Artificial Intelligence as a Service

Get insights on how the unique mixture of artificial intelligence services and Software-as-a-Service can lower initial risks for your business and leverage its benefits to the fullest.

Artificial Intelligence Areas We Work With

Artificial Intelligence (AI) is a technology cluster that unites several spheres of application. As a well-established technology provider, we've accumulated more than enough experience to extend your capabilities. Below you see our major fields of expertise.

Machine Learning /
Deep Learning

We know well enough how to work with advanced AI-powered programs that are able to learn from experience and identify patterns and anomalies. Self-learning artificial intelligence systems can communicate with humans and other machines in real-time and are able to make intelligent decisions autonomously based on previous interactions.

Deep Learning is a subfield of Machine Learning based on data processing using neural networks. This class of algorithms helps use large volumes of Big Data for the training of AI networks.

Natural Language Processing,
Deep Q&A Systems

The main business tasks covered by NLP are chatbots, smart assistants, and Q&A systems.

Currently, we may observe inflated expectations in this developing field. The major problem here is that businesses do not really understand where chatbots and virtual assistants can be applied and expect way too much from the delivered solutions.

Speech recognition and text analysis systems have not been adequately studied and developed. Nevertheless, a lot of industries can benefit from such solutions if approached wisely.

Image Analytics

The existing image analytics algorithms are up to the mark and have been outstandingly implemented in most libraries with the use of various approaches to the technology. Many industries are becoming aware of its applications and how they can draw value from unstructured data.

All this considered, most industries value not networks or recognition algorithms as such but rather how providers of artificial intelligence services are able to quickly and efficiently create AI solutions on the basis of neural networks.

Our Artificial Intelligence Solutions

As a provider of artificial intelligence services, we've got a dedicated AI cluster that focuses its strategy on a certain subset of tasks in the sphere. We strive to deep dive into these domains only since this is the best way to add value for the customer.

Classification and Ranking Systems

Our experts can deliver AI solutions to identify classes and rank customers operating in any domain. Classification is mainly used to divide subsets of objects (customers, goods, etc.) into classes, while ranking is applied to automatically create a model that will define the order (sequence/sorting) of new data sent to this model.

Dedicated Expert in Artificial Intelligence

Our enterprise can provide you with an expert who will help you go through customer AI solutions in detail. Furthermore, with his or her help you will be able to implement the chosen solution together with the development team.

NLP Systems

The natural language processing system identifies the subject matter of the text drawing conclusions from the previous learning experience. Such artificial intelligence solutions are designed to classify texts, answer questions, make comments, and produce reviews.

Anomaly Detection Systems

To deliver our artificial intelligence services, we use the anomaly detection system to monitor data incoming from different sources and detect anomalous values. Such types of data are not expected or supposed by the system and stand out within the scope of normal/abnormal behavior.

Tell us about your vision of the artificial intelligence solution you have in mind. With our industry experts, thorough analysis of your enterprise's needs, and advanced knowledge of the market, we'll be your top choice as a software provider.


Alexey Sliborsky

Artificial Intelligence
Division Head

“Most applications of Machine Learning pertain to predictive analytics (26%) and descriptive analytics (23%). In addition, these methods can often be found in robot control systems and machine vision (14%). Discrete manufacturing normally engages artificial intelligence solutions to extend the service life of industrial equipment and enhance maintenance support.”


Classification and Ranking Tasks

These tasks belong to our key artificial intelligence insights. We are well aware of how to approach them and how industries can generate business value by applying them to their operations.

Examples of Applied Classification Tasks

of goods

customer behavior


scoring, etc.

Let's have a look at one of the examples in more detail.

Using customer data (e.g. home address, date of birth, purchase history, browser history, goods in the cart, etc.), artificial intelligence identifies products that can be interesting for the customer or suit him or her. So there is a great chance that he or she will agree to buy suggested goods. Thus learning algorithms detect clients that fit into certain patterns (behavior, characteristics, etc.).

Business Cases

Search for a suitable bank card product (debit/credit cards, limited/premium/VIP products, extended packages)

Search for potential intruders (identification of the intruder's behavior and search for similar patterns among other people)

Search for potential products that can be offered to the client on the basis of purchases made by others

Credit risk assessment system (comprehensive intelligent solution for lenders. borrowers, and investors)

Identification of Products That Suit the Customer Best

Our in-house artificial intelligence cluster has created a solution that can effectively and autonomously classify and rank the customer's products and identify covert needs for products among customers.


