01
AI transformation
Redesign your workflow around AI, starting with which decisions stay with people and which tasks can be delegated. Build a system that fits the way your organization works.
ENGINEERING & TRANSFORMATION
AX · FIBER OPTICS · ENGINEERING DMS.LABS / FIBER CORE — 001
Beyondcomplexity.
Redesigning work with AI.
Making optical networks work in the field.
REDUCE THE NOISE. BUILD WHAT TRULY MATTERS.
AI workflow redesign, optical network training and technical support grounded in 20 years of engineering and international trade. We leave your team with practical processes, materials and the confidence to run them.
- FOCUS
- AI Transformation · Training · Global Agency
- APPROACH
- Simple, field-proven,
built to last
01 / ABOUT REEDO
Engineering
in plain language.
PRECISIONCONNECTIONCLARITY
Technology, explained simply.
I design, automate and teach so you leave with something you can put to work.
My background spans nearly twenty years of 3D design, hardware development, FTTx equipment and technical training. I have developed more than twenty hardware products and trained engineers in nine countries, working across optical network infrastructure and product development.
Today, I focus on AI transformation: rethinking how organizations work with AI. I connect automation, practical tools, training and content to make technology understandable and ready to use.
More about Reedo ↗Reedo.- Years of Experience
- 20+
- Countries Supported
- 9
- Core Patents
- 17
- Approach
- Field-First
02 / SERVICES
The work that turns
technology into real outcomes.
Our focus is AI workflow redesign and optical network training, backed by international trade, technical support and engineering experience.
ASSEMBLY STUDY / BRAND CONCEPT
Every part.
One purpose.
02
Optical network training
Practical training in FTTx architecture, optical power budgets and OTDR fault finding, informed by experience training engineers in nine countries.
03
International trade agency
Twenty years of optical equipment import and export across the Middle East, Southeast Asia and Europe. Support covers sourcing, specification reviews, customs, logistics and staff training.
04
Technical support
Diagnose and resolve issues during equipment deployment and operation, with remote and on-site support tailored to actual field conditions.
05
Automation & technical training
Automate repetitive reporting, documentation and administration, then help your team use the results through clear, practical training.
06
3D design & modeling
Turn ideas into designs you can evaluate, combining product modeling and structural reviews with experience developing FTTx hardware.
07
Content creation
Bring photography, writing, video and AI together to tell the stories of people and brands.
03 / SELECTED WORKS
The work speaks
before the words do.
Explore project work and practical guides. Start with the area closest to your challenge.
01 / Setting a Monthly Spend Cap for AI AutomationsDMS.LABS
WorkWorkplace automationAX transformation
Setting a Monthly Spend Cap for AI Automations
A practical guide to capping what an AI automation can spend each month. It separates the limit that only warns from the limit that blocks requests, and shows what the automation should do when a limit is hit.
Read the English guide02 / Before Trusting a Season ScoreDMS.LABS
WorkAX transformationWork automation
Before Trusting a Season Score
A DMS.Labs operating note on what a Naver DataLab season score measures and three checks to make before acting on a grade of A: peak timing, the relative baseline, and empty cells when a tool did not run.
Read the English operating note03 / One Line for Every Automation RunDMS.LABS
WorkWorkplace automationAX transformation
One Line for Every Automation Run
A practical guide to writing one line per automation run in a table outside the tool: when it ran, what started it, what it did outside and who checked it, so a wrong result can be traced weeks later.
Read the English guide04 / When the Person Behind the Automation Is AwayDMS.LABS
WorkWorkplace automationAX transformation
When the Person Behind the Automation Is Away
A practical guide to writing one card per automation: who owns it, who covers for them, who can stop it and where the access details are kept, so work does not stall when its builder is away.
Read the English guide05 / Did AI Answers Cite Our Site?DMS.LABS
WorkAX transformationPerformance measurement
Did AI Answers Cite Our Site?
A DMS.Labs operating note on sorting questions into three layers and recording Google AI Overviews and ChatGPT citations the same way, separating confirmed cells from unconfirmed ones.
Read the English operating note06 / Choosing the First Task to AutomateDMS.LABS
WorkWorkplace automationAX transformation
Choosing the First Task to Automate
A practical guide to picking the first task for AI automation. One table compares how often the work repeats, how clear the pass standard is, how long a check takes and what a wrong result would cost.
Read the English guide07 / Choosing Data to Share with AIDMS.LABS
WorkAI trainingWorkplace automation
Choosing Data to Share with AI
A practical guide to selecting inputs for AI exercises, with fictional practice copies, residual identification clues and separate checks for training use and retention.
Read the English guide08 / Acceptance Tests for an AI SkillDMS.LABS
WorkAI skillsAI training
Acceptance Tests for an AI Skill
Separate revision cases from final checks, define mandatory conditions and check that previously working behavior survives changes.
Read the English guide09 / Why Publication and Records Are SeparateDMS.LABS
WorkAI transformationWorkplace automation
Why Publication and Records Are Separate
An actual DMS.Labs publication-and-ledger gap: the October 11 repair and inspection code show how to restore the connection without republishing.
Read the English operating note10 / When to Restart an AI AutomationDMS.LABS
WorkWorkplace automationAI skills
When to Restart an AI Automation
A practical guide to checking external results and approval before a retry, with completion states, request identifiers and restart points.
Read the English guide11 / Build a Knowledge Base Your AI RemembersDMS.LABS
WorkAI trainingWorkplace automation
Build a Knowledge Base Your AI Remembers
An AI training guide to building a personal knowledge base with Obsidian and the LLM Wiki method, with a blank starter to download.
