AI and enterprise automation

We choose AI models dynamically according to the task through model routing. Complex analysis and reasoning go to stronger models, while quick routine tasks are routed to smaller, cheaper models to control costs. For high-security requirements, open-source models can run on the client's infrastructure or with a selected provider.

Yes, modern models understand complex professional languages, including legal and technical terminology, very well. However, in production solutions, we do not rely solely on the model itself. We structure and validate the outputs, and for sensitive processes, we set up human oversight.

The AI model never gets direct database access. A custom Node.js middleware layer sits between the model and corporate systems. The AI prepares a structured proposed output, the middleware validates it, checks user permissions, and only allows the next step or system write after a successful check.

Running costs depend on the volume of processed data, the chosen models, and the number of automated steps. With API models, you usually pay per token, not per user license like ChatGPT Plus. We reduce costs by optimizing prompts, caching, and routing simple tasks to cheaper models.

No-code tools are practical for simple notifications and data transfers between services. But when AI needs to decide on data structure, write to an ERP, handle API outages, or process larger data volumes, no-code scenarios often hit their limits. Our automations are built directly in code, with validation, logging, and error checking.

We start with a small pilot on one specific process. Before development, we jointly set measurable criteria: how much manual time the automation should save, what accuracy it must achieve, and where a human must remain in the approval loop.

Yes. For special regimes such as government, healthcare, or law, open-source AI models can run on infrastructure you own or have contractually secured with a chosen provider. In this mode, sensitive data can remain entirely outside public API services.

This depends on the scope and quality of available data. Simpler automations, like sorting emails or extracting invoices, can often be verified within a few weeks. Complex autonomous systems with a RAG database and integration with multiple internal systems usually require several months.

Our goal is not to replace people, but to eliminate repetitive and tedious work. AI can process documents, emails, or data significantly faster than a human, but for important decisions, clear responsibility, oversight, and the option for human intervention must remain.

AI can assist in processing orders from PDFs or XML feeds, evaluating supplier data, checking documentation, and optimizing warehouse processes. Its greatest benefit is found where people currently manually rewrite or verify large amounts of repetitive data.

Yes, if the first process has a clear benefit. A smaller company does not have to build a complex AI agent immediately. Often, it is enough to automate one narrow problem: sorting inquiries, extracting documents, checking product data, or searching internal documentation.