Emotion Tracker: Insights Through Data Precision

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.

Data Science

Web and mobile development

Data Engineering

Data Science

Web and mobile development

Data Engineering

CX improvement

cost reduction

About the client

State-owned banking and finance company headquartered in Italy, with activity in several European countries and more than $40 bln in revenue.

Tech stack

Flask

Python

PostqreSQL

MySQL

AWS

The client's needs

Challenges & solutions

Challenge

It is needed to check the quality of customer satisfaction from the work of each specific bank manager and create a system for tracking customer emotions.

Solution

Created a computer vision-based solution by means of cameras in front of each manager facing customers’ faces, tracking their emotions (positive, negative, neutral, etc.).

Analysis of customer emotions enables the administration to track customer satisfaction, as well as the work of each operator individually, thus reducing the company’s expenses, thereby optimizing the work of departments.

System is also capable of analyzing conversation flow, a useful feature when analyzing the quality of the operators' work.

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Luxury Retailer AI Demand Forecasting Implementation

Results

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

Alex Rasowsky

CTO Banking 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

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.

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

View case study

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.

AI Web Platform for Data-Driven E-commerce Decisions

Dropship.io is a powerful data intelligence platform that helps e-commerce businesses identify profitable products, analyze market trends, and optimize sales strategies. Using large-scale data scraping, AI-driven insights, data enrichment solutions, integrations with Shopify, Meta, and Stripe, it enables smarter product decisions and drives revenue growth.

3M+

total unique users

600M+

products monitored

Josef Ganim

Founder & CTO Dropship.io

View case study

AI-Powered E-commerce Platform: Data-Driven Case

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%