Training Catalogue · 2026/27

AI and data training your teams can use the next working day.

From a first introduction to AI, to agents running in production. From data governance to AI governance. Every course is hands-on, uses your own work and data, and is taught in Thai or English by people who build these systems every day.

Browse courses Start with a readiness assessment

Hands-on, case-based

Polls, exercises and a real project in every course. Most of the time is spent practising.

Built on your work

Adjusted to your goals, your maturity stage and your own anonymised data.

Taught by practitioners

The same consultants and engineers who deliver Data Wow's AI and data projects. In Thai or English.

A partner beyond the course

We keep supporting your AI and data projects after the training ends.

Principle

Every program is designed around your AI maturity stage

Where your organisation is today decides which program fits. The catalogue follows four stages, from basic awareness to AI leadership, and each stage prepares the next. Click a stage to see its courses.

Stage 1

Awareness & Understanding

AI Innovation Readiness Model AssessmentAI Fundamentals Bootcamp

Objective

Get a clear picture of current AI readiness and a direction
Build foundational knowledge of AI across the workforce
Get people interested and show why it matters now

Outcome

The organisation knows where it stands on AI and has a strategy. Staff understand the basics of AI and want to learn more.

Stage 2

Adoption & Experimentation

AI Pilot Program Development

Objective

Build the skills to experiment with AI on real work
Reduce fear of change
Test what works and learn from the first use cases

Outcome

The first AI projects prove the idea works and show where AI helps most.

Stage 3

Integration & Optimization

AI Scaling Program

Objective

Bring AI into core business processes
Optimise and scale across departments
Improve teamwork across departments and governance

Outcome

AI is part of daily work and improves speed and decisions.

Stage 4

Innovation & Leadership

AI Accelerator Program

Objective

Build a culture where AI innovation keeps going
Make the organisation a leader in AI-driven change

Outcome

The organisation is seen as an industry leader in AI and keeps innovating.

Where we start

AI Readiness Assessment

We look at the whole organisation to see how ready it is for AI. The result tells us which training fits and gives you a roadmap and plan. The assessment scores twelve areas across technology and organisation and rates each one on a three-level scale.

Technological competence
Governance	Data governance · Ethics & compliance
Data	Data readiness · Data management
Foundation	Infrastructure readiness · Process & procedure
Organizational competence
Workforce	Talent competency · Cross-functional collaboration
Leadership	Corporate strategy & goal · AI awareness & engagement
Culture	Employee adaptability · Tech embrace

CapableThe basics are in place. People use AI on their own, when they think of it.Start at stage 1–2DrivenTeams run AI projects with sponsors and measure the results.Start at stage 2–3ActivatedAI is part of how the organisation works and competes.Start at stage 3–4

This is course P01, the AI Innovation Readiness Model Assessment: a survey, interviews and a document review, followed by a report and a recommended training plan.

How it works

From first call to results in six steps

Every engagement follows the same path, whether it is a two-hour briefing or a twelve-week program. Most of the work before the day is ours; we ask for about 30 minutes of your time and a few sample files.

Discovery call

Tell us the team, the problem and the date. We suggest a path.

30 minutes

Readiness and pre-work

A short online input from participants, or a full readiness assessment for larger programs.

10 min per person

Tailoring

We rebuild cases, demos and labs on your sector and anonymised sample data, and test the tools on your network.

1–2 weeks before

Hands-on session

Short primers, then practice on real work, with a lead facilitator and teaching assistants in the room.

The day

Output pack

Typed-up outputs, templates, facilitator notes and each person's 30-day action.

Within 20 working days

Follow-up

Practice labs, office hours and support on the projects that follow the training.

2–4 weeks after, and ongoing

What a one-day workshop looks like

A typical six-hour day. Black blocks are short talks of about 20 minutes, mint blocks are hands-on labs on your own tasks, and the violet block is the capstone where teams produce the output they take home.

OpenLab 1 · 40mTalkLab 2 · 45mBreakTalkLab 3 · 40mLunchTalkLab 4 · 45mBreakCapstone · 60mClose

Talk and primers (about 30%)Hands-on labs (about 50%)Capstone (about 20%)Breaks

Course catalogue

Courses

Courses are grouped by track (the type of learning) and tagged with the maturity stage they serve. We adjust every course to your sector, data and tools before the day. Durations are teaching time without breaks. Open a card to see modules, prerequisites and what you receive.

