Enterprise LLMs.
Configuration of models, instructions, roles, policies and response paths for internal or customer-facing use. The model learns your boundaries, not just your tone.
AI Solutions · LLM · RAG · Agents
AI Solutions: LLM, RAG and agents built on your documents and data
AI systems that answer on what the company actually knows — not on guesswork. Knowledge first, rules first, then interfaces, agents and automation. With governance, source citation and a handoff to a person when it matters.
The problem
The problem / who it's for
You want to bring AI into your processes, but something holds you back. Recognise at least one of these?
- You tried an AI assistant, but it makes answers up. When it doesn't know, it doesn't stop — it improvises, and you can't trust it.
- Your information is scattered across documents, emails and folders. The knowledge exists, but no tool knows where to look.
- You don't know where the data you feed the AI ends up , who sees it, or whether it stays yours.
- The AI answers, but doesn't cite where the answer came from. Without a source, every line has to be checked by hand.
- You worry the assistant will tell a customer something it shouldn't. No one defined what it can say, what it can't, and when to stay silent.
- When the case is sensitive, the AI carries on alone instead of handing the request to a person.
- You bought AI tools that overlap and suspect you're paying twice for similar functions.
- You don't know where to start — everyone talks about AI, no one tells you which first use case is worth it.
Who it's for: companies and teams that want to use AI inside real processes — customer care, sales, operations, reporting and media — with reliable knowledge, clear rules and an orderly path from question to answer, from answer to action. In Italy and Milan, in Switzerland, across the European Union and worldwide. Whether you start from scratch or already tried something that didn't work.
What it means
What it means (plain)
AI Solutions isn't a magic product — it's an implementation layer built on top of what the company knows. It means connecting a language model to your documents, on your rules, and verifying every answer. Translated into everyday images:
The language model (LLM).
Think of a colleague who is broadly well-read but doesn't know your company. On its own it answers plausibly, not necessarily truly. The value isn't in the model: it's in what you let it read and the rules you give it.
The knowledge base.
It's your company's tidy address book: documents, FAQs, procedures, sales material and project data, collected in one queryable place. Without a tidy base, the AI gropes around; with a tidy base, it knows exactly where to look.
RAG: retrieval before the answer.
RAG means that, before answering, the system goes and finds the right passage in your documents and answers only on that, citing the source. It's the difference between a colleague answering from memory and one who opens the manual, finds the page and shows it to you.
Source citation.
Every answer carries the document it came from. So you can verify in a second instead of trusting blindly, and unverifiable answers go down.
Agents.
An agent is an assistant with a task and boundaries: it qualifies a request, guides a brief, prepares a summary, triggers a workflow. It knows what it can and can't do — it doesn't improvise outside its perimeter.
Governance and human handoff.
Governance is the rulebook you set: what the AI can say, what it can't, when to ask for consent, when to stop and when to hand the work to a person. Human handoff is the golden rule: in sensitive cases, the AI prepares the context and gives up the turn.
In one line
The value isn't in owning the AI — it's in building it on what you know and governing it.
System · Knowledge base
The knowledge base that feeds the answers
One control room to order the sources, retrieve the right passage, apply the rules and decide when the agent answers and when it hands over to a person. You set the rules before the AI ever faces a customer.
Sources & order
Retrieval (RAG)
Governance & handoff
Operating perimeter
AI systems designed to work inside the company
No standard packages: every solution starts from use case, available knowledge, integrations, risks and expected operational value.
Knowledge base.
Organisation of documents, FAQs, content, procedures, sales material and project data into queryable knowledge bases that stay maintainable over time.
RAG architecture.
Controlled information retrieval, internal source grounding and reduction of unverifiable answers. The AI answers on what exists, not on what it imagines.
AI agents.
Agents that qualify requests, guide briefs, answer, prepare summaries, trigger workflows and hand off to the human team with the context already prepared.
Automation.
Workflows for intake, classification, follow-up, reporting, content updates, operating tasks and notifications — to cut repeatable manual steps.
