10 posts tagged with "AI Workflow Automation"

Key takeaway On-Sitehub's AI-assisted risk assessment review reads each subcontractor's assessment items and classifies them as acceptable, requiring improvement, or inadequate. It checks whether the accident type, hazard, control measure, and risk rating fit the work context, then presents its reasoning and proposed revisions. The safety manager compares the current values with the AI suggestions and applies only the changes that are needed. The responsible person retains the final decision and completes the review.

When many subcontractor risk assessments arrive at once, safety managers often find themselves reading similar statements repeatedly.

They need to verify that the accident type matches the stated hazard, that the control measure can actually reduce the risk, and that the assigned risk rating is appropriate. Reviewing the main work category, detailed task, work location, and number of workers at the same time becomes even harder when every document uses a different format or wording.

As the number of items grows, so does the chance of overlooking something. The order in which items are reviewed may also vary depending on the reviewer's experience and habits.

AI does not approve or authorize the assessment. Its role in the first-pass review is to identify items that need attention and organize the evidence and suggestions that a safety manager should compare.

The risk assessment review workflow​

Depending on the site configuration, On-Sitehub allows subcontractor risk assessment data to be uploaded from Excel or entered directly on screen. Some templates can contain data for several subcontractors in one upload and then be separated into individual documents for each subcontractor.

This example screen is based on the structure of the product's preliminary-review document list. The site, subcontractor, document, revision, and item counts are all fictional.

On mobile, swipe horizontally to view the complete workflow.

1. Submission and AI review

2. Comparison and completion by the responsible person

For each item, the AI considers the following questions together:

• Does the accident type match the stated hazard?
• Is the hazard specific enough for the main work category, detailed task, work location, and number of workers?
• Can the proposed control measure meaningfully reduce the risk and be carried out on site?
• Is the risk rating appropriate in light of likelihood and severity?

After the review, each item receives one of three classifications:

• Acceptable: Based on the submitted document, no revision is suggested for this item.
• Requiring improvement: The overall direction is reasonable, but details need to be added, controls are missing, or the risk rating needs adjustment.
• Inadequate: The accident type and hazard do not align, or the control measure is ineffective. The item needs to be rewritten.

These classifications do not determine whether a legal requirement has been violated or guarantee that a site control is adequate. They are supporting opinions based on the submitted content, focusing on specificity, consistency, and practical applicability.

Classification and proposed revisions​

In addition to the classification and review notes, the AI can suggest revisions such as:

• A different accident type
• An adjusted risk rating
• A more complete control measure

The safety manager can compare the current value and the AI-proposed value side by side. Suggestions are not accepted as an all-or-nothing package. The reviewer can selectively apply only the accident type, risk rating, or control measure that needs to change. Before applying a suggestion, the reviewer can check the before-and-after values, and unsaved changes can be reverted.

When reviewing many items, the reviewer can filter items classified as requiring improvement or inadequate, then select the items and fields to update in a batch. The original text is not changed until a person selects the changes and saves them.

This fictional case is based on the product's comparison screen. The work details and AI suggestions are for explanation only and do not represent actual site data or assessment results.

Typical review examples​

The following examples illustrate how the feature approaches a review. Actual classifications and suggestions may vary according to the submitted document and the review criteria configured for each site.

Mobile crane operations​

Suppose the only control measure for a mobile crane operation is "restrict access within the working radius."

The AI may flag the item because it does not explain who will control access, where the boundary will be established, or how the restriction will be enforced. The safety manager then checks the equipment specifications, work location, and vehicle and pedestrian movements planned for the day before deciding which controls should actually be implemented.

Work at height​

If a work-at-height item lists only "wear a safety harness," the control may not be specific enough for the fall hazard.

The AI can review whether the accident type matches the hazard, whether the work location is described precisely, whether the control can be carried out, and whether the risk rating is suitable. It can then suggest areas that need improvement.

Hot work​

If a hot-work assessment lists only "place a fire extinguisher nearby," the control may be too narrow for the hazard.

The AI can assess whether the controls are sufficient for the stated hazard and suggest additional points that need confirmation. The safety manager makes the actual decision after checking the work area, nearby equipment, and materials in use.

