Building and
operationalizing
AI-driven software
at enterprise scale

How AI can work for your
enterprise

See how AI and ML technologies empower your company to improve the way you run your business in the strategic areas.

Data-driven R&D and smart forecasting

Enhance your R&D projects with AI and Deep Learning solutions to accelerate data processing and new idea development. Build tools utilizing ML and Data Science tot enable your research team to anticipate demand, forecast trends, and deliver innovation at scale.

Optimized production and maintenance

Increase your enterprise's ability to produce goods and services at a lower cost and higher quality. Improve productivity and minimize support and repairs. Use AI to provide predictive maintenance, utilize data-driven insights, and manage the entire production process smartly.

Targeted sales and tailored marketing

Adjust your marketing communication to your prospect needs, provide value at every stage of the customer journey, plan further steps based on customer behavior, and increase customer lifetime value with effective data science.

Personalized customer experience

Build and enhance a strong connection with your brand by providing a personalized experience. Use AI to get to know your customers better and offer them tailored services. Analyze their user experience and deliver solutions that improve their satisfaction.

Using the most effective
approaches and
methodologies to develop
powerful software that
utilizes the capabilities of AI
technologies

Leverage enterprise-level AI services
to deliver your critical applications

Build data pipelines to utilize data-driven analytics in improving your business.

Data Engineering

Apply ML to your software, enable AI and Data Science to work for your company.

Data Science

Provide AI applications that improve customer experience and save operational costs.

MLOps

Need help in building
AI applications?

Contact us to get to know how we deliver AI-powered software.

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Get insights into AI technologies and software development
approaches that help to deliver AI-powered applications

Artificial Intelligence for Business Deep Learning Data Pipeline
Machine Learning Data Mining Operationalizing AI

Artificial Intelligence for Business

Defined as a machine's ability to learn, understand, and solve problems in a similar way to humans, Artificial Intelligence is one of the driving forces of innovation. With the continuous development of AI technologies, enterprises across industries use their features to improve customer service, increase safety, accelerate business operations, and automate repetitive tasks. AI, with its capability to process large volumes of data and learning at a rapid pace, has become a crucial factor in technology development. Artificial Intelligence speeds up the implementation of new technologies, e.g., empowers automotive to adopt driverless solutions based on processing to training data at a scale impossible for humans. Furthermore, AI provides governments and enterprises with powerful tools to detect frauds and scams, e.g., comparing declarative data with actual costs, looking for dubious transactions, and more. AI-powered software is a competitive advantage, and its impact is growing.

Deep Learning

Deep Learning is an ML technique that utilizes Artificial Neural Networks, a computing system of algorithms used to learn through improving the ability to solve problems and perform tasks by considering examples. Designed to recognize patterns, Deep Learning clusters and classifies real-world data input - images, sound, text, or time series. This technology works as a layer on top of the dataset that groups unlabeled data based on their similarities and classifies data using the labeled dataset. Deep Learning and ANNs can be used in fraud detection to divide the dataset into a group consisting of legal operations and suspect ones, in customer experience to classify delighted customer and unhappy ones, in visual inspection to find well-filled documents and these with errors.

Data Pipeline

Data Pipeline is a data processing engine that aggregates, organizes, and channels data for storage, insights, and analysis. By building data pipelines, enterprises collect raw data from various resources, transform them, and share with unites responsible for storage, analyzing, and business intelligence. Data pipelines accelerate the way software is developed and services are delivered, as they provide an easy to use framework for working with batch and streaming data inside apps.

Machine Learning

Machine Learning is a self-learning AI technology that develops itself autonomically by analyzing experiences, examples, and data, without a programmer intervention or assistance. ML-enabled applications learn by observing behaviors and getting insights from provided inputs. Such apps use collected insights to make better decisions and improve operations. Machine Learning analyzes large volumes of data, using computational intelligence to accelerate the pace the knowledge is used to determine business opportunities, improve customer experience, minimize risks, or reduce operational costs.

Data Mining

Data Mining is a process of collecting and analyzing raw, massive datasets to find patterns and determine insights that can be turned into a business opportunity, product improvement, or competitive advantage. Data mining tackles challenges that, without computational intelligence, can not be overcome manually due to the size of analyzed sources. The data mining process consists of five stages: collecting and loading data into a data warehouse, storing and managing the data (in-house or in the cloud), determining how the data can be organized, sorting the data based on the user's results, providing ready-to-use data presentations.

Operationalizing AI

AI operationalization is a process of applying ML models to particular use cases and adopting AI experiments to reliable production models. Developing an AI-enabled application requires a learning and validating phase. Such an application cannot be directly deployed to production. Every ML model needs a proper implementation phase when the solution is verified and adjusted to the environment. It's a critical process to implement AI in an enterprise and obligate both Data Science proficiency and software development expertise.