Cases
Success stories
AI and software projects we have delivered for our clients.
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AI-powered solution for investment document analysis
The client specializes in investment fund analysis. The aim of the software is to dramatically reduce the time investment analysts spend reading fund descriptions, presentations and financial data. The solution allows for the creation of condensed and direct analysis of the funding firm, track record of previous funds, etc.
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AI-driven Chatbot for real estate
The client is a major real estate company in Dubai, providing the highest level of services to local and international clients.
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AI/LLM to Extract Information from Medical Records
The client is an Israeli entrepreneur in the medical field who wanted to ensure that the AI solution he needed could be developed. He had tight deadlines and additional functional requirements.
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AI-Chatbot for e-commerce platform
One of America's leading multi-format retailers. During the refinement process of Target's internal production chatbot, Innova's team unified the applied solutions by refactoring the existing codebase and introducing standardized reusable components. This approach enabled us to optimize the implementation costs for new scenarios and enhance the reliability of the existing system.
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AI Chatbot for Room Booking via WhatsApp
The client is a company managing a network of hotels in Eastern Europe.
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AI-powered Review Response System
The client develops a web application that enables marketplace sellers to manage their sales, access analytics, and more.
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AI-based Chatbot for Automotive Dealerships
Our client is a company specializing in automated solutions for automotive dealerships in Eastern Europe.
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AI-powered Chatbot for Enhances Customer Support
Our client is a leading enterprise software company specializing in IT services and customer support.
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KPI\SLAs compliance monitoring
Microsoft requested an automated way of tracking Azure-related KPIs and SLAs for meeting the monthly targets. KPIs were based on Azure logs and multiple other data sources. Before implementing the automation, all reporting was done manually. We created a data pipeline using C# and .NET that processed different data sources, including Azure operations logs, 10Tb of logs per day. Our team used distributed processing via DataStudio and Scope (internal Microsoft tools) to process this amount of data.
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