The major business challenge here is the customer's sales boost as well as reduction of costs for communication with buyers, ads, etc.

Many industries have established the customer base segmentation process of some kind, and they are making efforts to improve algorithms to learn which products suit which segments best. At the same time, there are often a lot of subtle correlations between the set of products and various customer profiles. AI solutions, in this case, enable you to identify these interdependencies and improve the accuracy of product classification in comparison to manual calculations.

In stark terms, we may single out such typical tasks as:

Identification of covert needs for products (e.g. a group of customers is likely to buy a certain product but for some reason, they do not use them yet). Thus you can identify those clients who would order a Gold card;

Reduction of marketing costs thanks to smarter communication with customers (e.g. you offer the product to a certain group of customers rather than to all of them).


Thanks to AI solutions, you can identify client groups that:

  • might use more prestigious cards than they currently do;
    are unlikely to renew their card;
  • may pay off the loan early;
  • are likely to get a loan for a car, a house, etc.;
  • are likely to open a deposit account.


With the help of AI services, the telecommunications industry can:

  • automatically identify the best tariff for the client;
  • identify which products/services the client would additionally purchase.


Using AI analytics, you will be able to identify clients that are likely to:

  • purchase a certain insurance policy (for a car or a house);
  • cancel their insurance policy or switch to another insurance agency.

Natural Language Processing

At Qulix, we focus on helping you gain a competitive edge through our NLP consulting and implementation services. We can easily tailor our artificial intelligence solutions to your project requirements since we constantly refine our NLP expertise and are exploring new grounds.


Works with slang,
can pick up changes
in meaning over time

Multiple answer options that can be sorted
by probability

Makes fewer mistakes than human, free
from human biases

Can be used for different languages after certain modifications


Which languages does the system support?

The system supports over 20 different languages, including the most popular world languages.

What about prediction accuracy?

Prediction accuracy depends on the quality of data used for learning. In most cases, it exceeds 75% provided that any system answer is taken into account even if the certainty probability is below 50%. However, if we include answers with a probability of over 50% only, the certainly will be up to 85% and higher.

Does the solution use cloud storage?

The solution is deployed at the client's server. We may upgrade it to migrate to the cloud.

Determining the Nature of Completed Tasks Using Reports

Our industry experts have designed a high-quality artificial intelligence solution that can determine the type of tasks performed by the employee according to the report and substitute manual processing of reports which is less accurate and more expensive than the automatic option we deliver.


The major business challenge here is to cut down the number of employees engaged in reading reports and identifying their types, which is required to make payments for the job done, as well as to reduce errors.

Before smarter algorithms were implemented, the customer had the workforce to perform monotonous operations and read reports to find out which tasks were done. Such checks included several levels since the accuracy of establishing a certain type is of the utmost importance. At the same time, the first round of checks generated a high percentage of erroneous decisions.

Artificial intelligence services reduced the number of erroneous decisions falling into the next level of checks as well as helped cut down the number of employees and transfer them to more complex tasks.

In stark terms, we may single out such typical tasks as:

identification and resolution of customer requests;

big data mining to retrieve desired information;

analysis of statements and text moods;

information retrieval from a set of resources.

Typical Business Сases

answering systems

Statement analysis
on open platforms


Anomaly Detection

This identification approach enables us to look for patterns that deviate from standard behavior. There are businesses where anomaly detection methods are rather effective in solving a wide array of issues, especially in industrial and transportation spheres.

You can find deviations from normal values which are difficult to detect manually.

You can predict engine failures,
changes in equipment operation,

You can visualize deviations from the normal line using data that seems to be in order.

Business Cases

Prediction of industrial
equipment failures

Abnormal load detection
(coupled with the IoT)

Fraud prevention
& detection systems

Identification of bottlenecks
in storehouses, on routes, etc.

Quality control for produced goods (coupled with the IoT)

Detection of abnormal

Abnormal Behavior Detection

To predict engine operation failures, our artificial intelligence experts have developed an algorithm to discover potential breakdowns using abnormal engine temperature values. For the customer's enterprise, it was significant to establish critical wear and tear or an imminent failure in advance since repairs done after the problem occurred had a notable impact on the budget.


The major business challenge here is to predict engine failure before machines take the route.

The enterprise spent loads of money to tackle engine failures during travel. The costs of dispatching reserve vehicles, towage voyages, and passenger refunds were so high that it was more effective to send vehicles for scheduled repair before it was needed according to the distance or travel time covered by them as said in the specifications.

