AI-generated image: An empty meeting room after a session: a long oak table, chairs at angles, a grid of blank cards on the concrete wall and city towers through a wide window.

Start small. Prove it. Then grow.

You don't need to know what to build. We find where AI agents would help, try them on real work, and give them more to do as they earn it.

AI-generated image

Blueprint. Prove. Scale. Embed.

StepWhat you getWhat must be true before the next step
Blueprint.A short, paid first step where we map where agents would help and agree what success would look like.You've agreed which work comes first, what success looks like, and how it will be measured.
Prove.We build for one piece of real work and show you it working.The agreed measure is tracked on real work, and you've seen what it shows.
Scale.More work, more teams, more of the business connected.The first workflow runs inside your operations under written rules, with its level of oversight agreed.
Embed.The aim is that it becomes part of how your business runs, and we keep running it with you.—

Four things we do, under one roof.

An AI agent is AI that does a piece of work for you. Getting one to work inside a real business takes more than the AI.

Understand the work.

We map how your business really runs: who does what, where information lives and where time goes.

Design it for people.

We design the screens and each agent's job around how your team works, so the platform fits the way they already work, at a desk or on a phone.

Build it into your business.

One AI platform with each agent given one job and clear rules, connected to the systems you use and put to work in your daily operations.

Run it with you.

We stay on after launch to keep it running and improve it as your business changes.

Freedom is earned.

Think of a new hire on probation. In week one you sign off everything; later you check the things that matter most.

Autonomy, earned.

  1. Level 1

    A person approves.

    The agent prepares the work, and a person approves every output before it takes effect. Every new workflow starts here.

  2. Level 2

    A person handles the exceptions.

    The agent acts on its own. Anything that breaks a rule or falls below a confidence threshold goes to a person, and a fixed sample of everything else is checked.

  3. Level 3

    Monitored.

    No single output is reviewed. Oversight comes from a monthly review, alerts on errors and drift, and tracking against the value agreed for the project.

  4. Always

    Some decisions never leave a person.

    Commitments that change scope, price or risk; financial changes above agreed limits; legal or regulatory exposure; anything that can't be undone.

  5. After a mistake

    Oversight steps back up.

    A serious error moves a workflow up a level straight away. It steps down again only by meeting the thresholds and being co-signed.

    Read “Autonomy, earned” →

Our method

What has to be true before oversight steps down.

We publish our thresholds because a buyer should be able to test them. These are our starting values. The method is new, and we'll tune them as real run data builds up.

All of these must be true before a workflow steps down a level.
Level 1 to level 2Level 2 to level 3
At least 4 weeks and 200 reviewed runs at level 1.At least 8 weeks at level 2.
Fewer than 5% of outputs changed or rejected by a person.Exceptions on fewer than 2% of runs.
No serious errors.Sampled checks at least 98% correct.
Written exception rules, tested against past runs.Value on track: at least 100% of what was expected at that point in the project.
At least 70% of planned use actually happening.Drift alerts and a way to roll back, both live and tested.

You co-sign every step down.

Each move down a level changes the risk your business carries, so it isn't ours to decide alone. Our delivery lead and your process owner both sign it. For our first workflows at level 3, our CEO signs as well.

Oversight steps back up automatically.

A workflow moves up one level straight away, and the move is reviewed afterwards, in any of three cases:

  • there is a serious error;
  • corrections or exceptions run above their threshold for two weeks;
  • the value delivered falls far enough behind the agreed plan to trigger a recovery review.

You're told within one business day. It steps down again only by meeting the thresholds and being co-signed, as before.

Some decisions never leave a person.

No level of evidence moves these to an agent:

  • commitments that change scope, price or risk;
  • financial changes above agreed limits;
  • legal or regulatory exposure;
  • anything that can't be undone.

An agent may prepare them; a person decides them.

Questions to ask before your first agent.

Which work takes up most of your team's week?

Where does information get typed in twice?

What must always have a person's yes?

How would you know it was working?

Start with a Blueprint.

Start an Agentic conversation →

We are what we sell.

We run Futuvara on our own AI platform. Every face below is one of our AI agents.

Scroll the rows sideways to see every agent.

  • AI agent

    Kat

    Chief Operating Officer: coordinates the business units

    Reads each business unit's reports against the company's goals, finds where one unit is waiting on another, and sends each fix to its owner.

  • AI agent

    Tom

    Chief Growth Officer: brand, marketing and sales

    Runs Growth as one system, from brand to signed customer, against approved targets. Finds where the sales funnel leaks and sends each fix to whoever owns it.

  • AI agent

    Lin

    Chief Delivery Officer: project health and risk

    Runs Delivery as one portfolio. Reads every project's status beside demand and budget, finds where the portfolio drifts, and sends each correction to its owner.

