescriptive transportation fleet management

From reactive and real-time visibility of the fleet, cargo details, schedules, etc., to predictive and prescriptive modeling for fleet and route optimization.

The results obtained from this analysis build supply chain resilience. AI technology assists managers with making business plans and calculating costs to develop a strategy that would work best in accordance with their goals. It might also enable them to adjust the entire supply chain by eliminating unnecessary inventory or improving processes within specific areas like warehousing or scheduling, limiting operational costs. With a specialized predictive planning system, a logistics company can optimize such decisions as several different factors may be taken into account, like costs, delays, safety, traffic, or weather conditions.

custom end to end predictive analytics solutions for smart manufacturing

Predictive modeling enables production and distribution optimization through better throughput, quality, safety, and yield improvements. The end-to-end custom implementation of a solution that interprets data provides visualization and enables custom automated actions to streamline logistics and supply chain networks is essential to take SCM to the next level.

Predictive modeling enables production and distribution optimization through better throughput, quality, safety, and yield improvements. The end-to-end custom implementation of a solution that interprets data provides visualization and enables custom automated actions to streamline logistics and supply chain networks is essential to take SCM to the next level.

Securing Supply Chains

AI can be used to provide logistics security services that monitor a complex system of users and devices for suspicious activities, unusual events, or anomalies in activity patterns. This is achieved through a series of layered algorithms designed for anomaly detection and can alert about an event even when the data show no obvious indication for concern. This approach enables us to deal with potential threats before they become actual threats.

Planning shipment of containers - Predictive Scheduling

Machine learning models provide a great way of finding patterns within a large amount of data and help optimize the decision-making process by simulating several scenarios. Predictive scheduling is a good example of a process that can benefit from machine learning. Planning a shipment of containers is one of the most complex logistics tasks that require a large number of multidimensional factors to be taken into consideration.

Predictive scheduling in maritime enables planning shipments for cargo that are built upon a set of known factors and prior experiences which cover a larger group of shipments. Data collected from a wider range of resources can provide a more comprehensive context to work with and will allow a model to make well-informed decisions about future events. Machine learning models can help with this task by using historical data about shipments, ordered and predicted loads (supported by demand forecasting), or other types of recent events which may be related to the current activity. Predictive scheduling models aim to find patterns within data, calculate probabilities, and make reliable estimates about future events.

Predictive scheduling is a technique used in several shipment applications that provides a better quality of service to customers by making a plan for an optimal delivery route, schedule, and containers allocation. This information will be helpful in preparing a schedule that maximizes efficiency and time-effectiveness, even taking recent events into account like delays at the docs or weather conditions.

Managing Vessel Capacity and Organizing Containers Positioning

Containers vary in size, weight, and intended destination, and cargo ships move hundreds of containers at once. These differences make the order of placement crucial to improving productivity. Some of these are incredibly urgent items with a specified time limit in which they must be moved to a new port. On the other hand, others have longer transport durations and are not considered essential or rapid-transport goods.

On the subject of container shipping, there are several things to consider. The ability to determine and distinguish containers and products according to when they will be delivered is critical. This can ensure that goods with a crucial need receive priority over other items and are loaded on the vessel as soon as possible, simplifying trade flows between ports.

Furthermore, by adequately distributing products among the hundreds of vessels that pass through a port, terminal traffic may be decreased, optimizing schedules and reducing shipping costs. Managing vessel capacity and container positioning is an integral part of the shipping process that AI capabilities can optimize.

Using a number of metrics, AI analysis can determine the best place to load a container on a vessel. The task involves a lot of variables that are different for every ship, its size, location, time constraints, and other factors, which means that a solution will have to be specialized according to specific parameters. Machine learning models can plan container positioning by analyzing a number of factors about a shipment, a vessel, or ports on the route. An innovative solution to this problem is a system capable of understanding the whole picture by considering a bigger range of variables related to a specific ship at a given time, including weather conditions, time constraints, or even political events that may impact the whole picture of the voyage.

