--- title: "AI consulting and custom AI development — G&G Technologies" description: "AI consulting and development for companies: agents and LLMs integrated into your processes, RAG over your documents and integration with your ERP." publisher: "G&G Technologies" lang: en canonical: https://ggtechnologies.sm/en/services/artificial-intelligence/ translation: https://ggtechnologies.sm/servizi/intelligenza-artificiale/index.md --- # AI applied to real work, not to demos. *Artificial Intelligence* Agents that read your documents and query your systems. AI does the heavy lifting, the decision stays with whoever answers for it. - [Talk to us](mailto:info@ggtechnologies.sm?subject=Information%20request) Or write to us: [info@ggtechnologies.sm](mailto:info@ggtechnologies.sm) - **1997** the first project - **18** years in manufacturing - **900** workers in the study *Where we start* ## Demos always work. Projects stall afterwards. The model in the demo works on clean data. Yours is incomplete, badly written and scattered across five different systems: that is where the project stalls. So consulting starts from the process: where time is lost, where mistakes happen, where a piece of data already exists but nobody reads it. Then we build agents that do one defined job, integrate language models (LLMs) into the systems you already run and — when the data is confidential — keep it inside the company. #### DigiSense® The [registered framework](https://ggtechnologies.sm/en/digisense/) we build every AI implementation on. #### Podz.AI Our own product: [download it and try it for thirty days](https://ggtechnologies.sm/en/podz-ai/). The quickest way to see how we work. #### European software Designed and developed in Europe: what matters is the jurisdiction your data answers to, not just where we work. *The proof · the reasoning, written down* ### On agents, the hard question we have already asked ourselves. An AI agent cannot tell the data it reads from the instructions it receives: to it they are the same text. That is what makes connecting an assistant to the mailbox or the business system an architecture decision rather than a setting. We have written it up at length, resting the argument on a vulnerability with a public identifier — **CVE-2025-32711** — that anybody can go and read for themselves, with its date and its severity ratings. It is a documented case, and the article lists its sources at the foot of the page. It proves one thing only, and it is the thing that matters before you sign: the reasoning has been done, and it is public. You can check it yourself instead of taking our word for it. [Read the article](https://ggtechnologies.sm/en/insights/autonomous-agents-boundaries/). And the author can be checked the same way: he completed the European [EITCA/AI](https://www.eitci.org/val.php?id=EITCA/AI/SLJ25004875&t=gp35PH6xztHR9qRw) programme in artificial intelligence and maintains a free public manual on an AI assistant, [*Claude: la guida completa*](https://angelogeminiani.github.io/claude-la-guida-completa/), in Italian. *What we build* ## Four ways AI enters a company starting from what is already there. If documents cannot leave the company, that is a separate story: [how on-premise AI works](https://ggtechnologies.sm/en/services/on-premise-ai/). For the people who will use the tools there are our [training courses](https://ggtechnologies.sm/en/training/). ### Agents built for one defined job Assistants built around one precise task, with access to company data and tools. - One defined task, not a generic chatbot - Controlled access to company data - Verifiable answers, with sources ### The person who validates AI reads, proposes, prepares. Whoever answers for the decision checks it and signs it off: we design it that way from the start, we do not bolt it on at the end. - Every proposal comes with the sources to check - The person deciding sees what they are deciding on - One agreed indicator, set before we start and measured after ### Data analysis and transformation Extraction and reading of data that does not sit in a table: documents, logs, archives. - Reading documents and historical archives - Normalising scattered data - Semantic search over your documents (RAG) ### Integration into existing processes AI plugs into the ERP and the flows you already run, and they stay as they are. - Plugs into your ERP and the systems you already run - Automating repetitive steps - The data stays where it is *How we work* ## First we understand the process, then we choose the technology. 01 ### Pick the task Which task you would like to delegate, and how it is done by hand today. 02 ### Decide where it runs Which model, where it runs, what data it sees. The choice changes a lot when data is confidential. 03 ### Build and test Development, integration into systems in use, and testing with the people who will use it. 04 ### Measure Measuring against the indicator agreed at the start, then correcting. *FAQ* ## Frequently asked questions ### Does my data have to be in order before I call you? No — it almost never is. Cleaning it up is part of the job: the data sits in documents, archives and logs today, and making it readable is what we do first. What you do need is to know which decision the data has to support. ### Do my documents end up in a third-party model? Only if you choose to. We build architectures where models run on your own server and the documents stay there. When cloud power is needed, personal data can be masked before it is sent. ### What is the difference between an AI agent and a chatbot? A chatbot answers. An agent performs a task: it reads a document, queries a system, produces a verifiable result. The practical difference is that an agent's outcome can be measured. ### What proof can I check before talking to you? Three things, and all of them from outside: Podz.AI can be downloaded and tried, the articles list their sources at the foot of each one, and the wearable study has a permanent identifier. Client names stay out of it: almost all of it is covered by confidentiality agreements, and a firm that shows you other people's names will one day show yours. It is the same choice we propose for your data. Everything else we discuss by describing the problems, not the names. ### How do you tell whether an AI project is worth it? You pick the measure first: hours saved, errors avoided, response times. If the measure cannot be defined before you start, the project usually is not ready. *Insights* ## Technical analysis on the same subject. ### [Before you hand an AI agent the keys](https://ggtechnologies.sm/en/insights/autonomous-agents-boundaries/) To an AI agent, the content it reads and the instructions it receives are the same text: that is prompt injection. Three questions to ask first. ### [The AI Act and client data](https://ggtechnologies.sm/en/insights/ai-act-client-data/) What a firm using AI on client data actually risks, between the AI Act, the GDPR and Italian Law 132/2025, and what to do from Monday morning. ### [The manual ages before the machine](https://ggtechnologies.sm/en/insights/documentation-that-ages-well/) Why a plant manual goes wrong in a single line, and how to assemble one that holds: the procedure on one side, the values that expire on the other. ## Where to go next ### [On-premise AI](https://ggtechnologies.sm/en/services/on-premise-ai/) How to choose between fully local, hybrid and cloud, starting from your data. ### [Podz.AI](https://ggtechnologies.sm/en/podz-ai/) The product, ready to install: it reads your files, 30 days to try it. ### [Robotics & Automation](https://ggtechnologies.sm/en/services/robotics-automation/) Automation at the machine, integration with your systems, downtime measured. ## Is there a task you would like to delegate to AI? Tell us how you do it by hand today. That is where you find out whether AI is actually needed. - [Email us](mailto:info@ggtechnologies.sm?subject=AI%20project) - [Call us](tel:+3780549900824)