Data Science
Services

Ardas uses data to extract valuable market insights for growing and scaling your business. Our data science services include consulting, development, and support to adapt companies to run experiments on their data searching.

stripo
flinqer
cx
infinox
softcube
volobus

Problems We Solve

Not enough Features

If you have a Saas product, and you need information about what is happening inside your business to improve and optimize the product, but there is no visualization to answer your questions. Although there is data, that does not say anything.

Lack of Data

You understand that there is not enough data in your business to make any decisions, and you do not know what kind of data is needed and how best to collect it. There are no specific tactics, so it is not clear what exactly to change and implement.

Limitations of Tools

Much manual work is needed to make any decisions. Automation might be a solution, but this cannot be done using conventional algorithms because a higher level of intelligence is required as well as a certain set of tools and skilled experts to use them.

Unreadable Data

Data visualization in your Saas product or business is not informative and human-readable enough. Users or clients are not getting the information they need or don't understand it.

Not Automated Vision

You need a system of recognition or classification of information to automate business processes. Challenges in this category are related to the tools that are used to extract insights.

 

Our Data Science Services

Each problem requires completely different approaches, so this is what we do to solve your problems. Let's decide what exactly you need

The Process of Data Science Services

We use our expertise to build models that collect data and produce actionable insights for your business to improve operational intelligence and product quality.

Data science consulting

We study data, ask the big questions, and establish any goals for the project as well as help you understand what opportunities exist and the pitfalls of adding DS methods. 

Data analysis and preparation 

Our team combines classic Agile principles with the CRISP-DM model for data mining and analysis. We will make the data for all of the additional steps ready for implementation. 

Modeling, balancing,  and  training

We will run several experiments to achieve a balance between accuracy and computer resource usage. We will aim to get tangible results in the shortest period to validate your ideas.

Evaluation and adjustment

Our team will adjust and optimize the selected model to improve the overall accuracy and lower the amount of power and time that it takes. 

Integration and deployment

We conduct a deployment on a test server. You will get a fully validated model that can be used to create software, complete with AI features.

Working on improvements 

We plan future improvements of the implemented DS technologies, taking into account the budget, the desired timing and technical capabilities.

Team Engagement Models for SaaS

Part-time
team

  • Your project is assigned to certain people
  • Little workload - no need to hire full time people
  • No monthly payments - pay for only worked hours

Dedicated
Team

  • Work only on your tasks - never switch to other projects
  • Enough work to keep employees busy
  • No recruiting and hiring expenses - easy onboarding

Project
Outsourcing

  • You have no IT resources - we do all the technical job
  • No need to set up anything - we are fully ready to start
  • We can start with any materials you provide
 

Successful Cases of Our Data Science Services

We can create a machine learning model, train complex deep neural networks, or apply a computer vision algorithm in order to achieve a well-defined business goal.

Recommending the Best Email Sending Frequency

Language: R / R Studio, xgboost.

Methods: Random Forests, Logistic Regression.

Analysing client behavior in SaaS platform, study what they read and how often. Picking up the most optimal amount of emails and sending frequency for each client based on what he can comfortably read. 

Results: In 2 months the percentage of opened emails and link clicking conversion was increased by 15%.

Studying the Best Personal Sending Time

Language: R, R Studio.

Methods: Probability Theory, Statistics.

Finding the best and optimal time to send emails for each user. We study when user’s activity is at maximum, considering many factors like link clicking, buying products/services, etc. Based on this information, we picked the best time for sending emails.

Results: Email opening and clicking conversion was increased by 20%.

Identifying Potential VIP Clients

Language: R, TensorFlow, Keras.

Methods: Neural Networks

To identify potential VIP clients first 2 weeks of their behavior are analyzed and prognosis for 90 days is made. We analyze when users logs in, what purchases he makes, his likes and dislikes.

Results: 90% of VIP clients are predicted correctly. Amount of VIP clients increased twice. Found the best processing time (3 minutes). Each one extra minute reduces conversion by 2%. Reduced time of VIP client processing twice. Finally, 2% of VIP clients bring 50% of the income.

Detecting, Recognizing and Searching People by Faces in Real Time

Language: Python, TensorFlow, Keras, DLib, OpenCV.

Methods: Neural Networks, Deep Learning.

