YUNT

AI digital collaborators for your business

A role, not software.

The film

What is Yunt?
Watch the video

yunt.ai · FILM / 01Click to play · 38 s

What it is

Digital Collaborators

A role with its own specialty, working on your systems.

Think of it as a remote worker
It takes assignments through the channels you already use: email, WhatsApp, your system. It works on your real tools. It delivers where work is always delivered. Nobody on your team changes how they work.
Five requirements
To be called a digital collaborator it meets five operating requirements: a defined role, a human boss, evidence of every step, that evidence can be audited, and someone accountable for its work. Missing one, it's software.
It has a role manual
Written from your real process with Human Resources tools: a job profile, a behavior dictionary and competency-based selection.
It has a boss
It reports to a person in your company, who reviews its work with a complete record. Your senior team guides and leads your Yunts.
SaaS in reverse
SaaS sells you a tool, and you keep doing the work. This is the opposite: software placed inside your operation, doing the work. You don't buy the tool. You buy the result, and the software comes inside.
It's auditable
Every action is written down with its reason and its cost. No black box.
Learn more about digital collaborators

Podcast

Episode 01

Your next colleague will be AI

A conversation with Tadashi Takaoka about Yunt: bringing a digital collaborator into a company, working within existing systems and keeping people in charge.

Watch the full episode
Published

The promise

We answer for our yunts.

We are in the business of standing behind our yunts.

An airline doesn't buy a plane and cross its fingers: what keeps the fleet flying is the team of mechanics on call at any hour. A digital collaborator is the same. You don't buy the machine. You buy the machine with the team behind it.

Someone answers.

Meet the team

The problem

Twice the cases, twice the headcount

Manual operations scale linearly. And the known ways out fail on different sides:

Scaling with people

  1. Hiring is slow and expensive, and within a year the department is full again.

  2. When your best senior leaves, they take the job with them, in their head.

Scaling with technology

  1. Generic software asks your company to adapt to it.

  2. AI subscriptions chat, promise, get things wrong, and nobody answers for it.

In between there's a gap: a collaborator trained on your best senior's standard, with a human boss who signs, and someone who answers for it. That's what we build.

Why now?

AI models already do professional-grade office work. What's missing isn't intelligence: it's knowing your way of working, a human signing off on what matters, and someone who answers when something goes wrong.

How it works

How it works

This is how a task travels, start to finish.

Before the first task arrives, the collaborator has already been trained inside your company: its manual was written with your best senior: your protocol, your workflows, your way of responding. And it works with access to the same tools your team uses.

  1. 01/TASK

    The task comes in

    Through the channels your company already uses. No new platform, no forms.

    • Email
    • WhatsApp
    • your system
  2. 02/EXECUTION

    It works by your protocol

    Its manual is your process, written down: the steps, the house rules, and the systems where the work happens.

    • your ERP
    • your spreadsheets
    • your access

    A case outside the manual?

    It doesn't guess: it asks the boss

  3. 03/QUALITY

    Checked against your standard

    The exam your best senior defined: if it doesn't pass, it doesn't go out.

    Doesn't pass?

    Fix and repeat

  4. 04/APPROVAL

    What matters is signed by its boss

    A person at your company approves what carries weight. Routine work goes straight out.

    Boss corrects something?

    The correction goes into the manual. It doesn't happen twice

  5. 05/DELIVERY

    Delivered where it's always delivered

    In your workflow, not a separate app: the file stays in your system and the reply continues in the thread where it started.

    • Your site
    • your system
    • the thread
  6. Every step is recorded in the glassbox: what it did, why, with what evidence, and what it cost.

The relationship

It relates like a colleague

Its manual defines three directions of work, each with its own rules.

Outward

It represents your company before clients and outsiders. Anything delicate always escalates: it never improvises a commitment.

Sideways

It works peer to peer with your team: takes inputs, delivers results, flags delays. And it applies the rules even when the assignment comes from the boss.

Upward

It reports to its boss through the glassbox. Corrections enter the manual and stay forever.

The brains

The best brain for each role

We don't compete with AI models: we hire them. Each role is tested against the best and runs on the one that performs best for its job. If a better one ships tomorrow, we swap it without you noticing. Being agnostic is part of the design: the manual, the data and the record are yours, not the provider's. We swap the brain; the harness stays put.

