Corporate Training
AI App Builder Training in Malaysia
A hands-on programme where business teams learn to turn workplace pain points into working AI applications — from identifying the problem to building, testing, governing and deploying a practical solution. No software development background required. Participants leave with a working prototype built around a real problem from their own organisation.
Tools used
Bolt, Microsoft Power Automate, Supabase, OpenAI (ChatGPT), Anthropic Claude, Google Gemini, GitHub
Duration
Flexible — from a half-day introduction to a multi-day deep dive
Group size
Flexible — tailored to your organisation
Price
Depends on duration, group size and customisation
Delivery mode
In-person or online, in-house corporate delivery
What participants leave with
A working AI application prototype, workflow design document, prompt/instruction framework, testing checklist, governance controls, implementation notes
HRD Corp
HRD Corp claimable — contact us to confirm eligibility for your scheme
Audience
Who should attend
This programme is designed for professionals who understand a business problem and want to turn it into a working AI-powered application. Participants do not need to be professional software developers. The focus is on people who know their workflows, can articulate what is broken or slow, and want to build a practical solution — using AI-powered and no-code/low-code tools rather than traditional programming. If you have ever looked at a repetitive task and thought "there must be a better way," this training is for you.
Business owners and managers
Want to understand what AI applications can realistically do for their organisation and where to invest building effort.
Department heads
Need to identify which team workflows are good candidates for AI-powered applications and scope the effort involved.
Operations and process improvement teams
Looking to turn manual, time-consuming processes into automated AI-powered workflows that still have human oversight.
HR and L&D teams
Need to build internal AI capability and design training pathways that move teams from AI usage to AI building.
Digital transformation teams
Responsible for evaluating AI application platforms, running pilots, and building the internal case for broader adoption.
IT and technology teams
Need to bridge business requirements and AI application implementation, and understand governance for AI-built tools.
Analysts and knowledge workers
Process large volumes of information and want to build AI applications that help them retrieve, synthesise and act on it.
Professionals who want to build
Anyone who understands a business problem and wants to turn it into a working AI-powered application — no software development background required.
Practical outcomes
What participants build
Participants build applications around real workplace problems, not generic exercises. Below are examples of the types of applications participants can build during the programme. The specific application each participant builds is determined by their own organisation’s needs and the use case they bring to the programme.
Internal knowledge assistant
Problem: Employees waste time searching for information across documents, wikis and shared drives.
What it does: An AI application that answers internal questions by retrieving and synthesising content from approved knowledge sources.
AI document Q&A application
Problem: Teams need to extract answers from long documents, contracts or reports quickly.
What it does: An application where users upload a document and ask questions about its contents, with cited answers.
Customer enquiry assistant
Problem: Support teams spend time answering the same categories of enquiry.
What it does: An AI assistant that classifies enquiries, drafts responses and routes complex cases to a human.
HR policy assistant
Problem: HR teams answer the same policy questions repeatedly.
What it does: An application that answers employee questions about leave, benefits and policies using approved HR documents.
Lead qualification application
Problem: Sales teams spend time qualifying leads manually before prioritising outreach.
What it does: An AI application that reviews incoming enquiries, scores them against qualification criteria and recommends next steps.
Feedback analysis application
Problem: Large volumes of customer or employee feedback are hard to synthesise manually.
What it does: An application that categorises, summarises and surfaces themes from qualitative feedback.
Report generation workflow
Problem: Compiling recurring reports from multiple data sources is time-consuming.
What it does: An AI-powered workflow that gathers data, formats it into a standard report and distributes it to stakeholders.
Internal workflow automation
Problem: Multi-step internal processes involve manual handoffs and lost context.
What it does: An AI application that coordinates a workflow — gathering inputs, processing data and prompting the next human step.
AI-powered form and approval workflow
Problem: Form submissions and approval processes are slow and manual.
What it does: An application that captures form data, processes it with AI, routes it for approval and updates the requesting system.
Department-specific AI assistant
Problem: Each department has specialised tasks that a general AI tool does not handle well.