Predictive Maintenance

Another business application for machine learning is a predictive maintenance system that analyzes several factors like a component's history, the environment in which it operates, or variables within a part itself to determine when a piece of machinery will require service. Predictive maintenance solutions can be applied in all stages of supply chain operations, wherever there is machinery involved (ships, trucks, warehouse machines, port machinery, just to name a few examples). An efficient predictive supervision model can support users by identifying the cause of an issue and its severity. This information helps prioritize repairs, thus optimizing time management and reducing costs related to unplanned downtime.

For example, many algorithms consider previous levels of wear at similar points in time, environmental factors that may lead to problems, how long parts have been in use, etc., to predict future performance. A machine learning model can help a company plan a schedule for regular machine service relevant to a manufacturer's recommendation or a service technician's schedule.

When a machine is in a critical condition, and a delay may lead to a breakdown, a prediction model can assign a task of performing emergency maintenance and replacing a broken part before it fails completely. This action allows for the immediate continuation of productive activities without any downtime.

Predictive analytics solutions for parts/machinery wear out that support this type of decision-making could help minimize machinery downtimes related to unplanned repairs by reducing the need to make such decisions manually by operators themselves and limiting accidents levels. Using these systems as a tool for predictive maintenance, companies can reduce equipment downtime and thus increase productivity across entire supply chains operations.

Demand Forecasting

Predictive analytics solutions can also be valuable for demand prediction and global supply chain planning. For companies operating in a competitive environment, such a model can help minimize risks related to a lack of inventory by producing a highly accurate demand prediction.

As a result, a firm can better prepare itself to meet an increase in demand and avoid shortages, especially when the goods under consideration are slow-moving and far from a warehouse. Having access to reliable data about future trends and events gives organizations time to make necessary changes, including acquiring more supplies or expanding production capacity if required.

With this information, businesses can optimize their supply chains processes with improved accuracy, which can significantly lower operational costs due to more efficient operations.

Predicting sales based on patterns in demand

Predicting sales based on past patterns in demand to optimize the production and transportation processes.

Some predictive models learn almost instantly on a massive data-set and can produce forecasts in real-time. While a micro-forecast is a short-term prediction that helps a company adjust its inventory for a specified period; it means that the model would be able to issue warnings about potential supply chain problems with high accuracy.

Demand forecasting is essential when it comes to planning capacity and schedules and evaluating optimal pricing strategies. By taking a market forecast into account, a company can define the optimal price that will cover costs of service and ensure a profit margin and attract a maximum number of customers, thus maximizing sales figures.

Predictive analytics solutions can also be applied to inventory management processes. They leverage a considerable amount of historical data about customer behavior to optimize lead time through proper merchandising (e.g., schedule replenishment according to real-time supply and demand). Thus, a company can deliver goods in time without the risk of potential material shortages or a surplus that may lead to additional costs.

The data about historical demand patterns can be used to forecast future demand for a product enabling a business to fulfill orders faster, improve its supply chain optimization processes and increase customer satisfaction which helps attract new clients thus improving the bottom line by generating more revenue.

Streamlining Backoffice Operations with NLP

How can AI extract data from any document with intelligent document processing

How can AI extract data from any document with intelligent document processing?

Solution providers in the logistics industry are also integrating Natural Language Processing (NLP) capabilities into their offerings to leverage a considerable amount of document, written communication, and unstructured text data available in a particular company. Such a system can rapidly extract the necessary information for a specific business to enhance its back-office automation.

Processing invoices, bills of ladings, customs declarations, purchase orders, packing lists, and all other documents a logistics company needs to handle daily is a very time-consuming and labor-intensive process. As a result, a lot of money is wasted through manual human work and many mistakes made in documents processing which could have been avoided if the data was automatically extracted from a document by a computer.

Depending on a business' needs, NLP can solve a specific problem or provide a complete technology solution for automating an entire back-office function using natural language processing and custom software development. NLP solutions can be applied for automated reading and extracting specific data from documents (natural language understanding - NLU) or for generating new documents (natural language generation - NLG).