Re-reviewing changed items​

When the responsible person edits an item after the AI review, the document status changes to Re-review required. Instead of analyzing the entire document again, the system can review only the changed items. When an AI suggestion is selectively applied, the applied value and its source remain in the history.

The review screen shows the number of items in each classification (Acceptable, Requiring improvement, and Inadequate) and lets the user filter by classification. It also provides an overall review summary and key observations for the full document. Results can be exported as a printable AI review report or an Excel file for sharing and recordkeeping.

Managing completed versions​

For sites where the feature is enabled, the responsible person can mark a preliminary review as complete after the AI review. A separate completed version is created, and the latest completed version for each subcontractor can be viewed in the "Risk Assessment DB" section of the toolbox meeting record.

Completion is different from automatic approval by AI. The AI classifies items and proposes possible revisions, but the responsible person decides which suggestions to apply and whether the preliminary review is complete.

People make the final decision​

A written risk assessment cannot contain every condition that matters. Weather, worker experience, equipment condition, simultaneous operations, and site traffic may all exist outside the document.

Even when the AI improves the wording, it does not verify actual installations or working conditions on site. When the submitted information is incomplete or incorrect, the analysis will also be limited. The AI's first-pass review should therefore be used as a starting point for confirming:

• Which submitted content supports each review note
• Whether a suggested control can actually be applied to the work location and equipment
• Whether an adjusted risk rating matches the site's criteria
• Whether conditions outside the document require additional checks

An item classified as acceptable is not exempt from site verification. It must be checked again when the work category, equipment, location, or working conditions change. AI classification helps prioritize the review; it does not reduce its required scope.

Using AI for an initial review can reduce repetitive checking and organize possible improvements in advance. Safety managers can then focus more of their attention on site verification and the final judgments that require human knowledge of the actual work environment.

This feature does not replace the site's risk assessment procedure or the responsibilities of its participants. The responsible people must conduct, verify, and record the assessment according to the laws and assigned roles that apply to the workplace. AI review results are supporting information for that process. The handling and retention rules for subcontractor documents should also be confirmed before deployment.

Site-specific scope​

The Excel template, upload method, preliminary-review completion process, and use of completed versions may differ by site configuration. The AI review perspective and three-level classification criteria can also be configured to match operational policies. Confirm the available scope and implementation schedule during deployment planning.

Explore On-Sitehub

miwork hangul converts HWP documents to PDF on a server and lets users review the result directly in a web browser.

Opening one or two Hangul documents and saving them as PDF is familiar work. The situation changes when hundreds or thousands of documents must be converted in batches or prepared as searchable data.

Each document has to be opened and saved, and someone must investigate whenever the process stops. Extracting text for search or generating document previews presents the same challenge. miwork hangul began as an effort to turn this work into a repeatable server-side document processing capability.

Why HWP to PDF comes first​

Once a PDF is available, users can review the document in a browser without installing a separate application. It also becomes easier to connect the result to search previews, document archives, and other downstream workflows.

Uploaded PDFs can also be reconstructed as HWPX. Rather than perfectly restoring every element of the original editing structure, this feature focuses on creating a practical, editable document from the text and images in a PDF.

Why Hangul document conversion is difficult​

An HWP file is more than a collection of text. Fonts, character styles, paragraphs, tables, shapes, images, headers and footers, footnotes, and page layout information all contribute to the rendered result. Even with identical text, differences in available fonts or layout calculations can change line breaks and page counts.

It is also not a format that exposes a browser-standard DOM in the way HTML does. Hancom provides an HWP Automation development guide and open-source resources related to HWPX and OWPML, but desktop automation and large-scale processing on Linux servers require different runtime environments and system designs.

This is not about a shortcoming in a particular product. It is a technical challenge that comes with reproducing a long-established document format across different environments.

Expanding the range of supported documents​

Over the past six months, we compared conversion results from a wide range of HWP documents and expanded the supported cases. Our internal tests now show a conversion success rate above 80%. Here, success means that conversion completes without an error and the principal text and page structure can be reviewed.

