Data Science Solutions

Success stories

Check out a few case studies that show why DATAFOREST will meet your business needs.

Emotion Tracker

For a banking institute, we implemented an advanced AI-driven system using machine learning and facial recognition to track customer emotions during interactions with bank managers. Cameras analyze real-time emotions (positive, negative, neutral) and conversation flow, providing insights into customer satisfaction and employee performance. This enables the Client to optimize operations, reduce inefficiencies, and cut costs while improving service quality.

15%

CX improvement

cost reduction

Alex Rasowsky

CTO Banking company

They delivered a successful AI model that integrated well into the overall solution and exceeded expectations for accuracy.

Sales automation

Client Identification

The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.

customer retention boost

25%

profit growth

Christopher Loss

CEO Dayrize Co, Restaurant chain

The team has met all requirements. DATAFOREST produces high-quality deliverables on time and at excellent value.

E-commerce

Sales automation

Entity Recognition

The online marketplace for cars wanted to improve search for users by adding full-text and voice search, as well as advanced search with specific options. We built a system application using Machine Learning and NLP methods to process text queries, and the Google Cloud Speech API to process audio queries. This helped greatly improve the user experience by providing a more intuitive and efficient search option for them.

faster service

15%

CX boost

Brian Bowman

President Carsoup, automotive online marketplace

Technically proficient and solution-oriented.

Business process automation

Retail

Employee Tracker

The large Retail company was facing a significant challenge in managing and tracking our employees' working hours and needed a solution that would automate the process and ensure accuracy. We developed a system for counting employees' working hours. Employees simply approach the device upon arrival and the system automatically identifies them and records their check-in time.

100h+

manual work reduced

13%

work experience boost

Bernd Herzmann

CTO Retail company

DATAFOREST has an excellent workflow and provide constant and close communication. The team brings in a range of technical talent to address issues as they arise.

DevOps

Cyber Security

AWS Cost Reduction

This project optimized the cloud infrastructure of a U.S. IT services company to reduce costs and improve performance. Our investigation identified several areas for optimization, including unused computing resources, inconsistent storage, and a lack of savings plans. We helped to optimize resources, implemented better policies for storage, and improved internal traffic flow through architecture redesigns and dockerization.

23k+

monthly savings

performance optimization

Harris N.

CTO IT Services & Consulting

The team's deep understanding of our needs allowed us to achieve a more secure, robust, and faster infrastructure that can handle growth without incurring exorbitant costs.

Insurance

Insurance Profitability Analysis Tool

This project involved developing a tailor-made data analysis tool for a U.S. insurance provider who were facing challenges analyzing a significant volume of data. The Client needed a professional and customized solution which would enable effective analysis of their data and provide actionable insights to improve their business operations. Our solution delivers real-time processing of data, flexible filtering capabilities through dashboards, and also supports dashboards detailing the evaluation of insurance loss or profit by industry vertical. Additionally, a predictive model for profitable insurance cases was built using historical data, and a reporting system was created to show significant factors and profitability based on different metrics.

> 10TB

data processed

89%

accuracy improvement

Sean B.

CEO Insurance provider

Insurance Profitability Analysis Tool case preview image

Great work! The team provided an excellent solution for consolidating our data from multiple sources and creating valuable insights for our business.

Healthcare

Stock relocation solution

The client was faced with the challenge of creating an optimal assortment list for more than 2,000 drugstores located in 30 different regions. They turned to us for a solution. We used a mathematical model and AI algorithms that considered location, housing density and proximity to key locations to determine an optimal assortment list for each store. By integrating with POS terminals, we were able to improve sales and help the client to streamline its product offerings.

10%

productivity boost

increase in sales

Mark S.

Partner Pharmacy network

The team reliably achieves what they promise and does so at a competitive price. Another impressive trait is their ability to prioritize features more critical to the core solution.

Web Applications

Data Engineering

Financial Intermediation Platform

The project aims to develop a deal origination platform for private equity investments in infrastructure-related sectors and involves building a secure, interactive B2B platform from scratch, with sign-up functionality to connect investment firms to proprietary investment opportunities. DATAFOREST built a highly-loaded platform and applied AI functionality to empower the application's development.

98%

model accuracy

100%

timely development

Enrico Cattabiani

Founder & CEO IDN, Infrastructure Deals Network

Financial Intermediation Platform preview

They understood our requirements, translated into actions rapidly, and adapted to requests easily.

Retail

Sales automation

Store heatmap

The electronics retailer, wanted to improve their sales and customer service by analyzing the flow of people into their stores. We created a system using Machine Learning, image detection, and face recognition. The system tracks visitors' movements and the most viewed shelves and products. This information helps the store to focus on selling popular products and to avoid unpopular ones, ultimately improving the sales process.

increase in sales

100%

dead zones removed

Jared D.

CEO Consumer Electronics Retail

DATAFOREST provides meaningful shopper-behavior Insights. They are very responsive and effective, trying to engineer and offer the best fit solution.

Retail

Demand forecasting

We built a sales forecasting system and optimized the volume of goods in the warehouse and the range of goods in different locations, considering each outlet's specifics. We set up a system that has processed more than 8 TB of sales data. These have helped the retail business increase revenue, improve logistics planning, and achieve other business goals.

