The model (LLM)
A brilliant colleague who doesn’t know your company yet: writes and answers like a well-prepared person, but is blank on your specifics. It has to be put to work on your content. By design, we don’t tie you to a single vendor.
AI · AI Systems & Custom Products
Enterprise AI systems and agents, built around your real problem
We don’t sell you technology — we take a problem off your plate. We design custom AI systems and agents that reason over your documents and data, do measurable work, and always stay under your control. We start with an assessment, not a demo.
The problem
The symptoms you already recognise
You’re probably here because something in your business no longer adds up. See if this sounds familiar:
- Knowledge lives in a few people’s heads and across hundreds of scattered files: when someone leaves, the right answer walks out with them.
- Your team burns hours on mechanical work copy-paste, routing requests, forms, reports that could almost run themselves.
- Customers write and call at night and on weekends and you know every slow reply is a missed opportunity.
- You’ve tried a chatbot before and it answered badly, made things up, embarrassed you. You’ve been wary ever since.
- You’ve seen dazzling demos that fell apart in the reality of your business.
- You worry about your data and the European rules (EU AI Act, GDPR) and don’t know where to start to get it right.
This page is for owners, directors and operations leaders — especially the non-technical ones — who have a concrete problem that costs time and money every day and want to tackle it with method, without becoming engineers.
Most AI projects don’t fail because of the technology. They fail because they’re treated as “let’s buy a piece of software and we’re done,” when in reality this is a change in how you work. That’s exactly the trap we keep you out of.
What it means
What it really means, no science fiction
Today “enterprise AI” no longer means “add a chatbot.” It means building systems that reason over your real documents and data and do measurable work, inside clear rules. Here are the building blocks, jargon-free.
The knowledge base
Your company’s written memory in one place: price lists, policies, FAQs, procedures, contracts. An asset that stays even when someone changes role. Key point: permissions apply to the AI too.
RAG
The open-book exam, so the AI doesn’t make things up: before answering it reads YOUR documents and answers only from what it finds there, showing the source. Answers grounded in your data, updatable and traceable.
The AI agent
It doesn’t just talk: it acts. A chatbot tells you how to do it; an agent does it for you — opens the ticket, books, fills forms, sends the email — and hands delicate cases to a person.
Automation / workflows
The conveyor belt for repetitive work: the task goes in one end and comes out finished the other, the same way every time, no copy-paste, no forgotten steps.
Governance
You stay in charge: on important decisions the AI asks a person first (human-in-the-loop), it doesn’t make things up, and your data stays protected server-side with an audit trail of every action.
AI doesn’t replace you. It takes the mechanical work off your hands and leaves you to decide what matters.
Sistema · Knowledge → Agent → Workflow
Conoscenza, agente, workflow: una regia sola.
La conoscenza alimenta l’agente, l’agente agisce con regole chiare e il workflow consegna il risultato: report, CRM, handoff a una persona dove serve. Un sistema, non una demo.
Conoscenza + regole
Azioni misurabili
Handoff a una persona
How we do it
A real path of verifiable milestones
We don’t ask you to jump in: we start small on a real problem and only scale when the numbers confirm it. Most of our energy goes into people, processes and data — not technology.
1
Assessment
Around 4–6 weeks. We start from your problem: data maturity, infrastructure, governance, organisational readiness — and the first use case that’s genuinely worth it.
2
Roadmap
We turn the analysis into a concrete plan: what comes first, what next, with milestones and shared success criteria.
3
Prototype
Narrow scope, a few weeks: to see — with your data — whether it actually works, before committing resources.
4
Production
The leap where most projects die: a process failure, not a technology one. That’s why we engineer it — integrations, security, access control, governance inside the system.
5
Training
Your team learns to work with the system. Change management starts on day 1.
6
Adoption
We support real day-to-day use: a tool nobody uses is worth nothing.
7
Measurement
We measure business outcomes — handling times, first-contact resolution, costs — not demo metrics.
8
Improvement
We keep refining on real data: a living system, not a project delivered and forgotten.
Typical timelines (indicative): focused prototype 4–8 weeks; prototype to production in one department 3–6 months; broad adoption 12–18 months.
Use cases
What you can actually have the systems do
All examples are anonymised — problem → method → result — for discipline and confidentiality.
Customer care and guidance
An agent answers repetitive requests — including nights and weekends — reading from the up-to-date knowledge base, and hands delicate cases to a person with full context.
Anonymised example (services): Problem — team swamped with repetitive requests, long after-hours response times. Method — governed knowledge base + RAG-grounded agent + human-in-the-loop escalation. Result — standard requests clear themselves; staff focus on high-value cases.
