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.
How to move e-banking into the microservices architecture?
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How to move e-banking into the microservices architecture?
Get the latest insights on the best way to transform your banking experience.
How to move e-banking into the microservices architecture?
Get the latest insights on the best way to transform your banking experience.
How to move e-banking into the microservices architecture?
Get the latest insights on the best way to transform your banking experience.
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.
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.
Technologies
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
Classification
of goods
Predicting
customer behavior
Spam
detection
Credit
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.
Challenge
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).
Banks
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.
Telecom
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.
Insurance
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.
Benefits
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
FAQ
The system supports over 20 different languages, including the most popular world languages.
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.
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.
Challenge
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
Question
answering systems
Statement analysis
on open platforms
Report
analysis
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,
etc.
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
demand
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.
Challenge
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
Data
Understanding
3–4 weeks
Main Results
Source data collected
Source data described
Data quality checked
Comments
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.
Data
Preparation
1–3 weeks
Main Results
Data selected
Data cleaned
Missing data generated
Data formatted to work with models
Modeling
1 week per model
Main Results
Modeling methods selected
Tests executed
Models designed and assessed
Evaluation
1–2 weeks
Main Results
Results evaluated
Next steps determined
Comments
As a result, the process can be restarted at this stage.
Deployment
Strong dependence
on the selected solution
Main Results
Solution implemented in the expected
form
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.
Contacts
Feel free to get in touch with us! Use this contact form for an ASAP response.
Call us at +44 781 135 1374
E-mail us at request@qulix.com
Feel free to get in touch with us! Use this contact form for an ASAP response.
Call us at +44 781 135 1374
E-mail us at request@qulix.com