88%

forecasting accuracy

0.9%

out-of-stock reduced

Andrew M.

CEO Luxury Goods Retail

How do we help companies?

I think what is really special about the DATAFOREST service is its flexibility, openness, and level of quality and expertise.

All Success Stories

5 Steps From Data Science Service to Business Insights

Step 1 of 5

Free consultation

It's a good time to get info about each other, share values and discuss your project in detail. We will advise you on a solution and try to help to understand if we are a perfect match for you.

Step 2 of 5

Discovering and feasibility analysis

One of our core values is flexibility, hence we work with either one page high level requirements or with a full pack of tech docs.

In Data Science, there are numerous models and approaches, so at this stage we perform a set of interviews in order to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create solution architecture, a project plan, and a list of insights or features that we have to achieve.

Step 3 of 5

Solution development

The work starts with data gathering, data cleaning and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for the further development. Ultimately, we interpret the results. Nevertheless, data modeling is about a process that requires lots of back and forth iterations. We are result focused, as it’s one of our core values as well.

Step 4 of 5

Solution delivery

Data Science solutions can be a list of insights or a variety of different models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect client’s participation in the project. While modeling, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high-level of communication is also our core value.

Step 5 of 5

Support and continuous improvement

We understand how crucial the solutions that we code for our clients are! Our goal is to build long-term relations, so we provide guarantees and support agreements. What is more, we are always happy to assist with further developments and statistics show that for us, 97% of our clients return to us with new projects.

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E-commerce

DATAFOREST improves client experience through customer sentiment analysis, integration for omnichannel communication, and personalization based on behavior. The introduction of back-office automation gives data-driven marketing, system integration, and custom dashboards. Recommendation systems, demand forecasting, and dynamic price management stimulate sales.

Retail

Big Data and data science enables the analysis of vast supply chain data, including historical sales, production, and transportation data, applied to market trends and economic indicators to forecast future demand. Using statistical models and optimization algorithms, data scientists determine optimal inventory levels, reorder points, safety stock levels, and replenishment strategies. Insights guide retail businesses in making data-driven decisions about product development, marketing strategies, customer experiences, and personalization initiatives.

Finance

The main challenges in this industry are being overcome with the help of data engineering, business automation, and DevOps software production philosophy. With the help of artificial intelligence (AI) and data science technology, companies develop custom software to optimize internal operations, eliminate manual and time-consuming tasks, and improve overall productivity. Routine automation requires rule-based tasks and processes using robotic process automation, the combining of which reduces labor costs uses custom software development with specialized functionality, and decreases human error, which can result in costly mistakes.

Insurance

The collaboration of data scientists and DevOps facilitates the continuous delivery of data-driven solutions and utilizes predictive models, machine learning, and data science tools to extract insights from insurance data. Effective data integration empowers new data product development, including predictive analytics tools, fraud detection systems, and customer self-service portals.

Cyber Security

Data management is critical for identifying and responding to threats effectively in the cyber security industry. Data scientists work closely with DevOps teams to create data pipelines that collect and process security-related data from various sources to access and analyze relevant data efficiently, leading to the development of advanced warning detection models, anomaly detection systems, and security analytics tools.

Pharma

Data science analyzes historical trial data, identifies relevant patient populations, integrates patient records to develop predictive models, and mines vast amounts of biomedical literature, clinical data, and molecular databases. Machine learning is also widely used in this industry. Data management practices permit data scientists to leverage reliable and relevant data for their analyses, supporting drug discovery, clinical trials, personalized medicine, adverse event analysis, and pharmacovigilance.

Advertising and Marketing

Influencer data scraping calls for extracting and analyzing data from social media platforms to identify influencers with significant reach, engagement, and impact on their followers. Data scientists build predictive models to forecast customer churn, enabling marketers to take proactive retention measures. These models identify the key drivers that impact customer retention: product preferences, pricing sensitivity, or customer service satisfaction. Data science techniques such as clustering, segmentation, and predictive modeling analyze and classify customers.

Real Estate

Data science techniques help real estate accurately value properties and determine optimal pricing. Data scientists provide insights into emerging real estate markets, investment opportunities, and potential risks. Machine learning algorithms suggest personalized property recommendations to potential buyers or renters.

Data Science will increase work efficiency. Get expert support!

What methods does DATAFOREST use?

The methods of mathematical and statistical analysis, as well as knowledge of probability theory, can be conditionally divided as follows:

Statistics methods

Linear\Logistic regression

Least squares

Agglomerative clustering

Pearson correlation

Chi-squared test

Non-NN machine learning

Support Vector Machines

Nearest Neighbors

Naive Bayes

Decision Trees

Random Forests

Gradient Boosting

LightGBM

XGBoost

K means/prototypes/modes

DBSCAN

Isolation forest

Local outlier factor

Neural networks, including deep learning

Self organizing map

Multilayer perceptron

Autoencoders

Generative adversarial network

What Data Science technologies do we use?

The tool for implementing the methods in the data science service is the program code, which can be divided into levels of integration as follows:

Pandas

SciPy

TensorFlow

Numpy

ADTK

DBscan

G. AutoML

Keras

MLFlow

Natural L. AI

NLTK

OpenCV

Pillow

PyOD

PyTorch

FB Prophet

SageMaker

Scikit-learn

SpaCy

XGBoost

YOLO

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