Maturity

Audience

Format

49 of 49 courses

Track 1 · AI Awareness & Leadership

A shared vocabulary, realistic expectations and a decision on where to start with AI. No technical background needed.

A01Foundation

AI for Executives: Opportunity Discovery & Workflow Transformation

Leaders redesign one function they own, rank AI opportunities by impact and feasibility, and commit to one action within 30 days.

1 day · 6 hoursUp to 40Stage 1 · 2

Leaders

You will be able to

Explain what AI, machine learning and generative AI can and cannot do, in business terms
Sort the team's tasks into automate, support with AI, or keep human
Leave with a ranked shortlist of use cases and a written first action for the next 30 days

Modules

What is AI in simple terms: one shared vocabulary
AI across your value chain, with live demonstrations on your own task inventory
Success stories and failures from Thai financial services and related industries
Function teardown and AI Innovation Canvas (cross-functional, two hours)
Workflow presentations and cross-team synthesis
Safe AI in practice: governance, market conduct and PDPA, applied to the ideas from the workshop

Prerequisites

10-minute pre-workshop input: AI readiness pulse, top-10 recurring tasks per team, one friction point. A named sponsor to open the session.

What you receive

Workshop output pack (all canvases typed up), function heat map, 30-day action list, facilitator observations.

A02Foundation

AI for Leaders: Validating Decisions & Driving Productivity with AI

Managers use AI to check their decisions: review their team's workflow, test proposals and design one measurable pilot.

1 day · 6 hoursUp to 40Stage 1 · 2

Leaders

You will be able to

Find at least three tasks in their team where AI cuts manual work
Use an AI assistant to analyse a dataset, then re-check the output before trusting it
Use AI as a reviewer to challenge proposals and find blind spots

Modules

Spotting AI opportunities: workflow audit of real team tasks
Validating decisions with data and AI (signature case built on your business)
The review room: "devil's advocate" prompting to stress-test a proposal
Safe AI and designing your pilot: PII, fairness, output validation, a metric to track in 30 days

Prerequisites

Comfortable with Excel or standard departmental reports. Microsoft 365 Copilot, Claude or ChatGPT access provisioned a week ahead. An anonymised sample dataset from the client.

What you receive

One measurable AI pilot per participant, a governance checklist, workflow-opportunity map.

A03Foundation

Introduction to LLMs & Generative AI

The session for all staff: how large language models work, where they go wrong, and how to use them safely from tomorrow.

Half-day · 3 hours (or 2-hour keynote)Up to 100Stage 1

LeadersBusiness usersDevelopers & IT

You will be able to

Describe how an LLM produces an answer and why it sometimes makes things up
Write a structured prompt and judge the output with a critical eye
Apply the organisation's do and don't rules for AI tools and personal data

Modules

From data to AI to generative AI: a plain-language map
How LLMs work, tokens, context and why confident answers can be wrong
Hands-on: first prompts on everyday work (summarise, draft, analyse, translate)
Safe use: what never goes into a public AI tool, PDPA basics
Where this is going: agents, copilots and what it means for your role

Prerequisites

None. Laptop or phone with a chat AI tool for the hands-on segment.

What you receive

Slides, one-page safe-use guide, starter prompt sheet.

A04Foundation

Executive Briefing: Agentic AI and What It Changes for Your Business

A briefing for the board on how AI has moved from chat to agents in the last 12 months, and the decisions leadership now has to make.

Keynote · 1.5–2 hoursBoard / ExCoStage 1 · 4

Leaders

You will be able to

Distinguish chat assistants, copilots and autonomous agents, and the risk profile of each
Recognise the three or four agent patterns that apply to your sector
Know which governance questions to ask before approving an AI project

Modules

The shift from generation to action: what an agent is
Live demonstration of an agent working on a realistic business task
Where agents pay in banking, utilities, logistics and retail
Risk, accountability and the regulator's view
Questions for the board, with a one-page checklist

Prerequisites

None.

