Your data, our passion

We built an AI-powered study assistant for primary school students, delivered as a mobile application with a dedicated AI backend designed through extensive R&D. The system uses vectorized textbooks to enable accurate chat- and image-based interactions, allowing students to ask questions and receive context-aware explanations directly from learning materials. Built with an end-to-end call chain architecture and LLMOps practices, the backend was successfully integrated into the mobile app, enabling a scalable and intelligent learning experience grounded in curriculum content.

A scalable AI-powered document processing solution designed to handle high volumes of diverse business documents—without relying on rigid templates. The system intelligently interprets various formats, including scanned files, and automatically extracts key data tailored to client requirements. Trained on real client datasets, it delivers up to 98% accuracy, significantly improving efficiency and reducing manual workload.

In this project, We addressed the inefficiencies of manually reconciling orders, delivery notes, and invoices by developing a generative AI–driven solution. We integrated this system with existing workflows and ERP platforms to automatically analyze document content, standardize key fields, and flag discrepancies before users even open the files. By automating data population and highlighting inconsistencies, we streamlined previously manual tasks such as form completion and approvals. As a result, we reduced processing time by 30% and significantly minimized human error.

We addressed the challenges around inconsistent and incomplete workplace accident reports in the corporate HSE system by developing an AI-supported solution. Our approach helps standardize report quality by guiding authors with immediate feedback, reducing the need for costly training and easing the burden on limited HSE specialists. At the same time, we enabled faster processing and summarization of reports at headquarters by automating information extraction and analysis. As a result, report quality improved significantly, and employees gained a clearer understanding of reporting expectations while overall processing became more efficient.

A scalable AI-powered document processing solution designed to handle high volumes of diverse business documents—without relying on rigid templates. The system intelligently interprets various formats, including scanned files, and automatically extracts key data tailored to client requirements. Trained on real client datasets, it delivers up to 98% accuracy, significantly improving efficiency and reducing manual workload.

In this project, We addressed the inefficiencies of manually reconciling orders, delivery notes, and invoices by developing a generative AI–driven solution. We integrated this system with existing workflows and ERP platforms to automatically analyze document content, standardize key fields, and flag discrepancies before users even open the files. By automating data population and highlighting inconsistencies, we streamlined previously manual tasks such as form completion and approvals. As a result, we reduced processing time by 30% and significantly minimized human error.

We addressed the challenges around inconsistent and incomplete workplace accident reports in the corporate HSE system by developing an AI-supported solution. Our approach helps standardize report quality by guiding authors with immediate feedback, reducing the need for costly training and easing the burden on limited HSE specialists. At the same time, we enabled faster processing and summarization of reports at headquarters by automating information extraction and analysis. As a result, report quality improved significantly, and employees gained a clearer understanding of reporting expectations while overall processing became more efficient.

We addressed the inefficiencies of manually reviewing and filtering resumes by developing a generative AI–based solution tailored for HR workflows. Our system analyzes resumes in various formats through an interactive chat interface, allowing users to iteratively refine searches and apply complex filtering conditions without reopening documents. We also generate an ideal candidate profile based on inputs such as company culture and automatically evaluate each resume against this benchmark. As a result, a process that previously required 3–4 hours of manual effort can now be completed in just a few minutes, significantly improving speed and decision quality.

We solved a problem of managing a high volume of simultaneous tender opportunities by developing an AI-driven solution that automates monitoring and evaluation. Our system continuously collects tender announcements from online sources and allows users to configure a detailed company profile. Based on this profile, we analyze and rank opportunities according to relevance, ensuring alignment with the client’s capabilities and strategic goals. We also implemented automated notifications to highlight the most promising tenders. As a result, the client receives a personalized, prioritized list of opportunities, significantly reducing the time and effort required for manual screening and selection.

To reduce the constant back-and-forth around routine questions, we built an AI-powered knowledge assistant directly into the company’s ERP system. Instead of relying on managers to provide answers, employees can interact with a chatbot that draws on a vectorized internal knowledge base to deliver precise, context-aware responses, supplementing them with external sources when needed. The solution was designed with modularity in mind, making it easy to extend with additional capabilities such as document processing or content generation. The impact is still being measured, but the system is expected to significantly improve knowledge accessibility and free up managerial time for higher-value work.

Handling a high volume of customer inquiries with limited specialist capacity required a smarter, more scalable approach. We implemented an AI-powered assistant that can instantly process and extract relevant information from extensive internal documentation, even across hundreds of pages. The system generates tailored response drafts for emails and chats based on the company’s knowledge base, and also supports voice-based queries for real-time assistance during calls.

Selecting the right service in banking and public administration settings often requires staff assistance, so we built an AI solution that interprets spoken customer requests and automatically maps them to the most relevant options in a structured service tree, asking clarifying questions when needed to improve accuracy. This reduces service selection time and significantly decreases the need for human intervention, improving efficiency and lowering operational costs.