Further processing of extracted or generated data can be applied to build more comprehensive solutions that cover broader operational processes. For example, a company can use NLP to extract an order ID from a bill of lading, allowing it to process the shipment by generating a PO and triggering a number of processes, including transportation management systems for booking transport and then handling invoices.

In general, extensive NLP solutions for intelligent document processing (IDP) can assist logistics service providers in a number of back-office processes which include generating documents, sending automatic alerts, performing automated data extraction (e.g., searching for a particular word or a combination of words in the text), correlating events with other information like images or documents, etc.

Also, most companies face high volumes of emails or support communication daily, including various requests, feedback, opinions, etc., that are a valuable source of data for a business. Traditionally, manual replies and processing such data was a time-consuming task requiring effort, expertise, and organizational resources.

Introducing NLP solutions in a messaging system allows an organization to extract information from unstructured text automatically. It can enhance customer service by resolving requests faster, providing more accurate responses based on knowledge about an issue or a request, etc.

NLP systems can also be applied to automate many functions that are not related directly to business but may affect a company's operational efficiency and customer relations management (CRM). For example, it is possible to integrate automatic sentiment analysis into automated answering systems built on top of a chatbot platform.

Automating Routine Tasks - AI-powered Robotic Process Automation

Robotic process automation (RPA) is a software application that can automatically perform several routine, rules-based tasks a user or a group of users in a company were previously performing manually.

RPA can be used to automate manual and semi-automated business processes that require some level of human interaction, such as finance and accounting operations, data entry, document processing, scheduling operations, etc. The technology called cognitive automation enables an organization to utilize the existing resources more efficiently by freeing employees from time-consuming and tedious work so they can focus on higher value-added activities instead.

For example, an individual working with financial reporting can automate a lot of their mundane work like data entry by using RPA solutions to develop a virtual workforce that will natively work within a company's IT infrastructure. RPA solutions also save a lot of effort and money (because you don't need employees to spend time on repetitive tasks) and speed up business processes while maintaining accuracy and security.

Benefits of AI in Logistics

AI in logistics and supply chains provides a range of benefits, including:

  • optimized routes that result in lower fuel consumption and timely deliveries
  • lower cost of transportation due to more efficient use of a fleet and space allocation
  • increased revenue thanks to higher customer satisfaction through a better service
  • fewer errors in a product delivery due to a more advanced data analysis
  • lower accidents rates and downtime thanks to proactive parts/machinery servicing
  • optimized inventory management due to a more precise forecasting
  • higher income thanks to dynamic freight pricing
  • increased security of a supply chain with a real-time monitoring
  • reduced needs for the human workforce, especially for repetitive tasks and those where autonomous vehicles and machines come in place
  • enabled a faster reaction to changing market conditions and a growing number of online customers

How Can Nexocode Experts Help?

nexocode is a leading provider of software solutions for the logistics industry with a portfolio including artificial intelligence-powered fleet management software, intelligent route optimization solutions, dynamic pricing models, +real-time monitoring systems for transportation providers, and other cutting-edge technologies explicitly designed for this sector. nexocode's AI logistics solutions for this industry were developed by a team of experienced and highly-skilled software developers with a deep understanding of the challenges faced by modern supply chain managers.

Wojciech Gębiś

Wojciech Gębiś
Project Lead & DevOps Engineer

Dorota Owczarek

Dorota Owczarek
Design Thinking Facilitator & Product Lead

Oleksandr Zakharchuk

Oleksandr Zakharchuk
Machine Learning Expert & Advisor

What Nexocode Can Offer as a Boutique AI Development Company?

What Nexocode Can Offer as a Boutique AI Development Company?

Bring AI solutions to life. Nexocode team serves clients at every level of AI maturity. Nexocode's offering covers end-to-end artificial intelligence services from AI strategy, advisory, and consulting to implementation and ongoing production support.

AI Design Sprint

AI Design Sprint

Identify AI opportunities and use them to build resilience and business results. We organize AI Design Sprint workshops where you can unleash the artificial intelligence potential and create new value for your business. We will show you how to benefit from artificial intelligence by focusing on people. AI Design Sprint is a low-investment kickstart that will uncover solutions to increase revenues and profits, improve user experience, and much more in just two days.