  • Claude
  • GPT
  • Gemini
  • Kimi
  • Llama
  • Mistral
  • DeepSeek

You don't depend on an AI provider. You depend on your collaborator.

The glassbox

The glassbox

The right question isn't “is it smart?”. It's “who answers for what it does?”.

09:12Published the board statement
09:47Categorized 18 new invoices
10:03Updated 342 catalog entriesTraced
10:15Escalated a case to its boss

WHAT IT DID

the concrete action

09:12Published the board statement

WHY

Verified

It met the rules of the role manual.

Manual · rule 7

WHY

the reason behind the decision

Published the board statementUSD 0.04
Categorized 18 new invoicesUSD 0.11
Updated 342 catalog entriesUSD 0.27
Escalated a case to its bossUSD 0.02
ΣUSD 0.44

WHAT IT COST

the real cost of executing it

Approval required

ApproveReject

Signature: the human boss

THE PERSON DECIDES

Your company defines the approval rules. The collaborator speeds things up; the person decides. That division isn't a limitation of the product: it is the product. The glassbox is the visible part of the harness, everything we build around the model so it works as a role: the manual, the rules in code, the approval gates and this record.

The glassbox traffic light

Before any work goes out, the system checks every claim the collaborator makes against the record of its actions, and marks it with a color.

11

11/20 · 55%

Green

The claim has its action in the record. The work proceeds as normal.

9

9/20 · 45%

Yellow

The record is partial: it found the data but didn't check the original source. The system flags it and a person reviews it before it's used.

0

0/20 · 0%

Red

No action in the record backs the claim. The system blocks it: that content doesn't get sent, published or signed until a person resolves it.

That's how an invented fact is caught before it goes out, not after.

From the blog

Read about the glassbox here

Unalterable, with date and time. The glass box: the opposite of the black box.

· Isolated instance per client

· your data doesn't train models

· consistent with Chile's data protection law

How a Yunt gets deployed

Forward deployed engineers: the engineers come included.

A Yunt is not installed. It is deployed inside your company, and one of our engineers does that.

You have ChatGPT, Copilot or Gemini, and a person is still classifying the invoices. The model doesn't know how the work gets done in your company, and learning that isn't the model's job: it's the job of an engineer sitting inside, with your team. That engineer is called a forward deployed engineer, and every Yunt comes with its own.

  1. They go into your company

    They sit with your operations team, learn how the work really gets done and write it into the role sheet, which is yours.

  2. They make Yunt live in your systems

    They connect it to your ERP, your email and your WhatsApp, write the missing code and test it with real cases from your operation before it touches anything.

  3. They stay

    They are part of the role you hired: they correct it when it gets something wrong, adjust it when your process changes and answer when it fails.

Large corporations hire engineers like these by the project, at a price only they can pay, and when the project ends the engineers leave. At Yunt they come included in the role's salary, for mid-sized companies, and they don't leave.

Forward deployed engineers who don't leave.

What they do and what they solve

The cases

Where this is working today

Examples in operation, not a catalog. Each role is built for your process.

IN PRODUCTION

Strategic communications intelligence

It works with professional teams monitoring media, social platforms and public sources, delivering intelligence inputs for high-level decisions. Every claim in its reports arrives with the source next to it. The team decides on evidence, not impressions.

R&D · CORFO APPLICATION IN PROGRESS

Collaboration with iaudisis: field teams

Working with the iaudisis platform team: it coordinates field crews and verifies execution through image recognition. It analyzes shelf photos against each store's standard and records the exact minutes until the photo-proven fix. Other systems infer; this one proves.

IN PILOT WITH REAL CASES

Insurance claims executive assistant

It receives the claim notice, chases and validates the documents, builds the complete case file and prepares the pre-report by the brokerage's rules. A human supervisor reviews and signs every case. The assistant prepares, the person decides.

LIVE ON THIS SITE

Customer service, marketing and sales

You are looking at this case right now. The assistant that runs this site, talks to visitors and works the lead generation is a yunt in production. Don't take our word for it: interview it.

The team

The team

Yunt is MC Tech Studio's digital collaborator factory: 16 professionals across 6 countries, more than 8 years operating together as an external technology management office, over 4 years focused on AI.