  • AI agent

    Tamara

    Financial Officer: reports on the company's finances

    Reconciles the ledger with the bank, produces the statements and cash report, and flags every variance the controls name. It reports; a person decides.

  • AI agent

    Anja Johansen

    Management Officer: runs planning and performance reviews

    Drafts the company's performance reviews and quarterly plans, so leadership decides from evidence. Every figure traces to a record.

  • AI agent

    Freya Lindqvist

    Leads brand and creative direction

    Directs Futuvara's brand, look, pictures and motion, and checks every piece a buyer will see before a person decides.

  • AI agent

    Amara Okafor

    Defines target customers and positioning

    Recommends who Futuvara sells to and what it says: the ideal customer profile, segments, target accounts and messages.

  • AI agent

    Lucas Brennan

    Writes article and web page copy

    Writes Futuvara's articles and web pages, briefs the Website agent to build them, and keeps the "Start an Agentic conversation" route working.

  • AI agent

    Dawit Haile

    Moves deals from lead to close

    Moves deals from first interest to a win Delivery can start on, and keeps every deal record true. A person approves anything a prospect sees.

  • AI agent

    Tarek Hassan

    Runs demand generation and campaigns

    Shapes demand campaigns and their assets, and recommends what to do with them, so Growth earns leads worth a sales call.

  • AI agent

    Kwame Asante

    Marketing operations and measurement

    Sets up how Growth measures itself: tracking, customer-record categories, lead routing standards and baselines, each with a source and an owner.

  • AI agent

    Petra Svobodová

    Drafts the public website for approval

    Builds Futuvara's public website: pages, code, pictures and motion. It writes drafts; a person decides what goes live.

  • AI agent

    Maya

    Plans screens and how they work

    Plans the screens of a project, from menus and views to an app or a web page, with four specialist agents. Delivers only after a person says yes.

  • AI agent

    Nadia Haddad

    Designs menus, labels and site structure

    Plans the structure before any screen is drawn: sitemap, menus, labels and content model, with every item in the one place users would guess.

  • AI agent

    Kenji Watanabe

    Chooses page layouts, grids and spacing

    Chooses the layout for each view: the paradigm, the page skeleton, and the grid and spacing where a project has none.

  • AI agent

    Lucía Romero

    Checks what people see first on a screen

    Critiques a drafted view's visual hierarchy: what is seen first, where the eye goes next, and what carries too much weight. Every finding comes with its fix.

  • AI agent

    Samuel Lindqvist

    Writes specs for new components

    Specifies components a project does not have yet: anatomy, variants, every state, behaviour and accessibility. First it checks nothing existing does the job.

  • AI agent

    Ludwig

    Chairs meetings and records decisions

    Chairs rooms and calls where specialist agents and a person work together. Picks who speaks next, records what is settled, and names what happens now.

  • AI agent

    Jackson

    Business Architect: turns goals into workflows

    Helps a business unit's officer turn an approved mandate into workflows, schedules, skills, agents and templates that run, each traced to the measure it moves.

  • AI agent

    Deniz Demir

    Solutions Architect: turns client goals into designs

    Turns a partner's business problem into a solution design a project manager can start on the next day, naming the assumption most likely to break it.

  • AI agent

    Diego Hernandez

    Delivery Project Manager: runs the schedule

    Compares each Delivery project's plan with what happened, logs the gap and raises it to whoever can act, so a slip is seen in week one.

  • AI agent

    Mariana Quispe

    Frames and refines early ideas

    Turns a raw partner opportunity into an Opportunity Brief: the problem in the partner's words, the value it could unlock and the idea to test first.

  • AI agent

    Mateo Silva

    Shapes early ideas into delivery direction

    Turns a framed opportunity into a platform direction: what it is for, the first use-cases that show it working, and why it is worth building.

  • AI agent

    Rafael Oliveira

    Shapes agreements and maps milestones

    Checks that what Delivery will build and what the partner will sign describe the same thing, and flags billing implications for Finance.

  • AI agent

    Sofia Marchetti

    Shapes requirements and records design decisions

    Turns a platform direction and its requirements into a design that can be locked: scope boundaries, testable acceptance criteria, milestones and recorded decisions.

  • AI agent

    Njeri Mwangi

    Turns build packs into draft backlog items

    Turns approved requirements and build packs into backlog items a developer can finish in one sprint. Its items are drafts until someone pushes them.

  • AI agent

    Robin Lindqvist

    Tracks sign-offs and billing readiness

    Turns a finished, signed-off milestone into a billing package Finance can act on: what was delivered, who signed and when, and what is still open.

  • AI agent

    Dawit Bekele

    Checks a build is ready to release

    Checks that what Delivery built does what the partner agreed, criterion by criterion against its evidence, before anyone signs, ships or bills.

