Data science services

to dive into business insights

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.

Check our service rates

Dealing with any of these concerns?

Data science includes an extensive range of services, and it solves the following problems.

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.

Unclear requests

You need forecasting for making decisions, but there is no understanding of how exactly in your case to correctly build a forecast - what data to use and what methods to implement it.

No 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.

What we offer as part of 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.

Data mining

The construction of effective data warehouses and the organization of data collection within the business as comfortable as possible for all its participants.

Data analysis

We carefully study the data sets of your business to provide you the best selection of methods that are optimal for solving your problems. 

Visualization

The creation of data visualizations that answer some questions and make it possible to see the processes that are usually not noticeable and not obvious.

BI services

We provide the analysis of user behavior, automation of making complex decisions instead of a person or additionally as a preliminary opinion.

Forecasting

Making forecasts and using them in the analysis and decision-making in advance, including risk reduction, resource redistribution, threat prevention, etc.

Deep learning

We set specific processes to create the construction of data models, including neural networks, and their subsequent training to create artificial intelligence systems.

AI services

Our team makes a complex construction of self-learning AI systems that can make decisions and automate processes that previously only a human could do.

Computer vision

We do the development of computer vision systems based on the recognition of patterns in images,  streaming video or sound, followed by decision making.

What is the benefit 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;

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

Set of technologies for data science

We always select the technologies that are optimal for the task in order to minimize the cost and speed up the implementation and get the benefits for the business.

  • Python
  • R and R Studio
  • Google Data Studio
  • BigQuery
  • Tableau
  • Pandas
  • Sklearn
  • TensorFlow
  • TensorRT (GPU)
  • Keras
  • DLib
  • Matlab/Octave, Maple
  • Matematica
  • QlikView
  • OpenCV

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 behaviour 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 respecting 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.

 
 
 

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