Learn
Conversations and documents become company know-how
Within access rights, AI learns your steps and decision criteria from conversations, documents, and decisions, and stores them as Skills.
AI Employee Platform — teach AI your company
Grow “AI Employees”that take root.
Most AI rollouts still fall short of results — not because of model quality, but because the AI never sticks. AGTO grows AI that has learned your company's know-how into “AI Employees,” and embeds them into daily work under human approval.
Within each user's access rights, AGTO learns from your documents, conversations, and decisions. It gets smarter the more you use it and fits naturally into your everyday workflow.
Why AGTO
Adopt AI for good with “Learn, Work, See”
AI that has learned your know-how works as an “AI Employee,” and every request, approval, outcome, and cost is visible per job. That's why it takes root on the frontline.
Work
The AI it learned works as an “AI Employee”
Built on accumulated Skills, AI Employees with defined roles are assigned to work and handle tasks under human approval.
See
Requests, approvals, outcomes, and costs are visible per job
For each job (Objective), a “Work Ledger” records who approved it, what was delivered, and how much it cost.
Stick
Built into your everyday workflow
No new tool to learn — the AI Employee is right inside your usual work. It gets smarter as you use it, and takes root.
Problem
Why doesn't the AI you rolled out get used?
Most generative AI still hasn't reached business results (MIT NANDA reports 95% of organizations see no clear return). The cause isn't performance — it's a lack of adoption, because the AI isn't built into frontline work.
1Depends on the user
It takes prompt skill, so only a few people can use it
The effort to master AI is left to individuals, so it never spreads beyond a handful of capable employees.
2Loses momentum
Usage peaks right after rollout, then fades
People try it at first, but without being built into work, it quietly stops being used.
3Unclear impact
You can't tell if it works, so it stalls at PoC
With cost, impact, and risk invisible, you can't make the call to roll it out, and it stalls.
How AGTO Works
The loop where conversation becomes company know-how
Within access rights, AI proposes candidates and only human-reviewed content is stored as Skills. Accumulated Skills are reused as AI Employee Skills, getting smarter the more you use them.
Knowledge Loop
Grow Skills, return them to work
Input
Capture know-how
Extracts steps and decision criteria from frontline conversations, documents, FAQs, recurring reports, AI requests, and feedback.
Learn
Grow it into a Skill
Content that owners review, edit, and approve is managed as a reusable unit of operational know-how.
Reuse
Return it to work
Feed it back into Q&A, unread triage, recurring checks, approvals, notifications, and reports — a learning loop that doesn't end at an answer.
Capabilities
What AGTO can do
We organized the work you can delegate to AI Employees by domain. With approval and audit logs built in, start small and grow as you use it.
1Skills
Manage skills by role in one place
Accumulate approved skills and reuse them across multiple jobs.
2Routine / Digest
Bundle unread, routine, and pending approvals daily
Smart Digest prioritizes and surfaces only what people must judge.
3Governance
Control with approval, permissions, and audit logs
Aggregate LLM usage and approval requests for explainable AI operations.
4Internal Q&A
AI organizes and answers internal questions
First-line answers from FAQs, docs, and past replies. Skilled after human approval.
AI Employee
AI that learned your company takes ownership as an AI Employee
Built on accumulated Skills, AI personas with defined roles, tone, and constraints are placed in channels. They run autonomously on routines and post the results. Usable tools are whitelisted, and high-risk actions require human approval.
Don't just “use” AI — “grow” AI Employees
Not a passive chatbot — AI that learned your company works as a member with a role. As it learns more, you can gradually widen what you delegate through routines and approval gates.
Define role, tone, and constraints
Usable tools are whitelisted
High-risk actions require approval (HITL)
Every action is recorded in the audit log
01
Research employee
Handles research and summarization, organizing key points and sources before posting.
02
DevOps employee
Monitors incident detection and routine checks, reporting status to the channel.
03
Content employee
Handles drafting and proofreading, compiling work for review.
04
BizOps employee
Builds metrics and recurring reports, sharing them on schedule.
AI Employee guideLearn more
Governance
People stay in control of how much AI is delegated
AGTO lets you manage the information AI references, the Skills it uses, and the scope it can execute. Through human approval, permissions, audit logs, and Version / Rollback, you widen delegation step by step to fit your operations.
Use it as an AI operations platform people control — not AI that acts on its own
Decide the target data, permissions, excluded information, approvers, retention periods, and routine execution scope up front. Operate it so you can see what AI looked at, why it proposed, and how far it executed.
Permission-based reference control
Manage what information AI can reference and what it can execute per user, team, and role.
Role-based access
Excluded-data settings
Human approval (HITL)
AI-proposed Skill changes and actions are applied only after people review and edit them.
Reviewers
Approval logs
Work Ledger
Beyond usage volume, record requests, approvals, outcomes, costs, and failure reasons per job (Objective). Impact is visible in numbers, so it becomes the basis for company-wide rollout instead of stalling at PoC.
Outcomes and costs
Approvals and failure reasons
Audit log
Track who approved or changed which Skill, and which checks or routines used it.
Audit log
Execution history
AI governanceLearn more
Pilot
Start by turning one team's know-howinto Skills and validating it
After confirming data scope and permissions, run a small pilot of Q&A candidates, Skill candidates, Digest, Routine, approval flows, and audit logs.
If FAQs are in place, you can validate answer quality from existing FAQs. If not, start from meeting notes, business documents, frontline conversations, and recurring reports.Run a small pilot
1
Day 1-2
Select the target team, work, and data scope
Target data, permissions, exclusion rules
2
Day 3-5
Connect and load conversations, FAQs, meeting notes, documents, and recurring reports
Initial dataset
3
Week 1
Turn repeated questions, steps, and decision criteria into Skill candidates
Q&A candidates, Skill drafts
4
Week 2
Validate Digest, Routine, approval flows, and report drafts
Summary quality, check-rule drafts
5
Final day
Review impact and next steps
Rollout decision, improvements, operating design
Metrics you can validate
Before scaling, check impact, operational load, and how much you can delegate with these metrics.
Fewer repeat questionsCatch-up timeSkills approvedChecks turned into routines
FAQ
What people often ask before adopting
Get a handle on target data, how much to delegate to AI, security, validation methods, and how pricing works before your first consultation.
Can we start from documents and FAQs, not just chat?
Yes. You can narrow the scope and turn it into Skills from channel conversations, Google Workspace, PDFs and existing manuals, business systems and ledgers, FAQs, recurring reports, and more.
Does the AI act on its own?
AGTO operates on the premise of human approval, permissions, and execution scope. You can manage how much you delegate Skill creation and routine execution, step by step.
How is confidential information handled?
Define target data, excluded information, reference permissions, retention / deletion rules, and approval logs in advance.
Can we use it even without FAQs in place?
Yes. You can start by extracting Skill candidates from meeting notes, business documents, frontline conversations, and AI request history.
What can we check in a small pilot?
The quality of Q&A candidates, Skill candidates, Digest, Routine, approval flows, and audit logs — plus the operational issues that come up when scaling.
How is pricing determined?
It's designed individually based on the number of users, the data scope connected, AI usage, and the scope of onboarding support.
Service Deck
What the service deck covers
The mechanism, how to use Skills and Routine, sharing standard procedures, visibility into quality / cost / execution, supported data, governance design, validation scope, and how pricing works — all in one place.
Next Step
Make AI stick in your company, starting with one task
Once we know the target work (we recommend inquiries or internal Q&A), whether you have FAQs and documents, and the jobs you want to hand to AI Employees, we can design a small starting scope.