Anomaly detection algorithms supported by predictive analytics made it possible to see patterns that potentially point out to the time when the engine shows certain dysfunctions. The task was complicated by the fact that the train engine operates in various modes during acceleration, slowdown, warm-up, etc.

In stark terms, we may single out such typical tasks as:

engine failure prediction

fraudulent behavior detection

abnormal load detection

abnormal demand detection in retail

prediction of conveyor band malfunctions

search for bottlenecks in storehouses

How to Start

For our artificial intelligence services, we use the Cross Industry Standard Process for Data Mining
(CRISP-DM) which is popular among data scientists.

The implementation of AI solutions is divided into several stages.

1.Business Understanding

Business analysts work with the customer to learn every detail of the future solution. Business analysts work with the customer to learn every detail of the future solution. Here you should have a critical look at the problems to be solved. Quite often, artificial intelligence can resolve issues that are difficult to comprehend when approached in a conventional way. We strongly advise the engagement of third-party analysts to audit both the tasks under consideration and the entire project.

2. Data Understanding

Here we gather and analyze data and scan the existing and potential data sources by checking the quality of the whole data set and recording it. This stage usually takes up to 3–4 weeks. Since data is of critical value for the entire predictive analytics process, the most common problems in AI-powered projects are connected with the abundance of "garbage" in data and the resulting small volume of relevant information.

3. Data Preparation

At this stage, we deal with the isolation of features and data alignment, cleaning, and transformation. Since the data significance can be reconsidered when you receive preliminary results of the model's operation, we may get back to this stage from later stages. This stage may require 1 to 3 weeks. The main risks here are significant losses in the data set after its cleaning and transformation. It is a common practice to roll back to the previous stage to gather additional information.

4. Modeling

This time we select 3–4 models suitable for the task and feed the data into them. The work with each model usually takes one week. Among other things, it includes tuning of parameters. You may easily make a mistake at the stage of data preparation or when you select features, so stage 3 is closely connected to stage 4 at the early stages of the project.

5. Evaluation

The models at hand and obtained assessments enable us to perform an analysis of how your business goals can be achieved as well as those goals that were not established at the very start. Furthermore, we go over the next steps: it can be either deployment or rollback to the business understanding stage. Normally, it requires 1–2 weeks.

6. Deployment

Depending on the expected result, the final stage is responsible for the end solution. It can take the form of a data findings report or a SaaS solution. The duration of this stage is rather hard to predict since the end result is in most cases very specific.

On average, the project schedule can be presented as below.

Business Understanding

2+ weeks

Main Results

Business goals identified

Data analysis goals identified

Project plan designed



3–4 weeks

Main Results

Source data collected

Source data described

Data quality checked


It often happens so that this stage is partially implemented by customers when they come to the conclusion that to solve the problem they need to resort to artificial intelligence services.


1–3 weeks

Main Results

Data selected

Data cleaned

Missing data generated

Data formatted to work with models


1 week per model

Main Results

Modeling methods selected

Tests executed

Models designed and assessed


1–2 weeks

Main Results

Results evaluated

Next steps determined


As a result, the process can be restarted at this stage.


Strong dependence
on the selected solution

Main Results

Solution implemented in the expected

Data for the entire project collected

Success measurement report and
comments provided

However, the project implementation schedule will be in any way tailored to your needs.

Analytical System for Preventing Engine
Breakdown Using Machine Learning

Our AI professionals created a smart system for the early detection of engine failures based on sensory data. Since train engine repairs can be quite costly, the system had to detect abnormal values and send a signal to maintenance staff to prevent sudden breakdowns. We worked with machine learning algorithms, tested several approaches to teaching neural networks, and finally selected the approach which worked best for data sets generated by train engines.

What Are the Costs?

In general, the project cost depends on the factors below.

Business goals

Quality and quantity of data

Accuracy level of the model

To calculate the project budget, we recommend using the T&M model since in this way we will be able to minimize the potential overhead in the form of costs of potential risks, the need to adjust requirements, etc.


Thank you, !

Thank you for contacting us!
We'll be in touch shortly.

Go back to the home page

Feel free to get in touch with us! Use this contact form for an ASAP response.

Call us at +44 151 528 8015
E-mail us at

Thank you!

Thank you for contacting us!
We'll be in touch shortly.

Go back to the home page

Feel free to get in touch with us! Use this contact form for an ASAP response.

Call us at +44 151 528 8015
E-mail us at