The HWP interpretation and PDF generation improvements developed through this work have also been incorporated into SoliDocs Viewer's Hangul document viewing capabilities. More demanding cases remain, including complex charts and equations, special objects, document-specific fonts, and precise page layouts. Documents that users agree to retain through the demo, together with the problem areas they mark, will be analyzed by type to improve these remaining cases.

The success rate is an observation based on our current internal test set, not a product guarantee. Results can vary depending on document structure, fonts, and object types, and the measurement will be updated as the test set grows.

Server-side applications​

miwork hangul runs in a Linux container without requiring the Hangul desktop application and can be connected through a server API. It can support batch PDF generation, web previews, and preprocessing for RAG and document AI workflows.

Check with an actual document​

The miwork hangul demo lets users convert HWP and HWPX documents to PDF, review them in a browser, and download the result. Users can mark the page and type of any mismatch they find, and can also try reconstructing a PDF as an editable HWPX document.

Open the miwork hangul demo →

TL;DR MiWork Agents started as a place to talk with AI. It has now evolved into an AI collaboration workspace where people and AI can communicate, organize knowledge, and use work tools in the same environment. This post introduces the upgraded channels, direct messages, wiki, shared chatbots, knowledge bases, MCP connections, and enterprise operations.

Don’t just ask AI. Work with AI.​

Enterprise AI usually begins with a private chat: a user asks a question and the AI responds. It is useful, but knowledge stays inside an individual conversation unless it connects to the way a team already works.

Real work also includes discussions with colleagues, shared files, recorded decisions, and actions in business systems. MiWork Agents now brings people, AI, knowledge, and tools into one collaboration environment.

Case Studies: How everyday work changes​

The point of this upgrade is not to add one more AI feature. It connects channels, the wiki, knowledge bases, shared chatbots, and MCP so AI can participate in the spaces where people already work. The following two case studies show how the upgraded capabilities connect in practice.

People and AI working together does not mean that AI takes over the work. It combines human experience and judgment with AI's ability to search knowledge and organize context that would otherwise be easy to miss.

Case Study 1. “Why did we decide to build it this way?”​

A developer who recently joined the project asks in a channel:

“Why was this feature left out of the mobile version?”

Even the people who participated in the discussion several months earlier may not remember the exact reasoning. A new team member would have to read old conversations and many documents, while an explanation based on memory could omit an important constraint.

The team brings AI into the current conversation. AI uses earlier discussions visible from the current channel and accessible wiki, knowledge-base, and technical documents to organize the original requirements, constraints, and trade-offs. Existing team members verify that the retrieved context matches what happened, while the new member identifies conditions that may have changed.

People decide together whether to retain the original decision or redesign the feature. If the decision changes, AI helps record not only what changed, but why it changed.

Case Study 2. Field and headquarters solve an equipment problem together​

A field technician posts equipment photos and symptoms to a channel from a mobile device:

“The vibration is stronger than usual, and the temperature is rising quickly.”

A technical specialist at headquarters asks the technician to check when the noise began, recently replaced parts, and current gauge readings. At the same time, AI searches accessible equipment manuals and maintenance history for similar symptoms, inspection points, and safety notes.

The field technician compares that information with the actual equipment and shares the results. The headquarters specialist reviews both the field observations and the organization's maintenance knowledge to determine a safe course of action. AI does not independently approve or perform a hazardous operation.

After the work, AI organizes the symptoms, evidence, actions, and results. Once reviewed by a person, the record can be saved to the wiki or maintenance history, while required parts and follow-up inspections can be registered through permission-aware work tools.

1. Channels and direct messages​

Teams can use topic-based channels, direct messages, and group conversations inside MiWork Agents.

• Public and private channels
• Colleague search and direct/group conversations
• Message reactions and threads
• Members and pinned messages
• AI mentions and invitations inside a conversation

People do not have to leave their collaboration context to use AI. They can bring AI into an existing discussion to summarize it or help with the next task.

2. AI conversations and work files​

Private AI conversations remain a core part of the product.

• Answers grounded in accessible knowledge bases and attachments
• PDF, DOCX, HWP/HWPX, and image uploads
• Conversation-level documents and files
• Voice conversation entry
• Favorites and share links
• Text formatting and conversation history

Files used for personal work can be organized separately, while documents that should be searchable by a team can be connected to a knowledge base.

