AI automation, assistants and agents

Yes. We normally validate one measurable AI scenario first and expand only after the result is clear.

The architecture is selected around your data requirements, access controls and hosting constraints.

No. We select the provider and model around language, quality, availability and deployment needs.

AI is useful for unstructured text, documents and requests phrased in different ways. Deterministic rules are safer when the workflow is predictable and every condition can be listed in advance. A solution can combine both approaches.

An assistant prepares an answer, summary or recommendation for an employee. An agent can perform agreed actions in connected systems. Its permissions, confirmation points and quality criteria must be defined in advance.

No. RAG retrieves relevant fragments from approved sources and supplies them with the request. It does not retrain the model; quality depends on the documents, search, access controls and answer review.

Beyond development, include hosting, model usage, external services, storage, monitoring and maintenance. The estimate depends on workload, request length, retries and human review. We first define the scenario and expected usage; there is no universal price without those inputs.

Use current sources, document owners, a permission matrix and test questions with expected answers. Check citations, refusal when information is missing and protection of restricted documents. A pilot establishes only the tested scenario. A customer-facing assistant does not by itself demonstrate a corporate RAG implementation.

An assistant clarifies request details and hands context to a manager. The published packaging case connects Telegram, MAX and Bitrix24. It demonstrates a specific qualification workflow, not an autonomous sales department. Prices, terms and exceptions remain subject to review by a responsible employee.