SERVICES — AI INTEGRATION
AI integration for businesses
A chatbot is not a strategy. Bringing AI into a company means connecting it to your data and your systems, with controls that make it as reliable as the rest of your software.
Empatix integrates artificial intelligence into companies' processes and software. We do not sell an AI product: we build the connection between language models and your data, your documents and your systems, with the controls needed to run them in production.
From chatbot to process
Trying an AI assistant is easy. Making it work inside a company is another matter: it has to know your data, respect your permissions, produce results in the format the rest of your software expects, and leave a trace of what it does. It is software engineering before it is artificial intelligence. We wrote about it in detail in our article on multi-agent architectures.
Where AI genuinely saves time
- Documents: reading orders, invoices, contracts and technical sheets, and extracting their data for your management system.
- Internal search: asking a question in plain language and getting the answer from your own manuals, procedures and history, with a reference to the source.
- Incoming requests: classifying emails and tickets, suggesting a reply, routing them to the right person.
- Drafts: quotes, product descriptions, reports, built from real data and reviewed before sending.
- Assistants inside your software: in business software or a web app, to reach a piece of information without crossing five screens.
- Agents: multi-step sequences carried out autonomously within precise limits.
How we make it reliable
- Validated output: model answers must match a typed schema; anything that does not is discarded or regenerated, never passed on to the next system.
- Reversible actions: when an agent changes data, the operation can be undone; sensitive ones wait for human confirmation.
- Minimal permissions: each component sees and touches only what its task requires.
- Isolated environments for running code or tools.
- Observability: every request, answer and action is logged, with cost and timing, to understand what happened and to improve.
- Automated checks: a set of test cases repeated at every change, like the tests of traditional software.
Data and confidentiality
Before any code is written we decide which information the model can read, where it is processed and how long it is kept. Personal data is handled under the GDPR; data the task does not need is not sent. For the most sensitive cases we consider models running in a dedicated environment.
How we start
From the use case, not the technology. In the listening phase we identify the task where AI can deliver a measurable result; then we build a prototype on your real data and compare it with how that task is done today. Only if the numbers hold up do we move to a production version.
Cost and timing
Besides development there is a usage cost for the models, which depends on the volume of requests: we estimate it and keep it under control from the prototype onwards. Development cost depends on how many systems must be connected and on the level of autonomy required. You receive an estimate split into phases, starting from the prototype.
How we work
PHASE 01
Listen
We understand the problem before proposing a solution. Workshops, questions, zero pointless slides.
Together we pick a repetitive, measurable task and define which data the model may see.
PHASE 02
Design
Clickable prototypes in days, not months. You decide on something you can actually touch.
We design the flow: what the model does, what the system checks, where human confirmation is needed.
PHASE 03
Build
Short sprints, frequent releases. Watch the product grow week after week.
A prototype on your real data, compared with manual work; then validation, permissions and logging.
PHASE 04
Evolve
We don't vanish after launch. We measure, improve and stay by your side.
We keep quality, cost and edge cases under control, and update prompts and models when needed.
Frequently asked questions
Where should a company start with AI?
From a repetitive, frequent, well-bounded task where a mistake is easy to spot and fix: routing requests, extracting data from documents, searching past records. A small, measurable first case tells you far more than a large exploratory project.
Does our data stay confidential?
It is the first thing we design. We decide which data the model can see, where it is processed and what is retained. Depending on the case we use services with contractual guarantees that data is not used for training, or models running in an environment you control.
What happens if the model gets it wrong?
A language model is probabilistic: mistakes are possible, so the system has to be built with that in mind. Answers are validated against precise schemas, important actions require human confirmation or are reversible, and every step is logged so you can understand what happened.
What is an AI agent?
A system where the model does not just answer but takes actions: it reads a document, queries the management system, prepares a draft, opens a case. In complex scenarios several agents with separate roles work together, each with permissions limited to its own task.
Do we need to change the software we already use?
Usually not. AI is added to existing systems through their APIs: your management system, CRM or mailbox stay where they are and gain one more capability.
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