AI Products · Owned systems · Vokira
Owned systems built around a real decision — plus Vokira Web, the product distributed by TMM that turns a conversation into a governable brief. No generic software, no unconfirmed promises. We start from your problem, not the tool.
You have a website, you run campaigns, you hold the data — yet the work stays manual and scattered. Recognise at least one of these?
Who it's for: companies and brands that already have media, data and customer relationships and want to turn a process into a product — with an owned system built around a decision, or with Vokira Web ready to go live. In Italy and Milan, in Switzerland, across the European Union and worldwide. Whether you start from an idea or already have a workflow to make repeatable.
An AI product isn't "buying software" — it's building a tool around a decision that repeats. Technology comes after method, data and responsibility. Translated into everyday images:
Off-the-shelf software is a one-size-fits-all jacket: you fit in it, but it doesn't fit you. A tailored product starts from a precise problem — orienting a customer, reading documents, collecting a brief, preparing a report — and takes the shape of that work. Technology is the last step, not the first.
Vokira is a product in its own right, distributed by TMM, not TMM itself. It's an interface that turns a conversation into a governable brief: it captures the request, draws on the knowledge base, understands the real need and hands off to the team. Think of it as a sharp front office that organises what comes in, instead of leaving you to rewrite it by hand.
A knowledge base is a tidy library of what you know: documents, policies, answers, rules. RAG is the librarian who, before answering, fetches the right book instead of going from memory. So the tool answers on what you decided, not on what it imagines.
An agent has a role, boundaries and an exit. It knows what it can do, knows when to stop, and knows when to hand over to a person (human handoff). It's the difference between a colleague with a job description and a megaphone that says anything.
A voice module that calls back only when the user asks (opt-in), with a sober script, recorded consent and escalation to a person. Not a call centre that chases: a requested callback, traceable and respectful.
Reading contracts, procedures and documents as a review system, not as a pile of files. The audit trail is the log that says who did what, when and on which version — the difference between "trust us" and "we can prove it".
The value isn't in having AI — it's in governing it inside a measurable process.
System · Product architecture
One control room to set the agent's role, connect the knowledge base, fix boundaries and consent, and measure real usage. You set the rules before the tool ever reaches your customers.
We start from the decision to change, not the tool. We map audience, process, available data and responsibilities. No price before we understand it: without the problem, any figure is a number out of thin air.
We check which sources, rules and signals can feed the system, and what state they're in. We remove redundancy with tools you already have instead of layering new ones — so you don't pay twice for overlapping functions.
We build the minimum interface, the flow, the outputs and the limits to validate. A prototype is for seeing value early, not for impressing: better to find what doesn't work on a screen than on an invoice.
We define privacy, consent, roles, logging, human escalation and prohibited content. The tool goes public only within a clear perimeter: where yes, where no, who answers when a person is needed.
We connect CRM, email, webhooks, CMS, files and internal systems, with no keys in the frontend. The product enters your existing flow instead of creating a parallel one to babysit.
We add usage and quality metrics, gather feedback, optimise and maintain. A product lives: without measurement you can't tell if it's an asset that works or a cost that sleeps.
Problem: every request arrived messy and someone rewrote it by hand. Method: Vokira Web as a front office, connected knowledge base, summary and handoff to the team. Result: already-qualified, readable requests, less time spent translating them.
Problem: rich documents and policies, never really consulted. Method: a queryable knowledge product with RAG, response rules and traced sources. Result: a usable knowledge base that answers on what you decided.
Problem: contracts and procedures spread across files, reviews and follow-ups. Method: a document workflow intelligence line with roles, states, limits and audit trail. Result: less friction, traceable responsibility on every version.
Problem: unsolicited calls, felt as intrusive. Method: opt-in voice/call advisor, sober script, recorded consent and human escalation. Result: callbacks only when the user asks, traceable and respectful.
Problem: licences for software that almost did the job. Method: a tailored product on the real use case, integrated into the existing flow. Result: a tool that does the whole job, with no paid-for, never-used functions.
Problem: tools adopted without knowing if or how they were used. Method: usage and quality metrics, feedback and evolutionary iteration. Result: an honest read of what works, to decide where to invest next.