What you receive

Briefing deck and board checklist.

A05Foundation

AI Strategy & Roadmap Workshop

Turn ambition into a funded, ordered AI portfolio: business case, priorities, operating model, build or buy, and the first year of investment.

1 day · 6 hoursUp to 25Stage 1 · 2

Leaders

You will be able to

State the AI ambition as business outcomes over three years
Rank use cases by value, feasibility, data readiness and risk
Decide the operating model: central team, federated, or hybrid, and the roles it needs

Modules

Where AI creates value in your sector, with evidence from comparable organisations
From use-case long list to portfolio: scoring and sequencing
Operating model, roles and partner strategy (build, buy, partner)
Data, platform and governance prerequisites by stage
Business case and budget: cost of models, people and change
Roadmap workshop: 12-month plan with milestones and owners

Prerequisites

Executive sponsor and the leadership team that owns the budget. Readiness assessment results if available.

What you receive

AI strategy on a page, prioritised portfolio, operating model recommendation, 12-month roadmap.

A06Foundation

Leading AI Change: Adoption and Change Management for Managers

What managers need to do to make AI stick: change roles and routines, handle resistance, set expectations and measure adoption in their own team.

Half-day · 3.5 hoursUp to 40Stage 2 · 3

LeadersBusiness users

You will be able to

Explain to their team what changes, what does not, and why
Spot and address the three common sources of resistance
Run a 30-day adoption plan with simple metrics for their team

Modules

What AI changes in a team's work and what stays human
Resistance: skill, trust and job security, and what works for each
Redesigning routines, roles and performance expectations
Communication kit for managers
Adoption metrics and the 30-day plan

Prerequisites

Managers whose teams are about to adopt or have adopted AI tools.

What you receive

Manager communication kit, adoption plan template, metrics sheet.

Track 2 · Applied AI for Business Teams

Safe, daily use of AI and data on your team's own work: prompting, checking results, analysis, storytelling, and the tools you already pay for.

B01Foundation

Generative AI at Work: Prompting that Works

Replace trial and error with a repeatable prompt structure, a checklist for checking AI output, and a personal prompt library built on real tasks.

1 day · 6 hoursUp to 40Stage 2

Business users

You will be able to

Write reliable prompts using the RACE structure: Role, Action, Context, Expectation
Check AI output for accuracy, relevance, made-up content and safe use before acting
Redo one recurring task end to end with AI and measure the time saved

Modules

The three objections, answered honestly: "I don't know how", "it isn't safe", "other tools are better"
Prompting lab: RACE, few-shot examples, forcing output formats, iterating
Try first, then accelerate: the same task done the old way and the AI way, timed
AI across documents, spreadsheets, email and meetings
Trust: a 4-point checklist, tested on an output with a planted error
Capstone: a one-page output ready for a decision, from a typical Monday-morning brief

Prerequisites

Laptop and an AI tool account (ChatGPT, Claude, Gemini or Microsoft 365 Copilot). Written data-confidentiality guidance from the client. We can draft it if you do not have one.

What you receive

Personal prompt library, validation checklist, one measured productivity gain.

B02Foundation

Microsoft 365 Copilot in the Flow of Work

Move Copilot out of the chat window and into Excel, Word, PowerPoint, Outlook and Teams, using your own files and your own week.

1 day · 6 hoursUp to 40Stage 2

Business users

You will be able to

Ask questions of a sales dataset in Excel without writing formulas
Turn an analysis into a first-draft one-pager or deck, then critique it
Produce meeting recaps, action lists and follow-ups from Teams and Outlook

Modules

What happens to a prompt inside the Microsoft 365 tenant boundary
Copilot Chat versus Copilot in apps: when to use which
Excel lab: summarise, compare, find the drift
Word and PowerPoint lab: draft, restructure, critique
Outlook and Teams lab: recap, extract actions, draft the reply
End-to-end timed task: meeting, dataset and deadline to deliverable

Prerequisites

Microsoft 365 Copilot licences verified per participant; Copilot endpoints not blocked by proxy. Licence type confirmed in writing.

What you receive

App-by-app prompt sheet, one rebuilt personal workflow with before/after timing.