Sales support
An assistant prepares commercial answers grounded in real price lists and terms and supports the sales team with a single verified source (single source of truth).
Operations and back-office
Where the value is most solid: automating repetitive processes, reconciliations, onboarding, case routing. Less copy-paste, fewer errors.
Reporting assistant
Prepares recurring reports and summaries pulling from the right data, so reporting stops eating up Monday mornings.
Voice customer care
A voice agent replaces the old rigid phone menu: understands the request, searches internal sources, resolves it or hands off to an operator with transcript and context.
Internal knowledge
The same engine serves your employees: they find the right procedure in seconds instead of asking three colleagues.
Why us
Why an independent, not a platform or a consultancy
The market offers two alternatives: self-serve automation tools, and the large proprietary systems of groups and consultancies. We are a third thing, built on structural facts.
A delivered operation, not a kit to assemble
Self-serve “DIY” tools push design, maintenance and governance onto you: they become one more project, not one less. We deliver a governed, working operation — with integrations, security, access and human-in-the-loop built into the system.
No proprietary “operating system” to tie you in
The large consultancies and groups hook you into their AI operating system: the value lives in their platform and you can’t take it with you. We have no OS to push — we choose the technology around your problem, not around a commercial deal.
You own data, models and workflows
A neutral architecture with server-side adapters: the underlying engine can be swapped without rewriting your business. No lock-in, an exit right per project — the opposite of platform dependency.
Vokira: proof that runs, not a slide
We distribute Vokira, an AI-native operating layer already in production: proof that the automation we design is a real, working system. One accountable senior team, no juniorization, no endless hand-offs.
The specialist layer
When automation touches media and data: the Adtelier layer
Independent doesn’t mean isolated. When an automated process needs to plug into advertising, campaign data or programmatic, we activate the Adtelier capability on demand — under our direction.
Depth switches on when needed
You always talk to us: we keep the relationship, the direction and the governance. If the automation has to touch ad-tech, data or programmatic, we activate the Adtelier specialist layer for that part and switch it off when it isn’t needed. Not a second opaque intermediary: a reserve of vertical specialism.
An agent that feeds distribution
The same RAG/agent systems we build can feed specialist media activation — audience, measurement, retail media — executed by the Adtelier layer under our direction, with the same transparency as the rest of the work.
FAQ
The questions we always get
What’s the difference between a chatbot and an enterprise AI agent?
A chatbot tells you how to do something: it answers questions. An agent does the work for you — opens the ticket, books, fills forms, sends the email, prepares the report — and hands delicate cases to a person. A serious agent also reads from your up-to-date knowledge base, so it doesn’t answer out of thin air like old chatbots.
What is RAG and how does it stop the AI from making things up?
RAG (Retrieval-Augmented Generation) is the technique where, before answering, the AI reads your real documents and answers only from what it finds there, showing you the source. It’s the difference between a closed-book and an open-book exam: it grounds answers in your data and makes them traceable. For delicate cases, a person is always in the loop.
Does my data stay private? Can the AI see documents an employee shouldn’t?
Your data stays protected, handled server-side, with rules on who sees what. The principle is simple: permissions apply to the AI too. If an employee can’t see a document, the AI won’t even surface it. Access keys never live in the frontend, and sensitive data can stay within private boundaries, in line with GDPR.
How much does an AI project cost and how soon will I see results?
No blind quote: the price comes after the assessment, because it depends on the problem we’re solving — like a tailor taking measurements before quoting. We start small with a prototype: typically 4–8 weeks for the prototype, 3–6 months for production in one department. At each stage you see the numbers before committing further.
Does my company need to worry about the EU AI Act and GDPR?
Yes, and that’s why we design compliance into the system from the start. The EU AI Act introduces, in phases, obligations on transparency, decision traceability and human oversight, with extra-territorial reach like GDPR and penalties that, for the most serious breaches, can reach up to 7% of global turnover. The deadlines are evolving, so we don’t sell you a date: we build transparency, traceability, human oversight and consent management into the delivery (including, where required, DPIA and FRIA).
Do I have to replace people, or does the AI work alongside them? And am I risking vendor lock-in?
AI takes the mechanical work off the table, not the people: your team gets back to selling, relationships and decisions. On lock-in we work with a neutral architecture and server-side adapters, so the underlying engine can be swapped without rewriting your business. Your data and your workflows stay yours.
We start from your problem, not from a demo
The first step isn’t a contract — it’s a conversation. Tell us where you lose time and money every day, and the assessment will tell you — with method — where the real value is and which use case to start from. It’s a commitment to method, not a promise of numbers.
Just your name and work email. After the click, someone from the team gets back to you.