Beauty Image Automation with Gen AI & LLaVA

Enhancing Content Creation via Gen AI

Dataforest created an innovative solution to automate the work process with imagery content using Generative AI (Gen AI). The solution does all the workflow: detecting, analyzing, labeling, storing, and retrieving images using an end-to-end trained large multimodal model LLaVA. Its easy-to-use UI eliminates human involvement and review, saving significant man-hours. It also delivers results that impressively exceed the quality of human work by having a tailored labeling system for 20 attributes and reaching 96% model accuracy.

Generative AI

Marketing

Business process automation

Generative AI

Marketing

Business process automation

Model accuracy

Attributes labeled with vision LLM

Revolutionizing Image Detection Workflow with Gen AI Automation

About the client

The client is an online resource that provides women with the latest hairstyles and haircuts. Their website and various channels offer articles and video materials that inspire and advise on a wide variety of hair looks and styles. It is the go-to destination for US women seeking to update their hairstyle or maintain their current look with professional tips and trends. His work scope involved time-consuming tasks in image research and editing.

Tech stack

Llava

ChatGPT

Django

AirFlow

Qdrant

The client's needs

The client creates valuable content for his users. This process involves the challenge of collecting, analyzing, and storing large volumes of visual data.

It also requires images to be labeled, edited, and unified into one format.

When done manually, the process is time-consuming, complex, and demands quick decision-making.

Moreover, the crucial need to organize this data into a unified, easily accessible repository is a significant challenge.

Finally, he needed a simple solution to handle the automated results and make the workflow smoother for the next step.

Challenges & solutions

Challenge

Automate a time-consuming work process that delivers low-quality results. Collecting images requires massive efforts from the client's team, involving tasks such as selecting, formatting, and labeling large volumes of visual data. This process was long, disorganized, and required extensive man-hours. Moreover, it often results in low work efficiency, with the quality of the resulting picture sets falling short of desired standards.

Solution

With extensive data scraping and Data Engineering expertise, Dataforest has developed a simple and efficient Gen AI solution that automates the required workflow and delivers faster and better-quality results.

Using data scraping and an advanced image recognition model, LLaVA, the solution automates detecting, analyzing, labeling, storing, and retrieving images. Therefore, it eliminates human involvement and review, saving significant person-hours. It also delivers results that impressively exceed the quality of human work by having a tailored labeling system for 20 attributes and reaching 96% model accuracy.

Challenge

All visual materials were stored on team members' personal devices, affecting workflow efficiency.

Solution

The developed solution labels data and stores it in a unified database. It displays imagery data in a Gallery with a friendly UI, where a filter system allows users to select images based on specific criteria.

Challenge

The client had an idea to introduce a "Look-A-Like" feature, which would offer admins the opportunity to make photo selections similar to specific images.

Solution

For the "Look-a-Like" feature, Dataforest developed a solution using a vector database. The application retrieves similar images and runs additional matching based on respective labels and predefined characteristics.

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Luxury Retailer AI Demand Forecasting Implementation

Results

Dataforest developed an easy-to-use Gen AI solution for image collection that automates all work within one UI-friendly workspace, saving time & manpower and delivering better-quality results.

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The solution uses LLaVA, an advanced image recognition model which helps to label 20+ attributes. It delivers high-quality results in the image set selection processing 3250 images per 1 hour and achieving 98% model accuracy. All images are stored in a unified, automatically updated repository and can be retrieved using filters via user-friendly UI.

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By leveraging our solution, the client revolutionized his workflow, saving human hours and getting faster and higher-quality results. Moreover, this web solution can be used for the new functionalities developed to work with his data.

Revolutionizing Image Detection Workflow with Gen AI Automation

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.

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.

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

View case study

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

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