Detecting and recognizing people's faces from video cameras in shops in real-time. Then building unique face landmarks and searching for the closest face in the database.

Results: Face search has 80% accuracy.

 
 
 

Clients Say About Us

 

Dmitry Kulaksyz

CPO, Stripo

‘’Great job’’

We started in 2016 with an idea and built a very detailed MVP plan mostly thinking about how to compete in a very busy market. We investigated all disadvantages of existing builders and designed a WYSIWYG builder that saves 50% more time than others. Later in 2019 we supported AMP language by Google for dynamic emails and became one of a few builders with the best AMP support.

Project Dates   2017 - Present

Project Summary

This startup became №1 in the world used by Amazon, McDonald's, Oracle, CocaCola, Airbnb, Uber, HP, and Cisco.

Read Case

Alex Mladenovich

Product Manager, UK

‘’Removed Problems From Us’’

Ardas was tasked with the re-development of our mature SaaS application. They initially helped us with our new hosting architecture before working with us to re-design and develop all elements of the application.

Project Dates   2007 - Present

Project Summary

Today we are proud to be a part of the highly technical and very successful SaaS solution. Building long-lasting relations is never easy, we have been accurate with all the details through the years.

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Michael O'Sullivan

CTO, UK

‘’Ardas is very flexible’’

I have been working with Andrew and Ardas's team as CTO of INFINOX Global as a principal financial services client. Ardas's passion really stands out. I frequently connect the team to discuss strategic decisions and plans for the future.

Project Dates  2014 - Present

Project Summary

After successfully transforming this solution into an automated system we entered long-term support and evolution cycle and still update and tune this system according to customer requirements.

Read Case
 
 

Benefits of Our Data Science Services for Your Business

Here are the most typical examples of how and where data science can be beneficial:

  • Producing Intelligent Real-Time insights in fintech and trading;
  • Defining VIP customers;
  • Improving the customer experience in any field;
  • Switching from manual work to automation;
  • Personalizing solutions in marketing and sales;
  • Improving usage of IT infrastructure;
  • Enhancing routes in logistics;
  • Balancing resource utilization in any sector;
  • Supporting decision makers in business intelligence;
  • Scaling workforce cooperation for better management;
  • Enhancing the efficiency of sources in the energy sector;
  • Forecasting market events;

FAQ About Data Science Services

We provide a wide range of AI solutions starting from demand prediction for logistics to in-store customer behavior analysis ecommerce application.

How do you provide data science services? Are you providing a data science expert or is it a group of people?

It all depends on the project, but rarely only one specialist works on a project. For data mining and storage organization, an architect, data engineers, and backend developers are provided for the project. An expert does data analysis and visualization in analyzing and working with data in business because he objectively understands the needs of the business in terms of obtaining information from data. The development of ML, AI is carried out by completely different data science specialists. At the same time, for Computer vision tasks, they are more likely consultants on openCV, and the tasks are implemented by those developers who work in the project and know the required programming language (java, PHP, Python, C #). Data science is difficult and, as a rule, we always select a team of people with the right skills and knowledge of the right technologies.

Which of all data science technologies makes sense for a Saas product?

First of all, this is monitoring the state of the business, i.e. Finance, users and metrics calculations that give a picture of how the business is going. Then it is an analysis of people's behavior and increasing customer service, the quality of the interface, reducing the churn rate, etc. It is super important to optimize pricing packages - analyzing the demand for functionality and balancing price offers so that you earn more. Further, this is forecasting - on the basis of the data obtained, you can build a forecast and use it to correct decision-making. At the very end is the development of AI decision-makers, if necessary.

How much does it cost to develop artificial intelligence systems?

The price starts from 1 month of work and can be anything. It all depends on what level of intelligence is needed. AI is never perfect. It always has an accuracy that is measured as a percentage. As a rule, first, PoC is done for which limited requirements are set, and then 4-8 weeks should be enough, then they determine what accuracy is needed and how much data is required to achieve this accuracy. The price depends not so much on building the AI ​​model as on training it with data and organizing it.

Industries we work with can benefit from data science

Delivering value is not only about technologies, but efficient processes. We work on both. Let's have a call and discuss how your business can be improved with machine learning methods.

 
Andrew
Ryzhokhin
Chief Executive Officer