SERVICES
AI Training for Business and Decision Workshops
Challenge
AI is a strategic topic, but many organizations lack a shared understanding of its real capabilities and limitations. The result is scattered initiatives, lack of priorities, and decisions based on trends rather than business value and areas of effective application.
Solution
We deliver training and workshops based on knowledge, practice, and real business scenarios that translate AI into concrete decisions: from identifying use cases to implementation roadmaps.
Training and Workshops Include
We introduce the real capabilities
and limitations of AI,
thoroughly analyzing the problem
We analyze processes and identify areas with the greatest potential
We define specific use cases tailored to the organization
We prioritize initiatives based on value, cost, and risk
We create implementation roadmaps
(PoC → MVP → production)
We support decision-making at both executive and operational levels
Application Areas
- AI Strategy and Organizational Transformation
- Use Case Identification and Validation
- Process Optimization and Automation
- Preparation for AI Implementation
- Technology Roadmap Development
SERVICES
AI Consulting - Technology Advisory
Expert support in designing AI architecture, selecting tools, and building secure and scalable infrastructure.
Challenge
Organizations face difficult decisions: which technology to choose, what architecture to implement it in, how to secure data and meet regulatory requirements (e.g., GDPR, NIS2, ISO). Additionally, challenges arise related
to licensing models, tools, and AI services. Wrong decisions at this stage lead to high costs, legal risks, and scalability issues.
Solution
We provide access to experienced AI architects who help design optimal architecture - technically aligned with business needs, secure, compliant with regulations, and properly positioned within the ecosystem of available technologies.
How the Service Works
We analyze business needs, data,
and organizational constraints
We select models, tools, and technology providers (LLM, NLP, CV, and others)
We design solution architecture (cloud, hybrid, on-premises)
We advise on hardware infrastructure selection (GPU, servers, compute clusters)
We design on-premises environments for systems requiring full data control
We incorporate compliance requirements such as GDPR, NIS2, ISO, security-by-design
We support the selection and procurement of tools and external services, ensuring proper license alignment with usage patterns
We assess costs, scalability, and technological risks
We support teams in decision-making and architecture implementation
Application Areas
- AI Technology and Vendor Selection
- System Architecture (cloud, hybrid, on-premises)
- GPU Infrastructure and Compute Environments
- Security and Compliance (GDPR, NIS2, ISO, and others)
- Support in License and External Service Selection
- Cost and Scalability Optimization
SERVICES
AI Project Delivery from Concept to Launch
We take responsibility for the entire process - from initial concept to a working production system that generates real business value.
Challenge
Organizations often have ideas for using AI-based solutions but encounter barriers in execution: lack of competencies, scattered responsibilities, difficulties transitioning from prototype to production, and underestimating the complexity of integration and system maintenance.
Solution
We deliver projects comprehensively, combining architectural, research, and engineering competencies. We take responsibility for building a solution that works in a real business environment.
How the Service Works
We define the business problem and success metrics (KPIs)
We analyze data, processes, and technological constraints
We define the business problem and success metrics (KPIs)
We design the solution and architecture (AI + client systems)
We create a PoC (prototype) and validate assumptions
We build a production solution (models, backend, integrations)
We integrate with existing client systems and data
We deploy and launch the solution in the target environment
We provide monitoring, optimization, and iterative development
Project Delivery Standards
- Focus on delivering business value, not just technology
- Experience in transitioning from PoC to full production deployments
- Team combining research + engineering + architecture competencies
- Building scalable, secure, and development-ready solutions
- Pragmatic approach - technology selection aligned with business goals and objectives, not trends
Application Areas
- Business Process Automation
- Conversational Systems, Knowledge Bases, and AI Assistants (LLM, RAG, agent systems)
- Document and Unstructured Data Processing and Analysis
- Recommendation and Decision Systems
- AI Integration with Existing IT Systems
SERVICES
AI Team Augmentation with Proven Experts
Flexible support from experienced AI/ML specialists who are responsible for project delivery - from needs and requirements analysis to production deployments.
Challenge
Building an AI team is difficult and time-consuming: experienced specialists are scarce, and successful projects require not only programmers but also experts in architecture, research, and production deployments.