Customer care & sales.
Assistants that orient customers, solve recurring requests and prepare escalations with context; support for lead qualification, call preparation and follow-up.
How we do it
How we do it (steps)
1
Use case & assessment.
We start from the problem, not the tool. We define the use case, the expected operational value, the users, the risk level and the integration needs. No price before we understand it: without the problem, any figure is a number out of thin air.
2
Knowledge & data.
We check what you actually have. We collect, clean and structure the sources that are truly usable — documents, FAQs, procedures — and we remove redundancy instead of adding overlapping tools.
3
Rules & governance.
We define boundaries, privacy, tone, escalation rules, consent and prohibited content. We decide when the AI answers, when it stops and when it hands the request to a person — before it goes live.
4
Agent & RAG.
We build the interface, the questions, the session memory, the allowed actions and the controlled retrieval from documents with source citation. The agent answers on what exists, not on what it makes up.
5
Workflow integration.
We connect the system to real processes: email, CRM, reports, CMS or internal tools. The AI doesn't stay an isolated demo: it enters where the work actually happens, in line with European privacy compliance.
6
Measurement & improvement.
We measure adoption, answer quality and escalation cases; we collect feedback and improve in a controlled way. Adoption is measured, not declared: it grows where it brings value, not for fashion.
Capabilities
Capabilities / use cases (anonymous: problem → method → result)
An assistant that makes things up.
Problem: an AI assistant answered plausibly but unverifiably. Method: a tidy knowledge base, RAG architecture with source citation, response boundaries. Result: answers anchored to real documents and fewer claims to check by hand.
Scattered knowledge.
Problem: information spread across documents, emails and folders, never queryable. Method: collection, cleaning and structuring of sources into a maintainable knowledge base. Result: a single queryable source, reduced search time.
Customer care under pressure.
Problem: recurring requests saturating the team. Method: an assistant that orients customers and solves repeatable cases, with human escalation and context already prepared. Result: simple cases handled by the AI, sensitive ones passed to a person with full context.
Privacy with no guard.
Problem: doubts about where the data fed to the AI ended up. Method: consent governance, response boundaries and privacy-respecting logging, with no keys exposed on the client side. Result: compliant, traceable AI use with clear responsibilities.
Reporting no one reads.
Problem: data, notes and reports no one had time to read. Method: an assistant that turns reports and notes into operational readings, next questions and traceable summaries. Result: faster decisions on a single read, not ten different files.
Paralysed start.
Problem: too many vendors, no clear first use case. Method: readiness audit, mapping of processes and priorities, choice of the first value use case. Result: a concrete prototype instead of an endless AI project on paper.
Independent, not a network
Why an independent, not a large group platform
With an AI system, what matters isn't only what it answers today — it's who owns it tomorrow. The structural difference of an independent is that the value built on your knowledge stays yours, instead of accumulating inside someone else's platform.
A system you own, not a platform you access.
The large consultancies and the big networks sell access to a group platform: you use it, but the data, models, configurations and operating memory stay inside their perimeter. Here it's the opposite — the knowledge base, the rules, the models and the system's IP are yours. The technical layer is neutral and replaceable: a server-side adapter, a default trial mode, the ability to change the underlying model without redoing the project.
No lock-in: the number-one AI risk of 2026.
The danger that worries enterprise leaders most today is dependency — orchestration, data gravity and behavioural memory that trap institutional memory inside a vendor's platform. We design for the right to leave: export in open standards, portability per project, no proprietary graph to rent. Change partner and you take your data, rules and method with you.
Bespoke, not prebuilt.
Libraries of hundreds or thousands of prebuilt agents start from a template and bend your problem to fit the tool. We do the reverse: we start from your knowledge and your real use cases. No standard packages — every system starts from use case, available data, integrations, risk and expected operational value, with one accountable senior team, no juniorisation and no handoff chains.
No conflict over the data, and proof that runs.