3. From conversations to a collaborative wiki​

Useful discussions become difficult to find when they remain only in a message history. The MiWork Agents wiki turns channel discussions into documents that a team can continue editing.

Meeting notes, decisions, follow-up tasks, and references can be kept in one document. The wiki also supports document import/export and block-based editing.

Discuss in a channel
  ↓
Use AI to organize the discussion
  ↓
Publish it as a wiki document
  ↓
Let the team edit and reuse it

The goal is not only to generate an answer, but to leave behind knowledge the organization can use again.

4. Team knowledge bases​

Knowledge bases manage documents and team-level read/edit permissions. Users can search only the knowledge they are allowed to access.

• Document upload and processing status
• Visibility for each knowledge base
• Team-level read and edit permissions
• Connected document management
• Source documents for grounded AI answers

This keeps AI search within the organization's existing responsibility and access boundaries.

5. Shared chatbots​

Purpose-specific shared chatbots can handle repeated questions from teams or external visitors. Each chatbot has its own knowledge bases, visibility, and web-search policy.

Shared chatbots run separately from a user's personal AI environment. Personal memory and personal MCP connections are not exposed automatically; only the knowledge and capabilities explicitly assigned to the chatbot are used.

This supports internal help desks, policy search, product information, and customer-facing FAQ experiences.

6. Work-tool connections through MCP​

MiWork Agents supports MCP so AI can connect to real business tools instead of stopping at a text response.

Administrators register organization-wide MCP connections, while users authenticate with their own accounts when required. AI can then query information or perform allowed work through those tools.

The important part is not the number of integrations, but controlling who can use which tool and under what authority.

7. Enterprise operations​

An AI collaboration platform needs more than a chat interface. MiWork Agents includes an operations console for managing organizational use.

• User accounts and platform roles
• Teams and team membership roles
• Individual, team, and organization budgets
• Chat and embedding models with fallback order
• Shared chatbot creation, approval, and operations
• Policies and access scope
• MCP connection status
• Audit logs for authentication, permissions, and content changes

Administrators can monitor cost and usage, configure models and budget policies, and review important changes through audit records.

8. The same collaboration flow on desktop and mobile​

The primary collaboration surfaces are also designed for mobile use. Channels, conversations, AI, and the wiki remain accessible through bottom navigation, while conversation lists and composer areas fit smaller screens without horizontal scrolling.

The aim is to keep the same work context when reviewing a document in the field or continuing a conversation while moving.

One connected workflow​

The expanded product can be summarized as one collaboration flow.

People discuss work in a channel
  ↓
AI joins the conversation and organizes it
  ↓
Knowledge bases provide grounded sources
  ↓
Decisions and follow-up work move into the wiki
  ↓
Connected tools carry out permitted actions through MCP
  ↓
Administrators manage access, budgets, models, and audit records

Closing​

With this upgrade, MiWork Agents has become an AI collaboration workspace where people and AI work together.

It is not intended to replace human collaboration with an AI conversation. It brings AI into the environment where people already communicate, find knowledge, create documents, and use work tools.

Explore MiWork Agents

TL;DR Minisoft's official MiWork site, miwork.ai, is now open. It brings together MiWork OCR for turning documents into usable data, MiWork Agents for connecting organizational knowledge to work, and MiWork VibeHub for building and operating internal apps.

Why open a separate site​

When a company starts applying AI to work, the questions usually fall into three groups:

• How do we read and use scattered PDF, HWP, and Office documents?
• How do we connect organizational knowledge and work tools to AI answers and actions?
• How do we build, review, deploy, and operate the small apps that teams actually need?

MiWork brings these questions into one work-solution family instead of leaving them as a disconnected product list. Each solution can be explored and adopted independently, with a clear place to start for a specific problem.

MiWork OCR — documents into data​

MiWork OCR converts PDFs and images as well as HWP/HWPX, Word, PowerPoint, Excel, and HTML into structured, page-level results.

Short jobs can return immediately, while longer documents can be submitted and tracked until the result is ready. The product page also describes table handling and configurable OCR engines for different document types and accuracy/speed requirements.

