AI Agents for Industry — A Complete Guide for Industrial SMBs
How to deploy an enterprise AI agent in an industrial SMB: RAG architecture on private data, cloud vs on-premise, GDPR compliance and real costs without per-conversation licensing. An honest technical guide based on real projects.
What is an enterprise industrial AI agent?
An enterprise AI agent is not ChatGPT with your logo: it is a system with controlled access to your data (catalog, manuals, ERP, machine logs), able to execute measurable actions within your business and auditable end-to-end. The difference with a generic chatbot is structural.
Real use cases in industrial SMBs
These are the cases where an AI agent delivers measurable return in a mid-sized industrial company. Not experiments: each case has a business metric behind it.
RAG on private data (catalog, manuals, ERP)
RAG (Retrieval-Augmented Generation) is the standard technique to make the model answer only on your verifiable data and never «invent». The difference with fine-tuning is radical: with RAG you do not train the model with your data — you retrieve it in real time.
Cloud vs on-premise — when to choose each
No deployment is «better» in the abstract: it depends on the data the agent handles, the regulatory framework of the sector and the real query volume. Honest criteria below.
GDPR compliance and sensitive data
Any AI deployment in a European company has to pass the GDPR filter and, from 2026, the EU AI Act. The good news: most industrial cases are low risk. The important news: it has to be documented.
Real cost (without per-conversation licensing)
This is where most commercial proposals mislead. The number that matters is not «X € per conversation» but the total 12 and 36-month cost compared to actual savings.
FAQ
How much does an AI agent cost for an industrial SMB?
A base cloud project is typically €8-15k of one-off development, with maintenance from €200 to €600/month. Inference (model consumption) is prepaid to the provider (Anthropic/OpenAI) and costs €0.003-0.015 per query. An on-premise case is €18-25k due to hardware but eliminates variable inference.
Do I need my own GPUs?
Only if you want fully on-premise. For most industrial cases connecting to a cloud provider (Anthropic Claude, OpenAI or Azure OpenAI) is enough, no GPU required. For full autonomy, an NVIDIA RTX 4090 (~€2,500) already runs quantized Llama 3.3 70B with decent quality.
Is it trained on my data? Does my data leave my infrastructure?
It is not trained on your data: we use RAG, which queries your document base in real time and only sends the model the fragments needed to answer. With a signed DPA, cloud providers do not train on your inputs. For absolute guarantee, an on-premise deployment ensures nothing ever leaves.
What happens if the model becomes outdated?
Model and document base are independent. If Claude 4.8 or Llama 4 is released, we switch endpoints and the agent keeps working with your data. Your documents reindex automatically when updated. There is no risk of being locked into a single version.
Can it connect with my SAP/Odoo/Holded?
Yes. ISIGECO integrates the agent with your ERP via its standard API (Holded, Odoo, SAP S/4HANA, Microsoft Business Central) or, if absent, via a custom connector. The agent can read orders, stock, customer terms and create new orders, quotes or tickets.
How is it different from ChatGPT Enterprise?
ChatGPT Enterprise is a generic interface: the model does not know your catalog, does not connect to your ERP and does not execute actions. An ISIGECO agent is built over your data, integrated with your systems and trained in your company's processes. The difference between an intern and a specialist.
How long until it's operational?
A functional MVP (1 use case, ~50-200 indexed documents, no ERP integration) is ready in 3-5 weeks. A full production agent with ERP integration, monitoring and admin panel takes 8-14 weeks. We work in two-week sprints with client validations.
Who maintains the system?
ISIGECO offers monthly maintenance (€200-600/month) covering monitoring, prompt tuning, indexing of new documents, model updates and support. Alternatively, we train your IT team to run it in-house: most of the code is standard Python and JavaScript.