Azati SoftwareClaimed by Official Representatives

Azati developed a mobile application powered by a CNN model to assess steak marbling levels based on photos, ensuring objective quality grading and faster selection processes.

Motivation

The client, a major player in the U.S. Agricultural sector, needed a tool to standardize meat quality evaluation by grading steak marbling. Azati developed a solution to streamline this process by training a CNN model to detect subtle marbling differences and integrating it into a user-friendly mobile application, enhancing consistency and efficiency in steak grading.

Key Features

• CNN-Powered Image Analysis: Leverages Convolutional Neural Networks to evaluate steak marbling levels from photos.
• User-Friendly Mobile Interface: Provides an intuitive platform for users to capture images and receive instant quality grading.
• Model Update Flexibility: Allows seamless AI model updates without the need to rebuild the app.
• Iteration-Based Functionality Enhancement: Refined through multiple iterations based on real-world user feedback.

Project Impact

Azati’s solution delivered measurable improvements in steak grading efficiency and accuracy. The trained CNN model ensures consistent quality assessment, while the flexible app design supports long-term scalability and usability. The client can now provide reliable, objective grading standards with faster processing times, enhancing operational efficiency in meat evaluation.

Azati contributed to the development of a modern banking system for a financial institution. The solution targets small and medium-sized businesses, providing tools for creating payments across various channels, displaying account and card information, tracking financial transactions, and generating reports in multiple formats.

Motivation

To deliver a scalable and resilient banking platform tailored for SMEs. The goal was to support high-load financial operations, improve system flexibility, enhance reporting capabilities, and ensure uninterrupted service during critical financial periods.

Key Features

• Multichannel Payment Support: Creation of payment orders and requests through various channels, supporting business clients with flexible financial tools.
• Account and Card Information: Display of real-time account balances, card data, and transaction history.
• Dynamic Reporting: Generation and printing of reports in multiple formats for internal and regulatory use.
• Microservices Architecture: Refactored system to microservices, improving scalability, deployment flexibility, and fault isolation.
• High-Performance Messaging: Integrated messaging systems with Apache Kafka and MQ solutions to ensure asynchronous communication and reliable transaction processing.
• Production Monitoring and Support: 24/7 support with active incident handling and pre-release load testing to ensure stability in high-load scenarios.

Project Impact

• Successful Microservice Migration: Transitioned from monolithic to microservice architecture, increasing system scalability and deployment efficiency.
• Enhanced Stability and Uptime: Improved system resilience, reduced production incidents, and ensured uninterrupted financial services.
• High-Load Readiness: Prepared the system for peak seasons, ensuring smooth operation during high-traffic periods, such as tax payouts.
• Accelerated Development Cycle: Streamlined development and testing workflows, reducing release times and improving delivery speed.
• Massive Throughput Growth: Scaled the system from 10,000 monthly payments to over 1 million daily, showcasing robust architectural evolution.
• Technological Modernization: Migrated the system to Java 17 and replaced foreign software with domestic alternatives, aligning with import substitution initiatives.

Azati modernized an outdated warehouse management platform for one of the largest retailers, completely rebuilding it using a modern technology stack. The result was a highly reliable, scalable, and performant system capable of supporting the heavy operational loads typical of a large logistics network spanning warehouses and retail stores.

Motivation

The client required a full-scale modernization of their legacy warehouse management system, which could no longer meet current reliability and performance standards under high load. Azati’s goal was to rebuild the platform with a modern architecture focused on fault tolerance, scalability, and ease of operation to ensure the stability of warehouse processes across the retailer’s vast infrastructure.

Key Features

• Configuration Management Service: Centralized repository for managing system-wide configuration rules.
• Asynchronous Task Service: Handles background processes such as stock recalculations, order handling, and event-based notifications.
• Printing Support Service: Automates the creation and printing of warehouse documents and labels using customizable templates.
• Scalable Microservices Core: Modular components that can be deployed, scaled, and maintained independently for improved reliability and performance.

Project Impact

The project is being rolled out in phases, and the new system already demonstrates significant improvements. The platform now handles warehouse operations faster, is more resilient due to service independence, and can easily scale with evolving business needs. Technical support is simplified, enabling rapid updates and issue resolution. Plans include deeper integration with external services and extending system functionality to meet expanding business requirements.

Azati successfully developed a secure, locally hosted alternative to ChatGPT to improve corporate communication while maintaining the strict confidentiality of organizational data.

Motivation

The project aimed to create a corporate-ready ChatGPT alternative using open-source language models. The solution enhanced communication efficiency, ensured robust data security, and met modern corporate needs for information sharing and processing.

Key Features

• Independent Local Deployment: Installed and configured models on internal servers for complete control over data processing.
• Customized Corporate Integration: Seamless integration with organizational databases, tailored to specific data structures and workflows.
• Enhanced Query Handling: Optimized response generation and query management with advanced RAG techniques.
• Employee Training and Onboarding: Comprehensive training sessions to ensure effective adoption and usage of the new system.

Project Impact

Azati’s solution replaced ChatGPT with a secure, efficient, and locally hosted alternative. The deployment enhanced communication within the organization, ensured strict data confidentiality, and reduced reliance on external cloud-based solutions. Local model deployment provided flexibility and superior performance, offering a scalable and secure framework tailored to the organization’s needs.

Azati’s microservice, powered by Large Language Models (LLMs), streamlines candidate selection, reducing hiring time and enhancing match accuracy with advanced semantic search and customizable filters.