Before a document can become searchable knowledge or feed a downstream AI workflow, it has to be read and structured reliably. MiWork OCR covers that first step.

MiWork Agents — from questions to action​

MiWork Agents is an enterprise AI agent workspace that connects organizational knowledge and work tools.

It brings document search and RAG, team knowledge bases, personal file workspaces, shared chatbots, and tool connections into one environment. Answers can include their source documents and continue into file work or connected business actions when needed.

For enterprise AI, operating standards matter as much as model quality. MiWork Agents also considers user and team access control, separated execution environments, credential management, usage and budget visibility, and audit records.

MiWork VibeHub — ideas into work apps​

MiWork VibeHub connects the full lifecycle of internal apps: create, review, deploy, discover, and operate.

App users can find and run the tools they need from an app store and personal dashboard. Builders can start from templates or their own Git repositories and use AI coding tools and shared platform services. Reviewers and operators manage visibility, app membership, deployment and rollback, runtime status, and logs.

When a field idea becomes a cost calculator, bid analyzer, or inspection tool quickly, speed is only part of the problem. Teams also need to decide who can use it, what data it can see, and how it is deployed and rolled back. VibeHub provides that operating foundation.

How the three solutions connect​

Each product can stand alone, but the shared flow becomes clearer when they are used together.

Document upload
  ↓
MiWork OCR — structured data
  ↓
MiWork Agents — knowledge search, questions, and actions
  ↓
MiWork VibeHub — build, deploy, and operate the work app

Organizations do not have to adopt them in this order. Some will start with document processing; others will start with team knowledge search or internal app operations. The site describes the current scope of each solution and provides a path to request an introduction.

What you can see on miwork.ai today​

• MiWork overview
• MiWork Agents
• MiWork VibeHub
• MiWork OCR
• Contact

This opening is not a claim that the product story is finished. It is a starting point for explaining the work solutions we are building in one place. We will keep updating the site as the product scope and use cases become more concrete.

Closing​

MiWork is not meant to be another screen with an AI feature attached. The goal is a connected flow in which documents become knowledge, knowledge becomes action, and the tools teams need can be safely operated inside the organization.

Take a look at the current direction and product family on the official site. Starting with one recurring work problem is enough.

TL;DR LUMINUS is now MiWork Agents, and MiniApp Hub is now MiWork VibeHub. MiWork is the umbrella brand for the work products Minisoft builds with AI.

What changes​

Why we changed them​

There was no single name for all of it. Presenting our products meant reading a list. "There is an AI Agent platform, and there is a mini app platform…" A list tells people what each product is. It does not tell them what this company does with AI.

The names did not explain the products. LUMINUS came from the Latin for light. It was pleasant to say, but nobody could tell what the product did from the name alone, so every introduction opened with an extra line of explanation. MiWork already has Work in it.

"Mini app" got too narrow. Today someone in the field describes what they need to an AI and a cost calculator or a bid analyzer appears in a short sitting — the flow usually called vibe coding. Apps made this way are not mini because they are small; what changed is the speed and the method of making them. VibeHub puts that change into the name.

Two products, for now​

Two products carry the MiWork name today: Agents and VibeHub. As more AI-based work products arrive they may join the same system, but which ones and when is not decided yet.

How existing posts and links are handled​

• Posts already published have been updated to the new names, with the former name noted at the top of each one.
• No post URLs changed. Saved links and inbound paths from press articles all still resolve.
• Screenshots and the guide video still show the former names. We will replace them in turn.
• Press articles keep the names they were published with.

Closing​

Renaming a product does not make it better. Helping people find you when they search, and telling them several products belong to one family without anyone having to explain it — that is about all a name does.

TL;DR A work platform in the AI era needs to manage not only ERP or groupware, but also spreadsheets, macros, Python tools, AI Agents, and vibe-coded apps. MiWork VibeHub aims to make scattered work tools searchable, shareable, installable, and operable as company assets.

Connecting scattered work assets, from Excel files to vibe-coded apps​

Companies have long adopted systems such as ERP, groupware, MES, and CRM to digitize work. These systems still support many core business processes.

But generative AI and coding agents are changing how work tools are created. Small internal tools can now be built faster, by more people, and in greater numbers. The question is no longer just whether a company can build apps. The question is how those apps can become lasting company assets.