Proof of Concept Development

Proof of Concept Development

Move from the AI Design Sprint towards your first deployment. Nexocode will develop a competitive AI-based solution that solves your company's problem, covers your business needs, and collects and analyzes feedback from testers. Proof of Concept guarantees a much smaller risk of failure, as it only takes a couple of weeks to develop. Still, you can quickly observe what AI brings and decide whether the achieved results look promising enough to follow up with further investment so that you can define your next key goal.

Data Quality Audit

Data Quality Audit

Data represents a new frontier for competitive differentiation. Nexocode offers professional data analysis for your model proposal. We will suggest a data collection strategy to make your model more accurate and provide value to your company. With your data house in order and a transparent process for data management, you will be able to shape your data into an actual business asset.

Building Data Systems

Building Data Systems

Unlock the value of data with a clear data strategy, process, and technology that supports it. Nexocode will keep you in applying professional MLOps handling Big Data. Practices we use will help guarantee that you can reliably build and operate your artificial intelligence solution at scale. We help our clients to improve their competitiveness by digitizing operations with data-driven insights from deep learning algorithms, which are capable of analyzing huge volumes of unstructured data at record speed.

Custom AI Software Development

AI Consulting

With vast expertise in implementing AI systems at scale, the team at nexocode reviews your problem and provides consultations on approaching further steps to complete your business goal and making an impact. We will help you grow beyond the proof of concept stage and implement AI at scale.

AI Consulting

AI Consulting

With vast expertise in implementing AI solutions, the team at nexocode reviews your problem and provides consultations on approaching further steps to complete your business goal and making an impact. We will help you grow beyond the proof of concept stage and implement AI at scale.

A development process that brings AI results in weeks, not years


2 hours

Explain your business and share your pain points to gain insights into AI capabilities and an approach designed by our nexocode experts.

AI Design

2 days

Identify a high-impact business problem and collaborate with nexocode experts to propose an AI solution.

Proof of AI

4 – 12 weeks

Rapidly build an AI model that solves your problem and subsequently test machine learning algorithms on your datasets.

AI Application Deployment
in Production

3 – 9 months

Build automated pipelines, scale, and deploy your artificial intelligence app into production. Adjust algorithms to meet your desired benchmarks.

AI Development - Your Questions Answered

Are artificial intelligence services right for my business?

AI Adoption is not bound by industry boundaries; it is suitable for any type of business no matter what the industry is. The most important factor while determining whether an enterprise should opt for AI technology is an early evaluation of AI opportunities, value AI solutions can bring, and feasibility assessment. A company can do this evaluation quickly with AI Design Sprint workshops. In just two days you can learn and understand the power of emerging technologies, spot AI opportunities, run early data quality inspection to assess feasibility, and prepare an implementation roadmap.

Will AI bring value to my company?

Companies want to invest in AI development to bring tangible value for themselves. But there is no guarantee that a custom AI software will get this expected value or the total cost will not be too high to handle. That is why a detailed planning phase and an iterative approach are crucial in every AI-based project. Only in this way will you be able to predict the cost, income, and break-even points of your development. There are ways to reduce the risk of not getting value from solutions that use AI. nexocode's approach is based on a deep understanding of the needs of the projects and the possibilities of current AI technologies coupled with the iterative approach. Our project methodology focuses on delivering value as early as possible and starting small with AI Proof of Concept development.

You can find out more on how to turn AI into a profitable investment in our article on the ROI of AI. Our team will help you leverage the latest AI tools and methodologies to create practical data-driven applications that deliver real business value.

How do you estimate an AI project?

It's difficult to give an exact estimate for artificial intelligence solutions. Determining the cost of the project is affected by a variety of factors, including what problems your organization is attempting to address, which artificial intelligence technologies, software, or tools would work best for your company, and so on.