What it does: A custom AI assistant built around a specific department’s documents, workflows and terminology.
The journey
The problem-to-app workflow
Building an AI application is not a single step — it is a structured journey from problem to deployed solution. The programme follows this complete workflow, and every participant moves through each stage for their own use case. See also our vibe coding training for a complementary building-focused programme.
Identify the use case
Pinpoint a real workplace problem that is worth solving with an AI application. Not every problem needs AI — this step filters for the right ones.
Map the workflow
Document the current process step by step — inputs, actions, decision points, outputs and handoffs.
Design the AI solution
Decide what the AI application should do, what data it needs, where humans stay in control and what the output looks like.
Build the application
Use AI-powered and no-code/low-code tools to build a working application from the design.
Connect data and tools
Integrate the application with your data sources, documents, APIs and business systems so it works on real information.
Test the application
Run test cases, check outputs for accuracy, identify failure modes and refine the application until it behaves reliably.
Apply governance controls
Add human review checkpoints, data access limits, audit logging and usage policies so the application operates responsibly.
Deploy
Move the tested application to a live environment where the intended users can access it.
Measure and improve
Monitor performance, gather user feedback and iterate on the application to keep it useful over time.
Curriculum
Skills covered
Participants learn practical skills for every stage of the problem-to-app workflow — explained in business language, with hands-on application at each step.
- AI use-case identification — recognising which problems are worth solving with AI and which are not
- Problem definition — articulating the business problem clearly enough to design a solution around it
- Workflow mapping — documenting current processes to identify where AI adds value
- Prompt engineering — writing clear, structured instructions that AI models can follow reliably
- AI application design — deciding what the app does, what data it needs and how users interact with it
- No-code/low-code application building — building functional applications without traditional programming
- AI workflow automation — connecting AI models to business processes to automate multi-step tasks
- Data and knowledge integration — connecting applications to documents, databases and knowledge sources
- API and tool integration concepts — understanding how applications connect to external services
- Testing and debugging — verifying that the application works correctly and fixing issues
- User experience design — making the application easy and intuitive for non-technical users
- AI output evaluation — judging whether AI-generated outputs are accurate, useful and safe
- Security and access considerations — protecting data, credentials and user access
- Data governance — handling data responsibly within the application
- Deployment planning — preparing the application for real organisational use
- Documentation — recording how the application works so it can be maintained
- Continuous improvement — monitoring and refining the application after deployment
Ecosystem
Tools used, named
The programme draws from a range of AI, no-code and backend tools. Which tools are used in a specific cohort depends on the use case and programme configuration — not every participant needs to purchase or use every tool listed below. Participants may use Microsoft Power Automate to connect AI models, business systems, APIs and automated workflows as part of their application build.
Bolt
AI-powered full-stack app builder that generates and deploys working applications from natural language descriptions. Used for rapid prototyping and application building.
Microsoft Power Automate
Workflow automation platform for connecting AI models, business systems, APIs and automated workflows. Participants may use Microsoft Power Automate to build the automation layer of their AI applications.
Supabase
Open-source backend platform providing database, authentication and storage for AI applications that need persistent data.
OpenAI (ChatGPT)
AI reasoning engine for generating text, answering questions, classifying inputs and powering application logic.
Anthropic Claude
AI reasoning engine particularly strong for long-document analysis and careful instruction following.
Google Gemini
AI reasoning and generation within Google Workspace ecosystems.
GitHub
Version control and code management where relevant for teams that need to track application changes or collaborate on builds.
APIs and webhooks
Connect AI applications to external services, data sources and business systems for real-time data exchange.
Tool licensing and costs
The training environment provides access to the tools needed for exercises. However, software subscriptions, API usage, hosting and paid integrations may be separate costs depending on your organisation\'s setup. We clarify what is included during pre-programme scoping.
