AI agents built for one 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.
Artificial intelligence enters a company when it solves a problem someone actually has.
Or write to us: info@ggtechnologies.sm
30+
years of experience
10+
automated factories
100%
developed in Europe
Where we start
Demos always work. Projects stall afterwards.
Demos always work. AI projects stall afterwards, when the model meets the company's real data: incomplete, badly written, scattered across five different systems.
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
The personal AI workstation for people who work with confidential data.
European software
Designed and developed entirely in Europe.
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.
Measuring the result
Before we write code we agree how we will know it worked: hours saved, errors avoided, response times.
- One agreed indicator, set before we start
- Measured against how the work is done today
- Corrections driven by the measure, not by impressions
Data analysis and transformation
Extraction, normalisation and reading of unstructured data: 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. Nothing gets rebuilt.
- Plugs into your ERP and the systems you already run
- Automating repetitive steps
- No migration: 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 our data have to be clean before we start?
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 our documents end up in a third-party model?
Only if you choose to. We build architectures where models run on your own server and documents do not leave. 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.
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.