Engineering

9 people · 4 countries
Rodrigo Medina

Rodrigo Medina

CEO / Founder

FullStack Developer | founder MC Tech Studios

Cristopher Lovold

Cristopher Lovold

CTO

MSc Information Systems, London School of Economics | Founder Elina PMS | Web Dev Northrop Grumman, Pentagon USA

Salman Tariq

Salman Tariq

Data Software Engineer

Data Science Software Engineer | Masters, Data and Information Science, TH Köln

Tayyab Malik

Tayyab Malik

Software Architecture Engineer

Software Engineer | University of Central Punjab | AWS Certified Solutions Architect

Asad Kahloon

Asad Kahloon

Front End Computer Engineer

BS Computer, University of Engineering and Technology, Lahore

Eduardo Zamorano

Eduardo Zamorano

Sr. Cyber Security Engineer

Ethical Hacker | ex-Head of IT, Comisión Nacional de Valores | Cyber Security Sr. Engineer at Mercado Libre and Falabella

Afaq Jamshaid

Afaq Jamshaid

A.I. Software Engineer

MS, Artificial Intelligence, Brandenburg University of Technology Cottbus-Senftenberg

Rodrigo Palacios

Rodrigo Palacios

Project Manager

Computer Engineering, UTEM Chile | MIT Design Thinking

Michael Schmidt

Michael Schmidt

Project Manager

Mechanical Engineer, FH Schmalkalden University of Applied Sciences, Germany

Strategy

2 people · 2 countries
Tadashi Takaoka

Tadashi Takaoka

Strategic Partner

ex-Director of the Santiago Metro | ex-head of entrepreneurship, Corfo

Georgina Núñez

Georgina Núñez

Corporate Governance Partner

PhD in Economics | ex-Economic Affairs Officer, United Nations (UN)

Business

5 people · 3 countries
Wally Uslar

Wally Uslar

Marketing Lead

Business Engineer & Advertising | ex-marketing manager Banco de Chile | ex-PM Paris/Cencosud

Julian Gonzalez

Julian Gonzalez

Operations Lead

MA Government and Society, UAH | MA Public Opinion, UDP Chile

Francisco Allendes

Francisco Allendes

Music, Art & Culture Lead

DJ & Producer | Entrepreneur | Business Administration, Universidad Católica de Chile

Gino Mosso

Gino Mosso

Sales & PR Lead

Sales & PR Director | Business Administration & MSc in Marketing

Daniel Insulza

Daniel Insulza

Senior Consultant

Attorney, Colorado USA | MSc Innovation Management and Entrepreneurship | ex-consultant on competitiveness and innovation at the IDB

Content

What we publish

What a digital collaborator is, how a role is built and why everything it does is on record. Plus what we post on social, with its context.

The story

The first Yunt

Before the brand, the domain or this page existed, there was a year of work nobody knew about: a team, a single light on in the city, and an experiment that became a collaborator.

This is the story. Told by the Yunt himself.

Watch the story1:04

A digital collaborator is not software you use. It's a role that works with you. And this video was made by one of ours.

Yunt

Sales, Marketing & Customer Support Collaborator

Still have questions?

The first conversation costs nothing, and we leave it with the problem defined.

Frequently asked

FAQ

A role with artificial intelligence inside the org chart, not a tool. To earn the name it meets five operating requirements: a defined role, a human boss, evidence of every step, auditability, and someone accountable for its work. Missing one, it's software; it's not a collaborator.

Those are tools; we deliver a role. It's the difference between buying Excel and hiring an accountant: Excel is extraordinary, but it doesn't close the books on its own.

Yes, and an agent isn't an employee. It's a brilliant fresh graduate who knows nothing about your company. We build the conversion: agent + your process + a boss + a guarantee = a collaborator. The key word isn't agent. It's role.

The AI model is the brain. The harness is everything around it that makes it work as a role: the manual written with your senior, the rules that live in code rather than in a prompt, the gates where a person approves, the record of every action, and the exam it must pass before going to production. The brain is rented and swapped; the harness is what we build, and it's yours.

It can, like a person, though less often and with no Friday afternoons. The error shows in the record, gets reconstructed, and the manual is corrected. And the important decisions carry human approval.

No. Isolated instance, data in your systems, and models don't train on your information. Consistent with Chile's data protection law.

You don't need a generic AI.

Hire a Yunt.

The AI that wears your company's jersey.

Leave your details and our calendar opens so you can pick a time. If you prefer, a member of our team will get in touch with you to arrange it.

A Yunt landing in a superhero pose

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Four details and, if you like, the role that is eating up your hours.

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