Takeaways
Individual and capstone outputs
Individual outputs
Each participant leaves with practical artefacts from their own use case:
- A defined AI use case
- A working prototype or application component
- A prompt or instruction framework
- A workflow design document
- A testing checklist
- Basic documentation
- Implementation notes for next steps
Capstone output
Participants can combine their skills into a practical end-to-end AI application based on a real organisational problem. The capstone follows the full journey:
The capstone should demonstrate a practical business outcome — not just a technical exercise, but an application that addresses a real problem in a usable way.
Responsibility
Testing and governance
Building an AI application is not complete until it can be tested, governed and safely used. This section of the programme ensures participants understand how to validate their application and deploy it responsibly — not just build something that looks like it works.
Functional testing
Verify that the application does what it was designed to do — every feature works end to end.
Prompt testing
Test the AI instructions with varied inputs to ensure consistent, reliable outputs across different scenarios.
Output quality testing
Evaluate whether AI-generated outputs are accurate, useful and appropriate for the business context.
Error handling
Design the application to catch errors, fail safely and notify the right people rather than silently producing wrong results.
User acceptance testing
Have real users test the application before deployment to confirm it meets their needs.
Data access controls
Restrict what data the application can read and what actions it can take — following least-privilege principles.
Privacy considerations
Ensure the application handles personal and sensitive data in line with your organisation’s policies and applicable Malaysian data protection requirements.
Sensitive information handling
Prevent the application from exposing or leaking confidential information through its outputs.
Authentication and authorisation
Ensure only the right people can access the application and that actions are tied to identified users.
Human oversight
Design checkpoints where a human reviews, approves or overrides AI outputs before they take effect.
AI output review
Establish processes for reviewing AI-generated content before it reaches end users or triggers business actions.
Auditability and documentation
Log application actions, inputs and outputs so every action can be traced and reviewed after the fact.
Responsible AI usage
Use AI in ways that are transparent, fair and appropriate for the business context — not for tasks where AI is unreliable.
Monitoring after deployment
Track the application’s performance, usage and accuracy over time so problems are caught early.
From prototype to production
Deployment pathway
A training prototype is not the same as a production-ready organisational application. The programme teaches participants the pathway from prototype to real use, and the review steps required along the way. Participants do not leave with a production-ready system — they leave with a working prototype and a clear roadmap for getting there.
Prototype
A working application built during the training programme. It demonstrates the concept but has not been through organisational review.
Internal testing
Test the prototype with sample data and a small group of users to identify issues before wider use.
User feedback
Gather feedback from internal users on usability, accuracy and missing features.
Security/governance review
IT or security teams review data access, authentication, credentials and compliance before approval.
Pilot deployment
Deploy to a limited group of real users in a controlled environment to validate the application in practice.
Production deployment
Roll out to the full intended user base with monitoring, documentation and support in place.
Monitoring
Continuously observe performance, usage and accuracy. Catch and fix issues before they cause business harm.
Continuous improvement
Iterate on the application based on monitoring data and user feedback to keep it useful over time.
Track record
Client evidence
See how organisations are applying AI to practical workplace challenges.
Client evidence coming soon
We are compiling genuine client outcomes from organisations we have worked with. In the meantime, explore our client stories for real examples of how participants moved from AI users to builders.
Practicalities
Duration, delivery format and price band
Duration
Flexible duration options depending on depth required.
Delivery format
Hands-on workshop format. Online or face-to-face. Corporate/in-house delivery.
Typical audience size
Flexible — tailored to your organisation.
Investment
Pricing depends on duration, group size, customisation and delivery format. Contact us for a tailored quotation.
Customisation
The programme is tailored based on your organisation’s use cases, preferred tools and governance requirements. Contact KadoshAI for a tailored quotation.
Frequently asked questions
- No. The programme is designed for business professionals who understand a workplace problem and want to turn it into a working AI-powered application. Participants use no-code and low-code tools alongside AI to build functional prototypes without writing traditional code.
Build with KadoshAI
Bring a real workplace problem. Build a practical AI solution. KadoshAI is a practical partner for organisations that want to move from AI awareness to working applications — not slide decks and theory, but real prototypes your team can use.
You can also explore The SHIFT pathway, browse AI applications we have built, or read client stories from organisations we have worked with.