Get in touch with our experts to get more insights into custom AI imaplementation services and kick off work on a roadmap that includes the preliminary technical implementation, estimated budget, extension steps, and the definition of the project's success.

How much data do I need for a project that uses AI?

The amount of data available dictates how precise algorithms can be when predicting quality, offline results, or other applications. However, each AI platform is different and the ballpark number of the data sets needs differ from one project to another. Our AI experts conduct dedicated Data Quality Audits to inspect how the data is stored, what insights does the data hold, and what are specific needs and directions should be applied in order to move forward with the research and implementation.

Which programming languages are used to build AI solutions?

AI and ML are a set of algorithms and methods instead of a single monolithic service. Our applications are built on solid mathematical and statistical foundations, as well as cutting-edge methods such as deep learning, neural networks, and natural language processing. Most of our models use Python language as a significant part of the ML foundation together with statistical learning frameworks like TensorFlow, PyTorch, and Keras coupled with Kafka or Hadoop technologies for processing big data.

Our services are extended and do not only cover machine learning model preparation. We help with integration with other systems, preparing highly interactive UI applications that utilize AI model outputs, and hosting the whole AI platform. Some of the platforms that we work with are Amazon Web Services, Google Cloud Platform, and Azure to name a few.

In addition, we also handle assistance with UI/UX design in order to build complete and usable AI products which are not just a set of disconnected tools but a solid business solution.

How to monitor the progress of the AI project development?

It is important that the AI project is conducted in an iterative agile way. Each step of the development process must be delivered and tested before proceeding further.

We follow Agile project management methodology and regularly communicate the project progress to clients, concerned teams, managers, and other stakeholders. We are fully transparent and we invite client representatives to strongly cooperate with the dev team for ease of knowledge sharing and showcasing the latest versions. For app or platform development, we provide test environments for clients to ensure the progress of their projects stays on track.

How to integrate artificial intelligence solutions with internal systems?

nexocode supports our clients with end-to-end AI solutions implementation covering integrations with other components of existing systems. Our aim is to provide solid infrastructure and ML Ops optimized for specific use cases. We create easy-to-use APIs that speed up AI models integration into existing systems and integrations with other components of the AI platform.

The range of our integrations includes connecting to databases in order to get data for model training and building data pipelines from different sources for real-time data flow.

We believe in knowledge sharing

Unlocking the Potential of AI in Logistics: An Interview with Christian Merkwirth from FLYR Labs

Jarek Jarzębowski

Revolutionize Your Stock Levels with Machine Learning Inventory Management

Dorota Owczarek


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  5. SSL protocol – a special standard for transmitting data on the Internet which unlike ordinary methods of data transmission encrypts data transmission.
  6. System log – the information that the User's computer transmits to the server which may contain various data (e.g. the user’s IP number), allowing to determine the approximate location where the connection came from.
  7. IP address – individual number which is usually assigned to every computer connected to the Internet. The IP number can be permanently associated with the computer (static) or assigned to a given connection (dynamic).
  8. GDPR – Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals regarding the processing of personal data and onthe free transmission of such data, repealing Directive 95/46 / EC (General Data Protection Regulation).
  9. Personal data – information about an identified or identifiable natural person ("data subject"). An identifiable natural person is a person who can be directly or indirectly identified, in particular on the basis of identifiers such as name, identification number, location data, online identifiers or one or more specific factors determining the physical, physiological, genetic, mental, economic, cultural or social identity of a natural person.
  10. Processing – any operations performed on personal data, such as collecting, recording, storing, developing, modifying, sharing, and deleting, especially when performed in IT systems.

2. Cookies

The Website is secured by the SSL protocol, which provides secure data transmission on the Internet. The Website, in accordance with art. 173 of the Telecommunications Act of 16 July 2004 of the Republic of Poland, uses Cookies, i.e. data, in particular text files, stored on the User's end device.
Cookies are used to:

  1. improve user experience and facilitate navigation on the site;
  2. help to identify returning Users who access the website using the device on which Cookies were saved;
  3. creating statistics which help to understand how the Users use websites, which allows to improve their structure and content;
  4. adjusting the content of the Website pages to specific User’s preferences and optimizing the websites website experience to the each User's individual needs.

