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
Artificial Intelligence
AI applied to real work, not to demos.
Agents that read your documents and query your systems. AI does the heavy lifting, the decision stays with whoever answers for it.
Or write to us: 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 we start 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 we build every AI implementation on.
Podz.AI
Our own product: download it and try it for thirty days. 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.
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. For the people who will use the tools there are our training courses.
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.