Motivation

A company in the HR industry faced costly and time-consuming recruitment processes. Azati developed an automated solution that streamlined candidate selection, reducing time-to-hire by 40% and improving placement accuracy by 25%, allowing for faster team formation and increased project success.
 

Key Features

• LLM-Powered Information Extraction: Uses Large Language Models to efficiently extract relevant data from diverse resumes.
• Semantic Search with Vector Storage: Stores resume data in vector format for fast and accurate candidate filtering based on semantic similarity.
• Customizable Relevance Filtering: Allows users to adjust the weight of criteria like skills, experience, and education for each project.
• Virtual Assessment Layer: Assesses candidate suitability and provides explanations for project fit.
• Rapid Candidate Filtering: Enables quick filtering of candidates based on specific job requirements.
• Scalable Microservice Architecture: Designed as a scalable solution to handle large datasets efficiently.

Project Impact

By implementing Azati’s AI-powered solution, our client achieved significant improvements in their recruitment process. In under 4 months, key performance metrics showed clear improvements:

40% reduction in time-to-hire: The automated candidate selection process drastically shortened hiring cycles, enabling faster team formation and quicker project starts.

Improved match accuracy through advanced semantic search and customizable filters, ensuring candidates are more precisely aligned with project needs for better outcomes.

Enhanced efficiency through automation, reducing manual effort and human errors, while automated candidate evaluation provides recruiters with transparent, data-driven insights to make better-informed decisions.

These results, delivered quickly and seamlessly, transformed our client’s hiring process, enabling them to build high-performing teams faster and more effectively, with measurable improvements in project success.

Azati’s team modernized a public app for Shopify stores, enhancing stability, performance, and customer engagement through improved architecture and customizable features.

Motivation

The primary goal was to stabilize and enhance a Shopify app by addressing architectural inefficiencies, reducing errors, and implementing a robust testing system. The project aimed to improve scalability, streamline operations, and boost sales through innovative features like customizable pop-up notifications.

Key Features

• Streamlined Application Architecture: Refactored the existing microservice-heavy architecture into a more efficient monolithic system with clear component responsibilities.
• Technology Stack Optimization: Maintained Rails API for backend operations, Vue.js for frontend administration, and AWS Lambda for high-load tracking tasks.
• Customizable Features for Engagement: Implemented a Vue.js store app for dynamic notifications, enhancing customer interaction and boosting sales.

Project Impact

The revamped application is now stable, with improved performance and error-free operation, ensuring seamless integration of new features.

Enhanced scalability allows the system to easily handle high traffic during peak events, such as Black Friday or Cyber Monday.

Azati’s dedicated QA team ensured a flawless user experience for a cutting-edge art trading platform supporting real and digital assets, including NFTs, by performing comprehensive manual and automated testing for both web and mobile applications.

Motivation

The objective was to guarantee the stability, performance, and user-friendliness of the customer’s art trading platform by thoroughly testing its intricate web and mobile applications, including features like blockchain integration, payment processing, and collaboration tools for auction houses.

Key Features

• WEB3 Integration: Tested interactions with smart contracts, blockchain transactions, and wallet integrations.
• Cross-Platform Compatibility: Validated seamless performance on multiple browsers, operating systems, and mobile devices.
• Payment System Testing: Ensured smooth integration with payment gateways, error handling for failed payments, and secure processing.
• UI/UX Validation: Checked for consistent design, responsive layouts, and optimal user interaction.
• Performance Testing: Verified platform scalability and stability under varying user loads.

Project Impact

The QA efforts resulted in a reliable and user-friendly platform for trading art and NFTs, ensuring its compatibility across diverse devices and browsers, enhancing its scalability, and establishing trust among users and auction house collaborators across 10+ countries.

Azati developed a unique AI and Computer Vision-based solution to help pharmaceutical companies generate assessment reports based on insights from medical practitioners. The solution automates the process of analyzing questionnaires and provides valuable data for enhancing pharmaceutical marketing strategies.

Motivation

The goal of the project was to create an MVP that uses AI and Machine Learning technologies to build assessment reports for pharmaceutical companies. The reports would be based on questionnaires and insights from doctors, helping the companies enhance trust in their products and improve marketing strategies. The challenge was to make the process of comparing pharmaceutical products easier and more informative for both professionals and consumers.
 

Key Features

• AI-Driven Data Analysis: The solution uses AI and NLP to process medical questionnaires, automatically identifying insights and generating reports. This allows pharmaceutical companies to better understand what doctors think about their products and how to improve marketing strategies.

• Report Generation and Sentiment Analysis: The system can analyze responses from medical professionals, correlate their insights with the pharmaceutical company’s marketing strategies, and generate detailed reports that highlight the most important feedback.
• Customizable Categorization: Azati’s solution allows for the automatic classification and grouping of data based on thematic relevance, making it easier to understand trends in doctor feedback on different aspects of pharmaceutical products.
• Data Visualization Tools: The reports are presented using advanced data visualization techniques, ensuring that stakeholders can easily interpret the results and apply the insights to improve marketing strategies.

Project Impact

Azati successfully delivered a solution that automates the process of analyzing medical questionnaires and provides pharmaceutical companies with actionable insights to improve their marketing strategies.

By using AI and NLP to evaluate feedback from medical practitioners, pharmaceutical companies can better tailor their products and campaigns, increasing trust among consumers and improving sales outcomes.