Rising customer churn at a large international company required a more proactive and data-driven retention approach, so we built a time-series owner database and developed a Cox time-varying churn risk model that continuously updates monthly risk scores for each customer. These scores are segmented into risk groups (low, medium, high), validated with retention curves, and enriched with unsupervised clustering to identify distinct customer personas, alongside a designed retention workflow prepared for future uplift modeling. This enabled early, targeted interventions that reduced churn, improved customer satisfaction, and optimized the use of retention resources.

Managing staffing and operations across dozens of seasonal holiday parks required moving beyond weekly, experience-based planning, so we developed a forecasting and optimization system that predicts footfall, revenue, and staffing demand at venue level with hourly granularity. Built on an automated MLOps pipeline (Snowflake-based), the solution enables reliable 1–4 week forecasts and supports procurement and inventory planning that was previously not feasible. This shift led to better alignment of staffing with real demand, reducing both understaffing and overstaffing while lowering labor costs and improving the ability to anticipate peak traffic periods.

We built a dynamic pallet planning system to replace complex manual decision-making in warehouse loading, where item sequencing and placement rules previously depended heavily on experience and were prone to costly errors. Using item attributes, customer-specific constraints, and optimization algorithms, the system determines optimal loading sequences and precise pallet layouts, while also generating multiple alternatives based on different optimization goals. Warehouse staff receive clear task instructions supported by 2D/3D visualizations, enabling faster execution, improved space utilization, and a significant reduction in handling errors and product damage.

We developed a dynamic pricing framework for a car-sharing company to replace manual, static price adjustments with an automated system capable of reacting to real-time internal and external factors. The solution processes large volumes of data near real time, combining vehicle telemetry with environmental and contextual signals, and is built as a flexible no-code, rule-based engine that allows business users to define and modify pricing logic, discounts, and what-if scenarios with ease. This enabled more responsive and data-driven pricing decisions, resulting in a 5%+ increase in service usage within six months and a significant reduction in the effort required to maintain and operate the pricing system.

We built an AI-powered study assistant for primary school students, delivered as a mobile application with a dedicated AI backend designed through extensive R&D. The system uses vectorized textbooks to enable accurate chat- and image-based interactions, allowing students to ask questions and receive context-aware explanations directly from learning materials. Built with an end-to-end call chain architecture and LLMOps practices, the backend was successfully integrated into the mobile app, enabling a scalable and intelligent learning experience grounded in curriculum content.

A scalable AI-powered document processing solution designed to handle high volumes of diverse business documents—without relying on rigid templates. The system intelligently interprets various formats, including scanned files, and automatically extracts key data tailored to client requirements. Trained on real client datasets, it delivers up to 98% accuracy, significantly improving efficiency and reducing manual workload.

In this project, We addressed the inefficiencies of manually reconciling orders, delivery notes, and invoices by developing a generative AI–driven solution. We integrated this system with existing workflows and ERP platforms to automatically analyze document content, standardize key fields, and flag discrepancies before users even open the files. By automating data population and highlighting inconsistencies, we streamlined previously manual tasks such as form completion and approvals. As a result, we reduced processing time by 30% and significantly minimized human error.

We addressed the challenges around inconsistent and incomplete workplace accident reports in the corporate HSE system by developing an AI-supported solution. Our approach helps standardize report quality by guiding authors with immediate feedback, reducing the need for costly training and easing the burden on limited HSE specialists. At the same time, we enabled faster processing and summarization of reports at headquarters by automating information extraction and analysis. As a result, report quality improved significantly, and employees gained a clearer understanding of reporting expectations while overall processing became more efficient.

A scalable AI-powered document processing solution designed to handle high volumes of diverse business documents—without relying on rigid templates. The system intelligently interprets various formats, including scanned files, and automatically extracts key data tailored to client requirements. Trained on real client datasets, it delivers up to 98% accuracy, significantly improving efficiency and reducing manual workload.

In this project, We addressed the inefficiencies of manually reconciling orders, delivery notes, and invoices by developing a generative AI–driven solution. We integrated this system with existing workflows and ERP platforms to automatically analyze document content, standardize key fields, and flag discrepancies before users even open the files. By automating data population and highlighting inconsistencies, we streamlined previously manual tasks such as form completion and approvals. As a result, we reduced processing time by 30% and significantly minimized human error.

We addressed the challenges around inconsistent and incomplete workplace accident reports in the corporate HSE system by developing an AI-supported solution. Our approach helps standardize report quality by guiding authors with immediate feedback, reducing the need for costly training and easing the burden on limited HSE specialists. At the same time, we enabled faster processing and summarization of reports at headquarters by automating information extraction and analysis. As a result, report quality improved significantly, and employees gained a clearer understanding of reporting expectations while overall processing became more efficient.

We addressed the inefficiencies of manually reviewing and filtering resumes by developing a generative AI–based solution tailored for HR workflows. Our system analyzes resumes in various formats through an interactive chat interface, allowing users to iteratively refine searches and apply complex filtering conditions without reopening documents. We also generate an ideal candidate profile based on inputs such as company culture and automatically evaluate each resume against this benchmark. As a result, a process that previously required 3–4 hours of manual effort can now be completed in just a few minutes, significantly improving speed and decision quality.