The most frequent questions about AI agents, RAG, training and AI Architect in Residence.
A clear collection of the main answers to understand what artificial intelligence can do in business, when it makes sense to develop an AI agent, how to manage data and costs, and how the Ergoware AIR service works.
An AI agent does not simply answer like a chatbot. It can analyze information, follow a procedure, use external tools, read documents, prepare structured outputs and support operational activities with a certain level of autonomy, always with defined rules and supervision.
A chatbot converses and answers. Automation executes a predefined procedure. An AI agent can interpret an objective, consult sources, choose operational steps, use tools and produce a more articulated result. The difference lies in the level of context, operational reasoning and system integration.
RAG means Retrieval Augmented Generation. It is a system that allows AI to consult company sources such as PDFs, manuals, procedures, contracts, price lists, emails or internal documents before generating an answer. It helps make the output closer to real, verifiable and updatable information.
No. A well-designed RAG reduces the risk of invented answers because it binds AI to retrievable sources, but it does not automatically remove every error. Human control, source quality, logging, testing, fallback rules and output checks are needed.
Because many SMEs have repetitive tasks, scattered documents, manual reports, data to read, customers to follow, procedures to verify and flows that consume time. A well-designed AI agent can reduce manual work, improve order and speed up operational decisions.
No. Two companies in the same sector can work very differently. Software, people, habits, procedures, data and priorities change. That is why truly useful agents must be designed on the company's actual flows, not copied as standard packages.
Ergoware AIR means Ergoware AI Architect in Residence. It is an ongoing service where a dedicated AI Architect supports the company with a monthly allocation of hours to analyze processes, design roadmaps, develop AI agents, RAG systems, automation, training and cloud or on-premise infrastructure.
No. It is not body rental and it is not simple hourly consulting. It is strategic and operational AI support: process analysis, agentic roadmap, progressive development, documentation, training, governance and support in building an internal AI capability.
No. The initial phase is paid because it produces a real output: analysis, opportunity map, priorities, risks, hour estimate and roadmap. If the company later activates the ongoing service, the cost can be deducted from the contract.
The reference rate is €90/h, but the service is organized into monthly plans to make the budget predictable. Plans start from 8 hours per month and can reach 40 hours per month, with the possibility of special agreements for higher needs.
No, unless specifically agreed. API, token, cloud, storage, licence, server or hardware costs are estimated separately. An important part of the work is choosing the most sustainable AI model among ChatGPT, Gemini, Claude, local models or hybrid architectures.
It depends on privacy, data processed, budget, required speed, model quality, number of users and workload. Cloud is often faster to start. Local or on-premise AI may be more appropriate when sensitive data, infrastructure control or recurring costs become central.
A locally installed model has no per-token costs like cloud APIs, but it still has indirect costs: hardware, GPU, electricity, storage, configuration, updates and maintenance. The choice must be made after a technical and economic assessment.
Yes. Integrations with Google Workspace, Drive, Gmail, Sheets, databases, CRM, ERP and other tools can be designed through APIs, automation or MCP architectures. Feasibility depends on permissions, security, data and operational objectives.
Yes. It is one of the most concrete cases: document reading, classification, data extraction, checks, tables, reports, review workflows and operator supervision. In these cases, privacy, accuracy and traceability are essential.
No. It can be designed for entrepreneurs, managers, administration, marketing, sales, professionals and operational teams. The path can be introductory, practical or advanced, depending on people's level and business objectives.
Courses can be created on practical AI use, prompts, AI agents, website creation, business application development, images and videos, market research, presentations, qualified reports, marketing plans, automation and RAG.
It depends on complexity. A simple prototype can take little time, while an agent connected to data, documents, APIs, permissions, dashboards and business workflows requires analysis, design, testing and iterations. That is why it is better to start from a precise use case.
No. Sometimes it is enough to configure existing tools, build procedures, create a knowledge base, use automation or design robust prompts and templates. Software development is needed when the flow requires interfaces, databases, integrations or custom features.
You can use the website form explaining industry, company size, software used, critical area and objective. From there we evaluate whether to start from training, an AI agent, RAG, an AI Readiness Check or the Ergoware AIR service.
Do you have a specific question about your business case?
Every company has different processes, data and tools. The best way to understand whether you need an AI agent, RAG, training or an AI Architect in Residence is to start from the real problem.