AI/ML 
Rozwój oprogramowania 
AI Agents
LLM-based AI Agents
Why are AI Agents becoming increasingly important?
In the traditional operating model, ERP and CRM systems functioned largely in a sequential manner. Data was entered manually, reports were static, and decisions were made with delays — limiting organizational flexibility and operational effectiveness.
Today, with the implementation of an integrated AI agent, it is possible to analyze data, make decisions, and trigger actions in real time. This approach gives companies a tangible competitive advantage by enabling faster responses to changing market and internal conditions.
The key innovation is that the AI agent goes beyond simply presenting information. It acts proactively — integrating automatically via API with ERP and CRM systems, analyzing data, drawing conclusions, initiating tasks, escalating actions where necessary, and generating accurate reports and recommendations. In practice, this results in fewer errors, faster processes, better decision quality, and greater operational transparency across the organization.
Common Challenges Faced by Clients Before Implementing an AI Agent
Manual and time-consuming data entry
Employees spend hours copying and matching data between systems — a process that often leads to errors.
Lack of real-time information and delayed decision-making
A standard report might be “from yesterday” — but decisions need to be made today, in real time.
Data silos between ERP and CRM systems
Lack of a complete view of the customer and operations — sales, service, and finance operate in silos.
Limited scalability of operations
As a company grows, manual processes become a bottleneck and slow down further development.
How does the AI Agent solve this?
- Real-time auto-analysis – The agent monitors ERP/CRM data, detects anomalies, and instantly suggests or initiates actions.
- Natural interaction (NLP) – The user asks a question in natural language (“Who is our top client this quarter?”) and receives both an answer and a recommended action.
- Full API integration – The agent operates directly within the client’s systems, eliminating the need for manual synchronization and reducing errors and manual work.
- Autonomous decisions and actions – Not just reporting: for example, creating a purchase order, sending reminders, or escalating an invoice.
- Improved scalability and efficiency – Companies can handle more processes with the same resources, thanks to automation.
- Improved data quality and better decision-making – Fewer manual entry errors and better data alignment across systems lead to higher-quality analytics.
- Enhanced customer and employee experience – Faster responses, fewer manual tasks, and more focus on strategic work.
Our AI Agent Implementation Methodology
Together with the client, we map processes, identify pain points, and define goals and KPIs.
We design the integration architecture, define the agent’s roles, and configure data flows, API settings, and security protocols.
Execution: configuration, integration, testing, user training, and pilot deployment.
After launch, we support the transformation by monitoring performance, optimizing the agent, and scaling the solution.
What do you gain by working with us?
- Reduced operational process time and lower costs associated with manual work.
- Ability to scale faster with minimal increase in operational costs.
- Better decision quality through real-time data analysis and ERP/CRM integration.
- Increased employee productivity — less routine, more strategic work.
- Competitive advantage — aligning processes at both the technological and operational levels.
Where does the AI Agent deliver the most value?
An LLM-based AI Agent is designed for companies ready to move from fragmented automation to fully contextual data and decision integration — with no excess coding, no delays, and no skill barriers. The AI Agent supports teams at the intersection of operations, sales, customer service, and finance, accelerating response times and improving decision quality.
In companies operating across multiple systems simultaneously — where data flows between ERP, CRM, communication tools, and sales platforms, and synchronization requires fast analysis and real-time decision-making.
Organizations with multiple departments and locations — the AI agent understands the context of processes in a distributed environment (e.g. multiple branches, languages, currencies), automates information flow, and ensures consistent service quality.
In industries that rely heavily on data and document workflows — such as manufacturing, logistics, retail, field services, or consulting — where fast decisions, reporting, and team communication based on a shared data source are critical.
In companies investing in automation, digitization, and scaling — where the AI agent is not only a support tool for current operations but also a catalyst for digital transformation, reducing manual work and accelerating key processes without expanding teams.
In sales and operations teams that need rapid access to knowledge — the AI agent responds in natural language to questions about order status, payment delays, resource availability, sales forecasts, or performance metrics — without needing BI tools or Excel.
