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Process Automation
AI enables the automation of routine operations, reducing costs and increasing efficiency
Data
Processing
AI analyzes large volumes of information, identifying patterns and anomalies.
Speech Recognition
AI understands and interprets human speech, images, and written text.
Forecasting and analytics
AI models predict future trends and outcomes based on historical data.
Logistics optimization
AI improves supply chain efficiency by optimizing routes
Personalized Suggestions
Machine learning algorithms analyze user behavior and generate personalized offers.
AI-solvable problems
15 years
within the IT and software domain
+100
successfully executed IT projects
5 years
within the field of Artificial Intelligence
About us
We create intelligent AI-driven products from concept to implementation, optimizing processes and improving efficiency where traditional methods fall short
Neural network implementation options
Smart chatbot serving as a universal business assistant
Development
Stages
Preparation Stage
Business analysis, technical specifications, research, architecture design.
Dataset Collection
Gather and structure data, clean and annotate, split for training.
Building AI System
Building and configuring the neural network; training it on the dataset.
Module Infrastructure
Admin panel, database, web services, API, data preprocessing / postprocessing.
Integrating AI Module
Deploy AI, integrate with company systems, support and scale
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The cost of AI work is only a small fraction of the cost of human labor (from one third to one tenth).
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AI operates in the same way a human does, so no changes to a company’s technologies or processes are required.
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Once trained, AI can perform processes continuously.
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Every step taken by the AI is recorded, ensuring fully transparent auditing.
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AI can work at night and on weekends if necessary.
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AI doesn’t get sick, doesn’t require vacations, and doesn’t need salary increases or career advancement.
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If a process changes, the AI needs to be retrained only once, instead of retraining an entire group of employees.
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It is necessary to monitor the server running the neural network and perform periodic maintenance or ensure timely cloud payments.
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Server load should be checked regularly, since increased usage may require more computational resources, with monitoring potentially automated.
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As processes or data change, the AI model must be retrained to incorporate updates and remain accurate.
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Factors that can increase the timeline
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Poor or insufficient data
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Complex business processes
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Need for custom infrastructure
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Multiple integrations
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