How Antutive builds
AI-first products
AI-first is a way of designing products, not a feature list: the intelligence is the interface and the mechanism, with a person confirming every action. This page describes how Antutive builds. The principles apply to every product we make, and Famant, our first flagship product, is the working example throughout.
Famant is ready for beta testing on iOS and Android. Everything here describes how it works today, and we update this page as the architecture is confirmed.
The intelligence loop
Your context
Calendars, documents and requests: the user's own world
Understanding
The model reads what things mean
Reasoning
What clashes, what matters, what's next
Proposal
A concrete suggested action
You confirm
Nothing happens until a person says so
Mechanism, not magic.
Four principles govern every capability every Antutive product ships. They are commitments about how we build, not claims about what is already live.
The intelligence is the product
An Antutive product is model-driven behaviour, not a rule engine with an AI sticker. Take the AI out of Famant, for example, and there is no product left, only another shared calendar and list app. That dependency is the test every product must pass.
Confirmation before action
Our products read, reason and propose, and a person confirms before anything changes. A wrong proposal costs one tap to correct or dismiss; because nothing happens before confirmation, a misreading never becomes a real-world mistake.
Grounded in the user's own context
A proposal is only useful if it understands this user's world. Our products work from the user's own data, and only theirs. In Famant that means the household's calendars, people, documents and history, kept strictly to that household.
Evaluation over adjectives
Capabilities are judged against fixed test scenarios before they move from development to testing to release. We publish accuracy or quality figures only when we have measured them, never before.
What the model has to do, per capability
Every AI sentence on this site has to describe a mechanism a technical reviewer could interrogate. Here is Famant, the first product built this way, capability by capability, in those terms.
| Capability | Mechanism | |
|---|---|---|
| Family scheduling & events | Personal and family calendars live in one shared view. The assistant reasons over the whole household's commitments, spots what overlaps and proposes a resolution. | |
| Tasks & chore delegation | A request in plain language, typed or spoken, becomes a task with an owner and a reminder. Chores are assigned across the family, and the assistant acts only when a member confirms. | |
| Shared lists & reminders | Groceries, errands and everything in between live on collaborative lists any family member can update, with reminders timed to when they matter. | |
| Document understanding | School forms, invitations and receipts are read with OCR and natural-language understanding, turned into proposed actions — an event, a reminder, a list entry — and stored so they can be found again by asking. | |
| Expenses & budgeting | Household expenses and shared financial activity are tracked in one place, so the family sees where money goes without a spreadsheet ritual. | |
| Meal & household planning | Weekly meal plans grounded in the household's own preferences and history, with grocery lists generated straight from the plan. | |
The full architecture,
published when it's confirmed.
Antutive's products run on managed cloud infrastructure, with modern foundation models providing the reasoning and understanding layer. We are confirming which details of Famant's architecture to publish here: which models power which capability, how the assistant is orchestrated and grounded on the user's own data, how quality is evaluated, and where data lives.
We publish infrastructure and model choices here once they are confirmed: concretely, and only what is real. No name-dropping.
Product experience
What the user sees: proposals, confirmations, one shared view
Assistant orchestration
Turning understanding into proposed actions, tool by tool
Foundation models
The reasoning and understanding layer, on a managed cloud platform
The user's own data
Grounding: each household's context, kept strictly to that household
Design intent: published concretely, with names, once the plan is confirmed.
What we will and won't claim
Products that touch people's real lives have to earn trust the slow way. Our rule is simple: this website describes mechanisms and statuses, not adjectives, and a claim appears only when the evidence behind it exists.
"We'll publish results when we've measured them."
Will claim
Capability mechanisms, described as they work today
Design principles we build to
Verifiable company facts
Won't claim
Accuracy or benchmark figures we haven't measured
Integrations that aren't built
Autonomy the product doesn't have
Compliance badges without reviewed policies behind them
See what all of this is for
The approach only matters in the products it produces. Explore the portfolio, or go straight to Famant, the first product built this way.