B03Intermediate

AI Champions Camp: Train the People Who Make AI Stick

Build your internal advocates. Champions leave with a prompt library, a proven use case and a rehearsed 30-minute session for their own team.

1 day + 3-hour online practice labUp to 20Stage 2 · 3

Business usersLeaders

You will be able to

Demonstrate a role-relevant AI use case confidently to colleagues
Answer the three adoption objections with evidence
Run a 30-minute team session using a prepared toolkit and the Show, Do, Coach model

Modules

Opening and the three objections
Prompting lab and prompt-fixing clinic (the coaching move they will reuse)
AI in the flow of work, with a timed before/after
Role-track use-case lab with a facilitator per track
Trust and validation
Capstone: the Champion challenge
Champion enablement: teaching others, paired teach-back, first session date agreed
Online lab 2–4 weeks later: friction clinic, two teach-back rounds, 30-day adoption plan

Prerequisites

Named Champions with a function and an intended internal audience, confirmed two weeks ahead. Sanitised sample material per track.

What you receive

Champion Toolkit: prompt library mapped to functions and a ready-to-run 30-minute session pack (slides, script, timings).

B04Intermediate

AI for Finance & Accounting Teams

Month-end, variance analysis, reconciliations and management reports with Claude or Copilot as the first-pass analyst. A person still signs off.

1 day · 6 hoursUp to 30Stage 2 · 3

Business users

You will be able to

Use AI to explain variances, draft commentary and reconcile two data sources
Build reusable prompts for recurring finance tasks and review outputs against controls
Decide which finance tasks are safe to delegate to AI and which stay with a human

Modules

Five finance use cases shortlisted with your team before the day
Spreadsheet analysis lab: variance, trend and outlier questions in plain language
Document lab: contracts, invoices and policy extraction
Reporting lab: from numbers to board commentary
Controls: audit trail, segregation of duties, what AI output must never bypass
Pilot design: one use case, one metric, 30 days

Prerequisites

Anonymised sample ledgers or reports from the client. AI tool access with data protection confirmed by IT.

What you receive

Finance prompt library, use-case scorecard, pilot plan.

B05Intermediate

AI for Sales & Marketing: From Data to Sales Impact

Sales planning, account reviews, channel analysis and campaign content with AI on your own sales data, plus simulations that show what moves the numbers.

1 day · 6 hoursUp to 40Stage 2 · 3

Business users

You will be able to

Use AI to find the accounts, SKUs and channels that are slowly slipping
Turn a data cut into a field-team brief or quarterly review story with a sized opportunity
Draft and test marketing briefs and campaign content against a brand standard

Modules

Exploratory analysis on a sales dataset without code
Sell-in versus sell-out and distributor performance
Account review and QBR narrative: findings, opportunity, next steps
Marketing track: digesting a market report, sharper agency briefs, competitive scan
Simulation lab: what-if scenarios on pricing, mix and coverage
Safe use with customer data

Prerequisites

Anonymised sales or campaign data from the client. Spreadsheet comfort.

What you receive

Analysis prompt set, QBR template, one completed account or campaign brief per group.

B06Intermediate

Data Storytelling: From Numbers to Narrative

Cut a report down to the few numbers that matter, find the 'why', estimate the impact honestly, and deliver a 3-minute answer-first story on one shared canvas.

1 day · 6.5 hoursUp to 50Stage 2 · 3

Business usersLeaders

You will be able to

Select the three numbers that would change an executive's decision and cut the rest
Run a repeatable diagnostic drill (slice, compare, time, isolate) before claiming a cause
Present and defend a recommendation, answer first, and say how confident you are

Modules

The vital few: signal versus noise, vanity versus decision metrics
Find the why: the four-step drill and saying how sure you are
The so-what ladder and a rough impact estimate with the assumption written down
Answer-first structure: SCQA and the Pyramid Principle
One standard: the Data Story Canvas and delivery under pressure
Deliver and defend: 3-minute stories with expert pressure-testing

Prerequisites

Pre-work diagnostic (15 minutes) so we can level the room. Managers fluent in their own KPIs; no tools required.

