Automated flows powered by artificial intelligence

What is Automation with AI?

Classical automation resolves processes based on deterministic rules. But many real processes include steps that require interpreting natural language, classifying ambiguous documents, extracting data from unstructured formats, or making decisions where the rule is non-binary. AI automation adds language, vision, and classification models to flows to solve those steps without constant human intervention.

At Q2BSTUDIO we integrate AI into workflows for companies that need to automate processes where input variability prevents fixed rules. The most common cases include: automatic classification of emails, tickets or requests by intention and urgency; data extraction from invoices, contracts or documents with variable formatting; summary of long texts for approvers; sentiment analysis in customer feedback; intelligent content-based routing; and validation of data with probabilistic criteria.

The architecture combines the automation flow (orchestration, connectors, rules, errors) with calls to AI models in the steps that require it. It's not that the whole process is AI: most of the steps are still deterministic (move data, notify, create record). AI is used surgically at the points where a fixed rule does not resolve. This optimizes costs (models are only invoked when they add value) and maintains predictability in the rest of the flow.

Each step with AI includes confidence assessment: if the model does not reach the defined threshold, the case is escalated to human review instead of acting with high uncertainty. This hybrid pattern (AI + human) is critical for processes with real impact. We also implement periodic evaluation: we compare model decisions with human reviews to detect degradations and adjust thresholds.

Technical integration uses model APIs (OpenAI, Azure AI, open source models) from the automation flow. We manage versioned prompts, structured outputs (JSON schema), response validations, fallbacks against model errors and token budgets. The model is treated as one more component of the flow: with defined inputs, expected outputs, metrics, and fault management.

It is important to distinguish this service from AI agents from the Artificial Intelligence hub. Here AI is a component within an orchestrated flow: it executes a step and returns a result. An AI agent has autonomy to plan multiple steps, select tools, and adapt its behavior. We use automation with AI when the process is clear and the AI only solves a specific step; agents when autonomy and adaptation are needed.