Cookies usually contain the name of the website from which they originate, their storage time on the end device and a unique number. On our Website, we use the following types of Cookies:

  • "Session" – cookie files stored on the User's end device until the Uses logs out, leaves the website or turns off the web browser;
  • "Persistent" – cookie files stored on the User's end device for the time specified in the Cookie file parameters or until they are deleted by the User;
  • "Performance" – cookies used specifically for gathering data on how visitors use a website to measure the performance of a website;
  • "Strictly necessary" – essential for browsing the website and using its features, such as accessing secure areas of the site;
  • "Functional" – cookies enabling remembering the settings selected by the User and personalizing the User interface;
  • "First-party" – cookies stored by the Website;
  • "Third-party" – cookies derived from a website other than the Website;
  • "Facebook cookies" – You should read Facebook cookies policy:
  • "Other Google cookies" – Refer to Google cookie policy:

3. How System Logs work on the Website

User's activity on the Website, including the User’s Personal Data, is recorded in System Logs. The information collected in the Logs is processed primarily for purposes related to the provision of services, i.e. for the purposes of:

  • analytics – to improve the quality of services provided by us as part of the Website and adapt its functionalities to the needs of the Users. The legal basis for processing in this case is the legitimate interest of Nexocode consisting in analyzing Users' activities and their preferences;
  • fraud detection, identification and countering threats to stability and correct operation of the Website.

4. Cookie mechanism on the Website

Our site uses basic cookies that facilitate the use of its resources. Cookies contain useful information and are stored on the User's computer – our server can read them when connecting to this computer again. Most web browsers allow cookies to be stored on the User's end device by default. Each User can change their Cookie settings in the web browser settings menu: Google ChromeOpen the menu (click the three-dot icon in the upper right corner), Settings > Advanced. In the "Privacy and security" section, click the Content Settings button. In the "Cookies and site date" section you can change the following Cookie settings:

  • Deleting cookies,
  • Blocking cookies by default,
  • Default permission for cookies,
  • Saving Cookies and website data by default and clearing them when the browser is closed,
  • Specifying exceptions for Cookies for specific websites or domains

Internet Explorer 6.0 and 7.0
From the browser menu (upper right corner): Tools > Internet Options > Privacy, click the Sites button. Use the slider to set the desired level, confirm the change with the OK button.

Mozilla Firefox
browser menu: Tools > Options > Privacy and security. Activate the “Custom” field. From there, you can check a relevant field to decide whether or not to accept cookies.

Open the browser’s settings menu: Go to the Advanced section > Site Settings > Cookies and site data. From there, adjust the setting: Allow sites to save and read cookie data

In the Safari drop-down menu, select Preferences and click the Security icon.From there, select the desired security level in the "Accept cookies" area.

Disabling Cookies in your browser does not deprive you of access to the resources of the Website. Web browsers, by default, allow storing Cookies on the User's end device. Website Users can freely adjust cookie settings. The web browser allows you to delete cookies. It is also possible to automatically block cookies. Detailed information on this subject is provided in the help or documentation of the specific web browser used by the User. The User can decide not to receive Cookies by changing browser settings. However, disabling Cookies necessary for authentication, security or remembering User preferences may impact user experience, or even make the Website unusable.

5. Additional information

External links may be placed on the Website enabling Users to directly reach other website. Also, while using the Website, cookies may also be placed on the User’s device from other entities, in particular from third parties such as Google, in order to enable the use the functionalities of the Website integrated with these third parties. Each of such providers sets out the rules for the use of cookies in their privacy policy, so for security reasons we recommend that you read the privacy policy document before using these pages. We reserve the right to change this privacy policy at any time by publishing an updated version on our Website. After making the change, the privacy policy will be published on the page with a new date. For more information on the conditions of providing services, in particular the rules of using the Website, contracting, as well as the conditions of accessing content and using the Website, please refer to the the Website’s Terms and Conditions.

Nexocode Team