Work Tools Are Becoming Smaller and Faster​

In the past, creating a business system required a long project. Teams had to define requirements, secure budgets, build, test, and deploy.

Now, generative AI and coding agents allow business users to create small tools for their own work.

Meeting note assistants, purchase request apps, quotation generators, bid analysis tools, cost calculators, and contract review tools can be created much faster than before.

Companies Already Have Many Work Assets​

Many important workflows exist outside official systems.

• Cost calculation spreadsheets
• Quotation macros
• Inventory management files
• Quality analysis scripts
• Bid analysis tools
• Contract review agents
• Internal web apps created through vibe coding

These tools move real work. But many of them remain scattered across personal PCs, shared folders, chat rooms, and email attachments.

The Next Problem Is Not a Lack of Apps​

AI is lowering the cost of building software. In the near future, one employee may create several work apps, and each department may continuously produce new tools.

In the past, companies lacked systems. In the future, they may have too many tools to manage.

Companies will need to ask new questions.

• Who created this app?
• Where is it running?
• Who is using it?
• What is the latest version?
• Can it continue after the creator leaves?

Can Personal Tools Become Company Assets?​

Good ideas often start with individuals. But if they remain on a personal laptop, they rarely become organizational assets.

For an individual tool to spread across teams, companies need a structure for registration, search, installation, operation, and feedback.

MiWork VibeHub as an Example​

An enterprise mini app platform such as MiWork VibeHub is one way to support this flow.

Spreadsheet-based tools, scripts, AI agents, and vibe-coded web apps can be registered as business assets.

Users can search for and install the tools they need. The company can manage who uses which app, what version is running, and which tools are gaining traction.

Future Competitiveness Will Come from Accumulated Work Assets​

AI will create more business tools. The important question is not how many apps a company can create.

It is how well those apps can be shared, operated, improved, and preserved as organizational assets.

Future competitiveness may come not from the number of systems a company has, but from how well it accumulates and operates work assets.

Closing​

AI is democratizing software development. Anyone can create work apps.

The enterprise challenge is shifting from building apps to connecting and managing the many work assets that are being created.

TL;DR MiWork VibeHub turns work apps that used to stay with one person or department into company-managed assets. App store, installation status, usage data, permissions, and version management help small work ideas spread across the organization.

An enterprise mini app platform where anyone can build, everyone can use, and the company can govern.

Enterprise Proposal · 2026

Problem Definition​

Companies already have many internal tools: inspection apps, report automation tools, data dashboards, chatbots, and request forms created by different teams. The problem is that even useful apps are often hard to find and hard to scale across the company.

In many organizations, business apps are created and managed separately by each department. Links, files, and tools are scattered, and useful apps are often not discovered. Similar apps are rebuilt, maintenance costs increase, and security or permission rules become unclear.

Introducing MiWork VibeHub​

MiWork VibeHub is an enterprise mini app platform that lets employees find internal apps like an app store, install them, and run them from a personal dashboard.

MiWork VibeHub solves this with an internal app store model. Employees search for the apps they need, install them, and the installed apps are automatically added to their personal dashboard. Ratings, comments, and rankings help useful apps spread naturally.

Solution Overview​

What MiWork VibeHub Makes Possible​

Companies have more internal work tools than they often realize. A spreadsheet refined by a business user, a small automation script created to reduce repetitive work, or a team-specific dashboard can already be a useful operational asset.

MiWork VibeHub gives these tools an official channel so they do not remain on personal devices or inside a single department. The point is to help useful apps be discovered, governed, and reused across the organization.

• Useful business apps can be shared across the organization.
• Apps created by individuals can be managed as company assets.
• Installation status and usage status can be checked.
• Versions and permissions can be governed systematically.
• Field ideas can accumulate as digital assets instead of remaining personal convenience tools.

Product Screens​

App Store​

The internal app store is an official catalog of verified mini apps. Users can search by categories such as safety, quality, purchasing, HR, administration, and report automation. They can compare apps by ratings and installation counts.

Dashboard​

Installed apps are automatically added to the personal dashboard. Frequently used apps can be pinned, and work alerts such as approval requests, unfinished tasks, and report status can be checked in one place.

