Employee Tracker: Data Engineering for Workforce Insights

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.

Data Science

Business process automation

Retail

Data Science

Business process automation

Retail

100

manual work reduced

work experience boost

About the client

Large corporate company with over 4k employees working part-time in 10 different time zones. Due to the constantly changing daily shifts of employees, the company needs additional departments with more than 100 employees who fill out the time sheets of all employees of the company.

Tech stack

Pandas

Pyspark

SciPy

TensorFlow

Hadoop

The client's needs

Challenges & solutions

Challenge

Main task is to track and generate time lists for more than 4k company employees for subsequent control of the worked hours number, optimize/reduce costs spent on control of the employees work hours number, as well as improve the accuracy of filling in time lists, thus avoiding the factor of human error.

Solution

Developed a solution for counting employees' working hours. After arrival, employees approach the device (tablet/computer), stand in front of the camera and the system automatically determines who that person is and notes check-in time. When an employee's shift ends and he leaves, the device notes check-out times in the same way.

The system operates in two modes: manual or fully automatic. The system automatically switches to manual mode and the operator on duty initiates the employee identity manually if any problems occur.

The system works in a centralized form - each department can work autonomously without having to communicate with the head office.

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Luxury Retailer AI Demand Forecasting Implementation

Results

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.

Bernd Herzmann

CTO Retail company

The Way We Deal with Your Task and Help Achieve Results

Step 1 of 5

Web development discovery

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 help you 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 a whole pack of tech docs. In AI demand forecasting case studies, there are numerous models and approaches, so at this stage, we perform a set of interviews to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create a solution architecture, a project plan, and a list of insights or features 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 further development. Ultimately, we interpret the results. Nevertheless, demand forecasting solution modeling is a process requiring many back-and-forth iterations. We are result-focused, as it's also one of our core values.

Step 4 of 5

Solution Delivery

AI demand forecasting solutions can be a list of insights or models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect the 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! We aim to build long-term relations, providing guarantees and supporting agreements. Moreover, we are always happy to assist with further developments, and statistics show that 97% of our clients return to us with new projects.

Success stories

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

All Success Stories

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

View case study

Technically proficient and solution-oriented.

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

View case study

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

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

View case study

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

DevOps Experience

The ML startup faced high costs during its growth for a data-driven platform infrastructure that processes around 30 TB per month and stores raw data for 12 months on AWS. We reduced the monthly cost from $75,000 to $22,000 and achieved 30% performance over SLA.

2k+

QPS performance

70%