With an AI product, the real question isn't "who has the AI" — it's who owns the system at the end. The difference between building something that is yours and renting access to something that stays someone else's is structural here, not a matter of style.
Model, data, code, IP and knowledge base stay with the client, on portable standards and open tracing: the right to leave is written, not promised. It's the opposite of a group platform, an agent app-store or an AI-native managed service, where the agent, the accumulated data and the logic stay with the vendor and you rent access. Change partner, and you take the system with you.
Proprietary platforms and shared experiment libraries improve the vendor with every client they serve: their edge grows, yours doesn't. With us, every iteration makes your product more capable, inside your perimeter. There's no "collective intelligence" you feed for free and can't take away.
No consulting cadence — discovery, design, build, test, handover — where every requirements change becomes a new quote and the senior names in the pitch aren't the people who deliver. One senior team accountable end-to-end, no juniorisation, no endless handoffs. And no owned media, no arbitrage, no data asset to monetise: no conflict of interest baked into the product.
Vokira is a product in its own right, distributed by TMM, already live: an AI-native layer that turns a conversation into a governable brief. It's not a percentage of "AI-powered" revenue in a press release, it's a system that works. The proof that we can take AI to production — with pre-release evals, anti prompt-injection guardrails, sourced retrieval and an audit trail — we show it, we don't just claim it.
You buy AI to have it; you govern it to make it yours. Only the second stays an asset when you change vendor.
Sometimes a tailored AI product has to touch the ad-tech, data and programmatic world — an agent that draws on audience signals, a measurement layer, a product that orchestrates creative and media in a data-driven way. In those cases TMM activates specialist depth on demand, staying the single accountable senior point of contact.
When the brief calls for programmatic execution, data and identity, research, advanced omnichannel or media bartering, TMM activates the Adtelier specialist layer under its own direction. The client still talks to one accountable senior team: vertical depth arrives when the problem needs it, not sold regardless. It's the specialist scale of the big players without their layered model.
Adtelier is an on-demand capability, not a proprietary platform or a data asset to monetise. That's why the client keeps owning the system, the data and the IP: no platform lock-in, no principal-media or arbitrage conflict of interest that weighs on the big networks. The specialist works inside your confirmed perimeter, under the same governance and transparency rules as the rest of the product — not inside its own.
Is Vokira a TMM product or is it TMM itself?
Vokira is a product in its own right, distributed by TMM, not the agency. TMM handles its distribution and integration into your context — knowledge base, boundaries, consent, handoff — but Vokira stays an independent platform. We say it plainly because the distinction matters: choose the product for what it does, not for who brings it to you.
When does a tailored product make sense instead of off-the-shelf software?
When off-the-shelf software forces you to bend the process to the tool instead of the other way round, and when the work you want to automate is a decision that repeats. If a website or a campaign is enough, we build nothing: the tailored tool only makes sense when the value of making it yours outweighs the cost of maintaining it.
What is a knowledge base with RAG, in plain terms?
It's a tidy library of what you know — documents, policies, answers — with a librarian (RAG) that fetches the right source before answering instead of going from memory. It works well only if the base is curated: stale or contradictory content gives stale or contradictory answers. Curating the knowledge comes before the model.
How do you handle voice and calls without being intrusive?
Opt-in only: the voice module calls back when the user asks, with a sober script, recorded consent and escalation to a person when needed. It's not a call centre that chases, it's a requested, traceable callback. Privacy compliance and consent aren't an add-on: they're part of the design from the start.
Do my data and integrations stay safe?
Yes: connections to CRM, email, webhooks, CMS and files go through an integration layer, with no keys exposed in the frontend, and with roles, logging and prohibited content defined before publication. We publicly describe only what sits within a confirmed perimeter: no availability, partnership or integration claimed without operational confirmation.
How do you measure whether the product actually works?
With usage and quality metrics decided together: who uses it, on what, with what outcome and where it jams. A prototype is for seeing value early; metrics are for confirming it over time. Without measurement we couldn't tell whether we built an asset that works or a cost that sleeps — and we'll tell you honestly.
Tell us which decision repeats and where the work stays manual — we'll tell you whether you need a tailored product, whether Vokira Web is already the answer, or whether a website is enough, before any quote. The price comes after the brief, never before.