We own no media and no identity data to resell, and we don't do arbitrage: we have no incentive to make you dependent on a platform of ours. The loyalty is to your result and to data control. And the AI operating layer isn't slide-deck theory: we run it in production in our own product, Vokira — working agents, RAG, governance and human handoff — as concrete proof, not a percentage in a press release.
The value built on your knowledge should stay yours: a system you own, not a platform you can't leave.
The specialist layer
When the AI system meets media: the Adtelier specialist layer
The RAG systems and agents we build on the company's knowledge don't stay confined to customer care or internal use: they can also feed the media chain. When a use case touches advertising activation, TMM activates the Adtelier specialist layer on demand, keeping a single accountable point of direction.
From internal agents to media activation, without changing direction.
The same agents that qualify and enrich intake, prepare briefs and audiences and generate traceable reporting can become the starting point of a campaign. When the brief calls for depth or scale — media-neutral buying, programmatic and DSP, data and AdTech, MMM measurement, media barter, international reach — TMM brings in Adtelier's specialist capability. You keep talking to one senior team: TMM holds the relationship and the orchestration, the specialist supplies depth only when the brief needs it.
Same principle, from the AI system to media: your data, the path in the clear.
The specialist layer adds no second opaque intermediary. The same pact as the AI system holds: data and IP stay the client's, the path and the fees are transparent, no arbitrage. Specialisation is triggered by the problem, not sold by default — the same 'from the problem, not the tool' logic that governs the whole page, extended from the internal system to external media.
The opposite of the holding-company conflict.
In the big-group model the AI platform and the media buying are the same conflict: the structure that advises you is the one with its own inventory to fill. Here the two planes stay distinct and loyal to your result: the AI is a system you own that defines strategy, audience and offer; the media specialist is a capability switched on when needed, under TMM direction, not a channel to saturate. Define, activate, measure — one intelligent pipeline, without the structural conflict of the giants.
FAQ
FAQ
What is RAG, in plain terms?
RAG (retrieval-augmented generation) means that, before answering, the system searches your documents for the relevant passage and generates the answer only on that, citing the source. It's the difference between a colleague answering from memory and one who opens the manual, finds the right page and shows it to you. It exists to cut invented answers and to make every claim verifiable.
How do you stop the AI from "making up" answers?
With three combined safeguards: a tidy knowledge base, controlled retrieval (RAG) that anchors the answer to real documents, and response boundaries that tell the AI what it doesn't know and when to stop. When the information isn't there, the system says so or hands the request to a person, instead of improvising. Source verification isn't an option: it's part of the design.
Does my data stay mine? Where does it end up?
Yes. The principle is that your data stays yours and its use stays traceable. Access keys never live in the browser, the integration goes through a server-side adapter, and the default trial mode avoids moving real data until it's necessary. Consent governance, response boundaries and privacy-respecting logging are part of the design, not an add-on.
When does a person step in instead of the AI?
When the case is sensitive, ambiguous or outside the allowed perimeter. We define escalation rules upfront: the agent recognises the boundary, prepares the conversation context and hands the turn to a person, who picks up without making the customer start over. Human handoff is a designed rule, not a fallback.
Where should I start? Do I have to redo everything?
No. You start from a first value use case, chosen after a readiness audit: often an audit, a prototype, an internal assistant, a workflow or a customer interface with human handoff. Readiness exists precisely for this — to understand what you already have, what you really need and what's redundant, so you don't pay for overlapping tools and don't launch an endless AI project.
What technology do you build on?
The choice of the technical layer comes after the use case, never before. The integration is designed to stay neutral and replaceable: a server-side adapter, a default trial mode and the ability to change the underlying layer without redoing the project. The loyalty is to your result and to data control, not to a platform.
Let's talk about your problem, not the tool.
Tell us which process you want to lighten and what knowledge you already have — we'll tell you what to order, what to govern and which first use case is worth it, before any quote. The price comes after the assessment, never before.