Business App Examples​

Cards for field safety management, report automation, and KPI data lookup make it easier to compare which apps are being used in real work.

Watch the Guide Video​

The guide video below shows the basic screen flow of MiWork VibeHub.

Security and Governance​

Convenience is not enough for an enterprise platform. MiWork VibeHub also covers company-account SSO, role-based access control, install/run/access logs, and an app approval process.

By separating the roles of users, app creators, app owners, approvers, operators, and review managers, the platform balances business autonomy with company-level control.

Expected Benefits​

MiWork VibeHub brings business apps that were previously scattered across personal PCs or individual departments into an official channel. By publishing only approved apps and operating them based on usage data, the company can reduce shadow IT, encourage app sharing, and manage field ideas as digital assets.

Employees can run frequently used apps from one screen and reduce repetitive work. Business teams can turn ideas into apps faster and spread best practices across the company. IT and DX teams can manage the app portfolio based on usage data, and executives can see which innovation cases are actually being used.

Closing​

The goal of MiWork VibeHub is not simply to create more apps. It is to make internal mini apps easier to find, safer to install, and easier to scale based on real usage data.

For small ideas from the field to become company assets, organizations need not only tools for building apps but also a platform for collecting and operating them.

TL;DR A multimodal LLM can be useful as an assistant that quickly summarizes possible risk factors from site photos. Final safety judgment still needs verifiable criteria and human confirmation.

Testing safety inspection support with site photos and safety-rule prompts​

Field safety inspections still depend heavily on human experience and manual work. Inspectors need to review site photos, check protective equipment and risk factors, and then write the findings into a report.

Recent multimodal LLMs can understand images and text together. This PoC explored whether site photos and safety-rule prompts can be used to support safety checks and draft inspection reports.

PoC Goals​

The goal was not to make final judgments about a specific site or device. The goal was to check whether a multimodal LLM can act as an assistant for industrial safety inspection.

• Validate whether safety rules can be checked from site photos
• Check whether rules can be applied through prompts without separate model training
• Explore risk summarization and inspection report drafting
• Consider future expansion into video-based inspection flows

Approach​

The input flow was intentionally simple. A site photo was provided with a prompt containing safety rules and the desired output format. The model was asked to identify visible workers, protective equipment, and risk factors, then return a structured inspection result.

The advantage of this approach is that teams can experiment without first preparing a dedicated training dataset or a specialized vision model. By changing the prompt, the inspection criteria and output format can be adjusted quickly.

What We Found​

The PoC confirmed several useful possibilities.

• Helmet usage could be identified.
• Safety vest usage could be identified.
• The approximate number of workers in the photo could be estimated.
• Visible risk factors could be summarized.
• Inspection results could be organized into a report format.

There were also clear limits. Accuracy dropped with low-resolution images, distant workers could be missed, and small or partially hidden details such as safety harness fastening were difficult to judge reliably.

Insight​

In this test, the multimodal LLM was more suitable as an inspection assistant than as a final decision maker. It was useful for summarizing visible risk candidates and organizing the items that a human inspector should review.

Report drafting was especially promising. Instead of writing every finding from scratch after reviewing photos, inspectors can start from a generated draft and focus on final confirmation and correction.

Future Expansion​

The next step is to explore a flow that extracts snapshots from CCTV, drone, or mobile video and uses them for periodic safety inspection support.

The point is not to let AI replace all safety judgment. Field safety requires responsibility and context. A realistic starting point is to use AI to support repetitive inspection, summarize risk candidates, and help people focus on more important decisions.

Closing​

Multimodal LLMs show potential for interpreting site photos together with safety rules. They are not reliable enough to judge every situation on their own, but they are worth testing for safety inspection assistance and report automation.

In industrial safety, AI can start by helping people see faster and record more consistently, rather than replacing human responsibility.

References​

• NIST AI Risk Management Framework
• OWASP Top 10 for Large Language Model Applications

TL;DR Introducing LLMs is not just choosing a model. To make AI useful in daily work, teams need retrieval, permissions, review steps, and security practices designed as one operating system.

