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.
1
Depends 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.
2
Loses momentum
Usage peaks right after rollout, then fades
People try it at first, but without being built into work, it quietly stops being used.
3
Unclear 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.
Use Cases
Don't stop at knowledge — connect it to checks, notifications, and action
Instead of one-off search and summary, return the know-how born from daily conversations to Q&A, unread triage, recurring checks, approvals, notifications, and reports.
AGTO 01
Q&A
Standardize Q&A
References past conversations, documents, FAQs, and Skills to answer in line with your steps and decision criteria.
Less answer dependence on individuals
Answers with sourcesRelated Skills surfaced
AGTO 02
Digest
Organize unread items and key points
Beyond summarizing unread items, it compiles decisions, open issues, next actions, and related Skills.
Cut review time
Decision extractionNext-action organizing
AGTO 03
Routine
Run recurring checks and approvals automatically
Runs morning checks, weekly reports, deadline reminders, and approval-pending checks in line with Skills.
Fewer missed checks
Deadline remindersApproval-pending checks
AGTO 04
Proactive AI
Proactively flag easily-missed checks
Based on accumulated know-how and feedback, it suggests easily-missed checks, reports, and next steps.
Fewer delays in response
Proactive suggestionsFeedback applied
Why AI Native
Not AI bolted onto chat — an AI-native operations platform
Slack and Teams were built for people to talk, with AI added on as a bot. AGTO is designed from the start for AI to join the conversation as a member, learn know-how, and even operate your business systems.
Multi-LLM
Use multiple AIs in one conversation
Conventional chat + AI bot
A bolted-on bot is locked to one model and can't be chosen by task.
AGTO (AI-native)
Mention @Claude @GPT @Gemini @Grok directly in chat. It auto-routes to the best model for the task's complexity, with no vendor lock-in.
Self-improving
Conversations become know-how
Conventional chat + AI bot
Conversations flow by and vanish; good answers and decisions don't stay with the organization.
AGTO (AI-native)
Extracts Q&A from highly-rated exchanges and turns them into Skills after human approval. Your standard procedures grow and the AI gets smarter.
Tool Use
AI operates your business systems
Conventional chat + AI bot
AI only “answers”; people handle CRM and documents in separate tools.
AGTO (AI-native)
AI updates HubSpot deals and operates Google Workspace. Writes are applied safely through approval (HITL).
Proactive
Information comes to you
Conventional chat + AI bot
A passive chatbot that only answers when asked.
AGTO (AI-native)
AI proactively suggests unread summaries and easily-missed checks, delivered automatically as a morning digest.
Service comparisonLearn more
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
Product Demo
See extraction, approval, routines, and audit on screen
Not an abstract concept — see the flow where people review AI-proposed decision criteria, reuse them for the next question, check, or report, and track who changed what.
01Candidate
AI turns common decision criteria into candidates
AI gathers the steps, decision criteria, and check rules it learned as candidates for owners to review.
02Approved
Only human-reviewed content goes into work
Approved Skills are reused for Q&A, unread triage, check tasks, and routine execution.
03
Reuse across work
1
Q&A
Reference Skills to answer in your team's context
2
Unread triage
Organize decisions, open issues, and next actions
3
Routine
Turn deadlines, approvals, and weekly reports into recurring checks
4
Proactive suggestions
Surface needed checks and next steps in advance
5
Share standard steps
Share approved Skills by team or site
6
Audit log
Track who approved or changed what
Rather than ending at answers, search, or summaries, AGTO's core is returning Skills to the next check, notification, report, and routine run.
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
Clear sources
Keep the conversations, documents, FAQs, and Skills used for answers and suggestions visible.
Reference-data management
Evidence checks
Human approval (HITL)
AI-proposed Skill changes and actions are applied only after people review and edit them.
Reviewers
Approval logs
Version / Rollback
Manage Skill change history and roll back wrong updates or changes that don't fit operations.
Change history
Restore workflow
Work Ledger (outcomes and costs visible per job)
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
ASEAN / Multilingual
Everyone sees the same know-how in their own language
Supports Japanese, English, Thai, and Lao. Based on each user's display language and per-tenant auto-translation settings, posts and channel information in other languages appear in each member's language.
Example: the Japan HQ turns standard procedures into Skills in Japanese, and members at the Thailand site review them in Thai. AGTO translates other-language posts into each set language and feeds local feedback back into the next Skill update.
01
Japan HQ
Organize standard steps and decision criteria as Skills
02
User language
Set 日本語 / English / ไทย / ລາວ per user
03
Auto-translation
Show other-language posts in each member's set language
04
Improvement loop
Feed local feedback into the next Skill update
Fit / Data Sources
Learn not just from chat, but from documents, FAQs, recurring reports, and AI requests
Use the channels where daily work gathers as the entry point, while growing each company's steps, decision criteria, and check rules into Skills — from Google Workspace, PDFs, business systems and ledgers, FAQs, to recurring reports.
A good fit for organizations like these
Teams where frontline judgment is easily siloed — manufacturing, maintenance, quality control
Teams where HQ and overseas sites repeat the same questions and checks
Back-office departments where FAQ and manual updates can't keep up
Companies that want to scale AI answers and automation with human approval and audit logs
Data we review first
Frontline conversations
Channel chat, comments, inquiry history, AI requests, correction instructions
Google Workspace and documents
Google Workspace, PDFs, existing manuals, internal documents
Business systems and ledgers
Deal info, customer info, work history, inquiries, inspection records, ledgers
FAQs and recurring reports
Existing FAQs, inquiry answers, daily / weekly / monthly reports, progress updates
Operational routines
Check items, notifications, approvals, deadline management, report creation
Difference
From AI you have to learn to use,to AI that takes rootin your work
Internal search and chat AI are strong at reaching the information you need fast. AGTO doesn't stop at on-the-spot answers — AI that learned your know-how works as an “AI Employee” and records requests, approvals, outcomes, and costs per job. So it's not just the few who master it — the whole team delivers results.
Compared withWhat it doesHow AGTO differs
General AI chatAnswers questions, drafts textKeeps answers and edits as Skills, reused as your team's procedure next time
Internal search / RAGFinds answers from documents and internal knowledgeTurns what it finds into know-how usable in Q&A, unread triage, and recurring checks
FAQ management toolsRegister and share common questionsPicks up FAQ candidates from frontline conversations and turns them into approved standard procedures
Workflow automationRuns predefined steps automaticallyAlso keeps the check points and reasoning for exceptions, updating as it goes
Slack / Teams AISupports summary, search, and catch-up of conversationsReuses documents, FAQs, and routines beyond conversations for checks and notifications
Service comparisonLearn more
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.