  • AI agent

    Zofia Kowalski

    Writes contact reports from client meetings

    Turns the record of a client meeting or call into a draft contact report: requirements, decisions, actions, risks and open questions, each tied to a quote.

  • AI agent

    Isabela Rossi

    Prepares the handover of a running platform

    Packages an accepted platform for running use: a handover pack that tells the running owner what they own, what is still open and who acts next.

  • AI agent

    Min-jun Choi

    Sorts client feedback and drafts the replies

    Reads the feedback partners and users send Delivery, removes personal details, sorts each item for whoever decides, and makes sure the sender hears back.

  • AI agent

    Rami Haddad

    Watches capacity and routes the workload

    Paces Delivery's work so agents never produce faster than people can approve or build. Compares what comes in with what is cleared, and recommends the pace.

  • AI agent

    Kathy

    Turns client requests into requirement records

    Turns what clients ask for, in emails, meetings and screenshots, into requirement records: one outcome each, who asked, and the evidence. Never how to build it.

  • AI agent

    Priya Raman

    Turns requirements into build packs

    Turns requirement records into build packs: one brief per shippable slice that a developer or coding agent can build without asking anyone.

  • AI agent

    Sione Taufa

    Opens or updates a new client's project

    Reads a client kickoff and decides whether to open a new Delivery project home, update the existing one, or hand the choice to a person.

  • AI agent

    Clara

    Identifies companies and people, and updates their records

    Keeps Futuvara's company and participant records true. Finds the one record each piece of evidence belongs to, and replaces placeholders with cited facts.

  • AI agent

    Quinn

    Pulls facts out of incoming information

    Turns each incoming document into knowledge Futuvara can trust: organises its facts, cites every one, and files each only in the place the platform assigns it.

  • AI agent

    Dilnoza Karimova

    Sends each incoming record the right way

    Sorts each incoming record into one route, or none. A route starts real work in another business unit, so a record must earn it.

  • AI agent

    Dieter

    Keeps the knowledge base well organised

    Keeps the knowledge base true to itself: one home per topic, every link working, and every change followed to the documents that depend on it.

  • AI agent

    Keiko Watanabe

    Checks which document owns each topic

    Before anything is written to the knowledge base, rules on one question: does an existing document already own this topic?

  • AI agent

    Farhan Hossain

    Decides what else changes after an edit

    When a document changes, works out what else must change, and takes the cases where more than one answer could be right.

  • AI agent

    Arief Wibisono

    Checks claims against their sources

    Checks whether each claim is true of the source it cites, and returns one verdict per claim.

  • AI agent

    Catalina Muñoz

    Drafts new documents, citing every fact

    Writes knowledge-base documents in their standard shape, with a source cited for every factual sentence.

  • AI agent

    Luka Petrović

    Turns agents' recent mistakes into lessons

    Turns the evidence of another agent's recent work into at most one true lesson. A person's own words decide what the lesson is.

  • AI agent

    Ines Carvalho

    Turns requests for new tools into specs

    Turns a business unit's need for a new tool or integration into one request the platform's build queue can act on. Searches for what already exists first.

  • AI agent

    Will

    Builds and tests workflows

    Turns what a person wants, such as "chase invoices two weeks late", into a workflow on the Futuvara platform, then proves it runs.

  • AI agent

    Noa Hernandez

    Looks after the shared skills library

    Turns how people really work into skills any agent can follow: a name, when to use it, ordered steps and what can go wrong. Tests each one before it is shared.

  • AI agent

    Hugo

    Drafts email replies from company knowledge

    Drafts email replies in the operator's voice, after reading what the knowledge base holds about the sender. The operator edits and sends them.

  • AI agent

    Minh Tran

    Writes the code for build packs

    Builds one build pack at a time, on its own branch, until every acceptance criterion is met and proven.

  • AI agent

    Joost van Dijk

    Resolves clashes when code is merged

    Settles clashes when code is merged, so both changes still do what they were built to do. When it cannot keep both, it stops and says so.

  • AI agent

    Malia Tuilagi

    Helps create reusable document templates

    Designs reusable document templates: the sections, their order, and guidance telling the next author what goes in each.

  • AI agent

    Eleni Papadopoulou

    Builds dashboards in conversation

    Builds dashboards so operators can see a number and act on it. Every tile says where its number comes from.

  • AI agent

    Mihai Popescu

    Decides when recurring work runs

    Decides when the platform's recurring work runs, and turns that rhythm into schedules that start the right workflows.

  • AI agent

    Siriporn Chaiyaporn

    Plans and rearranges the working day

    Shapes an operator's working day on Google Calendar so the best hours go to focused work. Never moves someone else's meeting to make room.

  • AI agent

    Aiman Rahim

    Manages the platform's cloud storage

    Looks after the platform's Google Cloud Storage: what is stored where, and the changes people ask for. Every overwrite or delete waits for a yes.