AI & automation
GPT runs inside the automation: it understands meaning, classifies, replies and extracts data.
Unlike the classic approach, the AI makes the decision, not rigid if-then rules. The LLM reads an email or a lead, grasps the meaning and picks the action itself. Launch in 2-3 weeks on Make, Zapier and n8n via the OpenAI API.
AI workflow automation means scenarios where an LLM (GPT) runs inside the automation: it understands the meaning of a request, classifies and prioritises enquiries, drafts replies and extracts data from documents. Unlike the classic approach, the AI makes the decision, not rigid rules. According to McKinsey, generative AI can take on work activities that today absorb up to 60-70% of employees' time. Launch in 2-3 weeks on Make, Zapier or n8n. Request a quote and get an estimate within 24-48 hours.
Here we explain smart business process workflows with AI for companies in Valencia and across Spain. The key difference from the classic approach is that an LLM (GPT) runs inside the scenario, understanding the meaning of text and deciding by itself instead of following rigid if-then rules. This unlocks tasks the classic approach cannot handle: classifying and prioritising incoming leads by content, replying by the meaning of the request, generating content and draft emails, extracting data from emails, invoices and PDFs, and smart routing by topic and tone. Technically it is built on the same platforms (Make, Zapier, n8n), but with a call to the OpenAI API (GPT models) at the required steps. Below you will find a step-by-step plan to add the AI node to your process, a comparison table with the classic approach and answers to common questions. At the end, our “AI & workflows” packages with real prices.
The approach is the same as in the classic approach, but with one key difference: you find the step where a person was needed to read text and decide, and replace it with an LLM call. For example, a lead arrives, GPT reads it, determines the topic, urgency and the right team, and the automation routes it onwards. The AI decides by meaning, not a keyword rule. The scenario is built on Make, Zapier or n8n, and the AI step is an OpenAI API call with a well-crafted prompt. Start with one process where “understanding text” delivers the most visible effect.
Classic automation runs on rigid rules: “if the email contains word X, do Y”. It is reliable on structured data but helpless where you must understand the meaning of free text. Smart AI automation adds an LLM (GPT) to the scenario that reads the text, grasps the context and decides by itself. That is why it handles tasks rules cannot: it parses real conversations, classifies enquiries by topic, drafts meaningful replies and extracts data from unstructured documents. Below is a comparison by key parameters.
| Parameter | Classic automation | Smart AI automation |
|---|---|---|
| Who decides | Rigid if-then rule | LLM (GPT) by the meaning of text |
| Data type | Structured (forms, fields) | Free text: emails, documents, chats |
| Typical tasks | Lead routing, notifications, syncing | Classification, replies, data extraction |
| Launch time | 1-2 weeks | 2-3 weeks + prompt tuning |
AI automation pays back most where staff spend hours reading and interpreting text: incoming leads, correspondence, invoices, contracts, support. GPT handles such tasks in seconds with predictable quality, and a person only reviews borderline cases. This moves the team from routine to judgement-based work. The scale of the effect is backed by research: generative AI can take on a large share of working time precisely through its handling of language.
«A model helps where a person decides and the preparation can go to the machine. The moment it is handed the decision, mistakes begin, and they are still yours to answer for.»
Smart process workflows differs from the classic approach because an LLM (GPT) makes the decisions inside the scenario, not rigid rules. This frees the team from the most demanding routine, reading and interpreting text: classifying and prioritising leads, replying by meaning, generating drafts, extracting data from emails and documents, and smart routing. It is built on Make, Zapier or n8n with an OpenAI API call and launches in 2-3 weeks. Start with one process where “understanding text” gives the biggest effect, then expand the same way. According to McKinsey, generative AI can automate a significant share of work activities through its handling of language. Request a quote and we will prepare your “AI & workflows” plan and estimate within 24-48 hours.
AI and automation
n8n, Make, GPT-API, voicebots and document OCR, from €490, paid once. We count the hours routine work eats up and automate exactly that: basic automation is live in 4-5 days.
We start by mapping your processes: we find where someone copies data by hand from an email into a spreadsheet and from there into the CRM, and remove exactly those steps. It goes in once and the automations then run on their own, with no monthly fee to us. Everything is built on your accounts and credentials, so the solution stays yours even without us. Custom AI development in Spain starts at €3,000; our Start tier is six times cheaper.
A short brief, and we’ll come back with a plan and quote within 24-48 h. No pressure.
It is automation with an LLM (GPT) running inside it. The model reads the text of a request, email or document, understands the meaning and picks the action. Unlike the classic approach, the AI decides, not a rigid keyword rule.
Classic automation runs “if X then Y” rules on structured data. AI automation adds GPT, which understands free text and decides by meaning: it parses conversations, classifies enquiries and extracts data from documents. In practice both approaches work together: AI understands the text and classic rules carry out the actions.
Classifying and prioritising leads, replying by meaning, generating content and draft emails, extracting data from emails, invoices and PDFs, and smart routing by topic and tone. Tasks where you must understand text, not match fields. We start with the task where manual reading of text takes the most time.
We build scenarios on Make, Zapier or n8n and connect the AI steps via the OpenAI API (GPT models). When needed we use other LLMs. The model choice depends on the task, text volume and the cost of requests. A model switch is planned in from the start: if a cheaper or more accurate one appears, the scenario switches without a rebuild.
Accuracy depends on the prompt and the examples. We test the scenario on real data and add human review for borderline cases. The AI does not make critical decisions alone: it prepares an option and a person confirms the final action.
A basic AI scenario with one process launches in 2-3 weeks, a bit longer than the classic approach due to prompt tuning and testing. Timelines depend on the integrations and the volume of examples available. In the first days after launch we review the AI's decisions on real requests and adjust the prompts where it errs.
Yes, when set up correctly. Connections use the OpenAI API with access keys, permissions are kept to a minimum and sensitive fields can be masked before sending. OpenAI API requests are not used to train models without consent. Which fields to mask is decided together with you during the audit.
Yes, that is the recommended path. We pick one process where understanding text gives the biggest effect (for example, triaging incoming leads), make it automatic and expand to neighbouring tasks the same way. Each next process launches faster than the first: the prompts and connections are already in place.
GPT understands and processes text in Russian, Spanish and English, plus dozens of other languages. That suits a business in Spain with a multilingual audience: one automation handles requests in several languages without separate rules. Replies to customers go out in the language of their enquiry.
The price includes a process audit, design of the AI step and prompts, turnkey scenario setup, OpenAI API and tool connections, testing on real data and launch support. OpenAI API tokens and platform fees are paid separately. Token spend is estimated before the start from the volume of your texts, so the full budget is visible.