Solution
We provide a ready-made, multidisciplinary team of AI experts - from engineers and architects, through researchers publishing at top conferences, to experienced developers deploying systems in production environments.
Service Scope
We select specialists (Lead / Senior / Mid) for specific business problems
We provide competencies in multiple AI areas, including: RAG, LLM, CV, NLP, MLOps
We integrate experts directly into client processes (Scrum / Agile)
We support development, research, and model optimization
We scale the team as the project evolves
What Sets Our Specialists Apart?
-
Experience in delivering production projects (commercial client deployments)
since 2022 - Scientific publications and participation in major conferences (EMNLP, EACL, ICASSP, COLING)
- Team with strong academic background - composed exclusively of individuals with technical education and active AI doctoral candidates and researchers
-
Practical experience working with LLM, RAG, semantic search,
and agent systems - Competencies in building complete systems: from model to infrastructure (GPU, cloud, DevOps)
- Experience working with large organizations and regulated environments
Application Areas
AI and ML Development
System Integration
Technology Project Support
AMUai HUB – AI Control and Management Layer
AMUai HUB, the central AI management panel for organizations, is the layer that determines how, where, and by whom artificial intelligence is used. It supports the management, control, and optimization of AI operations through centralized oversight of models, users, and data, as well as monitoring usage, security, and costs.
Role in Architecture
AMUai HUB separates two critical layers:
- business (processes, applications, users)
- technological (AI models and their providers)
Results
- Model changes do not affect organizational operations
- AI becomes a managed resource
Operating Concept
Central AI orchestration layer managing how models are used across the entire organization
Intelligent query routing between different AI engines (AMUai Core, OpenAI, Gemini, Claude, proprietary models)
Single consistent and stable AI access interface for users and business systems
Data processing policy management, defining where, how, and by which models operations are performed
Real-time monitoring of AI model usage, quality, and costs
Separation of business and technological layers, ensuring operational independence
Ability to change AI providers without impacting business systems and processes
Flexible support for on-premises, hybrid, and multicloud environments
Parallel use of multiple AI models to optimize costs, quality, and security
Key Principles
Architectural Independence
• Changing AI engine ≠ changing systems
• Freedom of choice: on-premises, cloud, multi-vendor
Control and Security
• Control over data flow
• Ability to use on-premises environments
• Support for compliance and audits
Cost and License Optimization
• Dynamic model usage management
• Ability to choose the most cost-effective provider
• Better negotiating position (no vendor lock-in)
Long-term Stability
• Decoupling strategic AI layer from vendors
• Resilience to market changes
AMUai CORE: Execution, Logic, Intelligence
AMUai Core is the execution layer - the engine responsible for implementing AI logic and delivering specific business functionalities. This is where models, RAG, agents, and systems automating processes operate.
Role in Architecture
- Executes AI tasks
- Processes data
- Delivers specific business use cases
Operating Concept
Implementation of AI models (LLM, RAG, agent systems)
Building business logic and automation
Integration with client systems (ERP, CRM, documents)
Ability to operate on-premises or in the cloud
Extensibility with new modules and use cases
Can operate under AMUai HUB control, which determines when, where, and how Core is utilized
What Sets AMUai CORE Apart?
- Ready-to-use, production-grade AI modules
- Ability to work on proprietary infrastructure and data
- Combination of research and implementation approach (team with academic background)
- Scalability and integration with client ecosystem
- Ability to work closely with AMUai HUB (control + execution)
Core Modules
AI Knowledge Base
• Internal knowledge base designed specifically for AI utilization
• Secure sharing and access control to organizational know-how
• Foundation for AI agents and intelligent assistants
RFx AI Processing & Offer Generation
• Automatic analysis of tender documentation
• Offer generation based on organizational data and knowledge
• Significant reduction in offer preparation time and increased efficiency
Intelligent Department
• Intelligent document and correspondence processing
• Automatic classification and case routing
• Support and relief for operational departments
AI Agreements Safeflow
• Automatic analysis of contract compliance with policies and regulations
• Identification of risks, gaps, and deviations
• Standardization and streamlining of legal processes