You have probably heard the phrase, "AI will take people's jobs." But inside a company, it is often hard to see how AI is actually used. Here is the practical story of what Minisoft experienced while bringing LLMs, or large language models, into our work over the past year.

How a Business Request Becomes an Output​

A work request does not end with the LLM alone. Internal data retrieval, human review, and final output validation are what make the result usable in real business.

What Is So Different About LLMs?​

Traditional chatbots answered fixed questions with fixed responses. If a user asked in a slightly different way, the answer was often, "Sorry, I did not understand."

LLMs are different. They understand sentences as people write them, read the context, and generate an answer. It feels a little like giving a new employee the entire manual and letting them answer freely.

What We Tried First - And Why It Failed​

Our first attempt was simple. We connected a public LLM API to an internal inquiry bot. The result was rough.

Problem 1: It Did Not Know Our Company​

When someone asked, "What is our vacation policy?", it answered with general advice. It had no way to know Minisoft's actual policy.

Problem 2: It Made Things Up Convincingly​

Sometimes it invented internal policies that did not exist. This is the familiar problem of hallucination. If an employee trusts the wrong answer, the damage can be real.

Starting Again with RAG​

The answer was RAG, or retrieval-augmented generation. The system searches internal documents first, then passes those materials to the LLM with a prompt such as, "Please answer using these sources."

Think of a librarian. When you ask about a topic, the librarian first finds the relevant books, then helps answer from those books. If the answer is not in the book, the system can say so honestly.

What Actually Changed​

New-hire Training Took Less Time​

New employees started asking the bot instead of searching the internal wiki. Questions like "How do I settle travel expenses?" or "My VPN is not connecting" became easier to resolve. Repeated HR and admin questions dropped by about 40%.

Meeting Notes Became Automated​

Upload a two-hour meeting recording, and within five minutes the system produces a summary, action items, and owners. The 30 minutes usually spent cleaning up notes after meetings disappeared.

AI Became the First Pass of Code Review​

On the engineering team, an LLM now performs the first pass when a pull request is opened. It catches obvious mistakes, security risks, and convention violations. Human reviewers can spend more time on the design decisions that matter.

What We Learned​

1. AI is a tool, not magic The key is deciding what to delegate to AI and what must stay with people.

2. Data cleanup is half the work For an LLM to answer well, internal documents must be organized. We had to clean up documents before the AI work could succeed.

3. Start small, then expand We did not try to automate everything at once. We solved one problem for one team first, then expanded after the effect was proven.

Closing​

LLMs are not magic. But used well, they reduce repetitive work and let people focus on more important decisions. In the next article, we will look more closely at how a RAG system is built.

References​

• NIST AI Risk Management Framework
• OWASP Top 10 for Large Language Model Applications

TL;DR mini Labs records how Minisoft prepares, tests, and applies AI and business software in real work. The focus is not technology change by itself, but how that change becomes practical products and workflows.

Two years of AI preparation, and a record of change that continues​

Over the past two years, we have been preparing for LLM-based AI technology.

At first, the work started with building simple chatbots. But over time, the questions changed.

• Can AI understand documents?
• Can AI understand images?
• Can enterprise data be connected safely?
• Can multiple AI Agents work together?

To answer these questions, we tested many technologies and carried out multiple PoCs and pilot projects.

Some experiments worked better than expected. Some were harder than expected. But every step helped us grow.

Technology trends change faster than expected​

The pace of technology change is fast. Something that felt cutting edge six months ago can quickly make room for a new approach.

After the rise of RAG, multimodal LLMs became more practical, and MCP is becoming a new standard for AI Agents.

Our internal technology stack is changing just as quickly.

Our company and solutions are evolving together​

We continue to upgrade our solutions in line with technology trends.

Starting from simple chatbots, we have expanded into RAG-based knowledge search, Vision AI, multimodal document understanding, MCP-based AI Agents, Enterprise Security, and Mini App Platform.

The solutions we build are less like finished products and more like platforms that continue to evolve.

What we want to record​

MiniSoft Labs is not a place for showing only finished answers.

It is a place to record which technologies we chose, what we tested, what worked, and what was difficult.

Technology keeps changing. So do we.

MiniSoft Labs will record that journey of change.