Read the English guide12 / Before you ask AI to do the job, write the one-page job sheetDMS.LABS
WorkAI skillsWorkplace automation
Before you ask AI to do the job, write the one-page job sheet
An AI skill-building guide that borrows the agent skill format: a six-part job sheet, tested against a list of real failures.
Read the English guide13 / AI training should start with 'Should I hand this task over?'DMS.LABS
WorkAI trainingWorkplace automation
AI training should start with 'Should I hand this task over?'
A training design guide built on three studies: a task boundary map for deciding what to hand to AI, plus a check two weeks later.
Read the English guide14 / Can Muse and dots replace an existing automation setup?DMS.LABS
WorkAI automationAI agents
Can Muse and dots replace an existing automation setup?
A documentation-based comparison of 38 active routines: research, publishing, Windows workflows and video production, with migration limits kept explicit.
Read the English guide15 / How to use Jev: setup and costsDMS.LABS
WorkAI skillsWorkflow automation
How to use Jev: setup and costs
A 12-page trial covering structured judgments, Windows setup, budget settings, limitations and evidence-based review.
Read the English guide16 / What to keep in view while Claude Code worksDMS.LABS
WorkAI educationClaude Code
What to keep in view while Claude Code works
A document-based guide to You should Know: benefits, limits, activation requirements and a bounded trial.
Read the English guide17 / How to hand work to an AI agentDMS.LABS
WorkAI educationAI agents
How to hand work to an AI agent
A practical Work guide drawn from OpenAI's dots demo: five habits for delegating to AI and where to draw the line on permissions.
Read the English guide18 / Measuring AI adoptionDMS.LABS
WorkAI transformationPerformance measurement
Measuring AI adoption
A practical AI transformation guide to revenue, costs and review time, informed by a KISDI study and its limitations.
Read the English guide19 / Giving AI automation permission in three stepsDMS.LABS
WorkPractical guideWorkplace automation
Giving AI automation permission in three steps
A practical Work guide to widening an AI automation's permissions in order: read, draft, then act.
Read the English guide20 / Publishing a page in two languagesDMS.LABS
WorkPractical guideMultilingual sites
Publishing a page in two languages
A practical Work guide to checking Korean and English article bodies, reciprocal language links, photographs and mobile layout on the production domain.
Read the English guide21 / OPTICAL NETWORK / SIGNAL PATHDMS.LABS
One signal.
Many connections.
FTTxOptical NetworksField Training
Optical Network Training
Hands-on training in fiber-to-the-x (FTTx) network architecture, optical power budgets and fault finding with an optical time-domain reflectometer (OTDR). Written in English for engineers making decisions in the field.
Explore training in English22 / AX / TRANSFORMATION PLAYBOOKDMS.LABS
From tasks
to systems.
01Map tasks
02Assign roles
03Handoffs
0490-day review
AI TransformationWorkflow DesignHandoffs
AI Transformation Design
A practical approach to redesigning work around AI: map the workflow, define human and AI responsibilities, plan handoffs, and measure progress over 90 days.
Read the project in Korean23 / AUTOMATION / CONNECTED FLOWDMS.LABS
AutomationPractical WorkflowsProductivity
Workplace Automation Lab
Documented experiments and practical workflows for repetitive work, including email, reports, document preparation, field data and trade documents.
Read the projects in Korean24 / AI / CREATIVE ENGINEDMS.LABS
ComfyUIAIPipelines
AI Skills & Implementation
Implementation notes on connecting AI to real work, from ComfyUI API integrations and image generation pipelines to tools for organizing knowledge and running AI workflows.
Read the projects in Korean25 / EDUCATION / LEARNING BY MAKINGDMS.LABS
EducationAIWorkshops
Practical AI Education
Hands-on curricula and examples that help learners use AI in their own work. Programs connect problem definition, workflow design and guided practice, including individual technical consulting.
Read the projects in Korean26 / STORYLENS / OPTICAL STORIESDMS.LABS
PhotographyAICreative Learning
Dreaming Camera / StoryLens
An AI education project that connects photography, AI, writing and music, helping people explore technology through creative work.
Visit StoryLens · Korean · new tab04 / HOW I WORK
This is how
I work.
I care about results that help in practice. Every project should be easy to explain, possible to try and clear about what comes next.
Technology should help,
not merely impress.
No jargon, just clarity.
Start with the problem we can solve and explain the technology in language your team can use.
Results you can use right away.
Deliver reusable workflows and documentation that support everyday work beyond the first demonstration.
Field-first, always.
Design, automation and training earn their value when people can use them confidently in the field.
People before technology.
Good work starts with a conversation that understands your context, before choosing the tools.
05 / REEDO INSIGHTS
Notes from the work.
Notes on automation, tools, creativity and the way we work.
012026-11-03
Plan What to Do Before the AI Answers
A promise to review the AI's answer later tends to blur at the very moment the answer arrives. This article reads a meta-analysis of 642 planning tests and proposes a one-line if-then plan for that moment, along with how modestly to expect it to work.
022026-11-02
Decorating a Travel Photo: Ask for Slots First
To turn a travel photo into a social card or a travel-journal page, does it help to split the page into photo, drawing and margin slots first? I tested a one-line request and a slot-based request once each on the same AI-made example photo, and list what held, what did not, and a prompt you can copy.
032026-10-30
Does an AI-Made Product Have Copyright?
Korea's registration guide for generative AI works and the U.S. Copyright Office report point the same way: protection follows the human contribution. Here is a way to record that contribution before you sell an AI-made digital product.
06 / CONTACT
Let's make
it matter.
Make the complex simple.
Tell me what you are working on.
We can start by making the next step clearer.