What you receive

Data Story Canvas (shared standard), diagnostic drill card, story scorecard, synthetic practice dataset.

B07Foundation

Data Literacy Foundations: Reading, Questioning and Using Data

What each metric means, how to compare it fairly, and how to ask a data question that gets a useful answer. The foundation every other course builds on.

Half-day · 3 hoursUp to 40Stage 1

Business users

You will be able to

Read a KPI report and explain what moved, against what baseline
Avoid the common traps: averages, small samples, correlation as cause
Frame a business question so an analyst or an AI tool can answer it

Modules

Metrics, dimensions and baselines
Comparisons that mislead and how to spot them
From question to analysis plan
Hands-on with a synthetic business dataset

Prerequisites

None.

What you receive

KPI glossary template, question-framing worksheet.

B08Intermediate

AI-Assisted Data Analysis for Business Users

Explore data, make charts and build simple models on your spreadsheets with AI assistants, with checks that stop a wrong answer reaching a decision.

1 day · 6 hoursUp to 30Stage 2

Business usersData & AI teams

You will be able to

Profile a dataset, find quality issues and summarise it with an AI assistant
Make charts and compare segments without writing code
Check AI-generated analysis by working out one key number by hand

Modules

Exploratory data analysis as a conversation
Cleaning and joining data with AI help
Visualisation that answers a question
Simple forecasting and what-if simulation
Validation habits and when to call a data scientist

Prerequisites

Spreadsheet comfort. AI tool with file analysis enabled.

What you receive

Analysis playbook, prompt set, one completed analysis on your own data.

B09Intermediate

AI for HR & People Teams

Recruiting, onboarding, policy questions, learning content and people analytics with AI, within the fairness and PDPA limits that HR data requires.

1 day · 6 hoursUp to 30Stage 2 · 3

Business users

You will be able to

Draft job descriptions, interview guides and onboarding plans from role profiles
Build a policy assistant over HR documents and know what it must not answer
Analyse engagement and attrition data with AI and validate the findings

Modules

HR use-case map: hire, onboard, develop, engage, retain
Recruiting lab: screening support without automated rejection
Policy and employee-question assistant on your documents
Learning content and skills frameworks with AI
People analytics lab with validation habits
Fairness, bias and PDPA for employee data

Prerequisites

Anonymised HR documents and sample data. AI tool access approved for HR use.

What you receive

HR prompt library, policy assistant prototype, fairness and PDPA checklist for HR AI.

B10Intermediate

AI for Customer Service & Contact Centres

Agent assist, knowledge search, quality review of every call and chat, and chatbot handoff design, built on your own transcripts and knowledge base in Thai.

1 day · 6 hoursUp to 30Stage 2 · 3

Business usersDevelopers & IT

You will be able to

Use AI to answer, summarise and draft replies from the knowledge base while talking to a customer
Review every call or chat against a conduct and quality checklist, not just a sample
Decide what a bot handles, when it hands over to a person, and how to measure it

Modules

Customer service value chain and where AI pays
Knowledge base readiness: what makes answers trustworthy
Agent-assist lab on real (anonymised) conversations
Quality and conduct review at full coverage
Bot design: intents, escalation, tone, Thai language considerations
Metrics: handling time, first-contact resolution, QA coverage, CSAT

Prerequisites

Anonymised transcripts and the current knowledge base. Supervisors and team leads in the room.

What you receive

Agent-assist prompt set, QA checklist and scoring template, bot design canvas.

B11Intermediate

AI for Operations & Supply Chain

Demand forecasting, maintenance logs, incident notes, SOPs and planning with AI, for plant, logistics and operations teams who work from spreadsheets and shift reports.

1 day · 6 hoursUp to 30Stage 2 · 3

Business users

You will be able to

Turn messy shift, maintenance and incident notes into structured data and actions
Run and sanity-check a simple forecast with an AI assistant
Find the five operations processes with the strongest case for automation

Modules

Operations use-case map: plan, source, make, deliver, maintain
Text-to-structure lab: incident and maintenance logs
Forecasting and inventory questions in plain language, with validation
SOP and work-instruction generation and review
Automation candidates and severity-based escalation design
Safety, quality and compliance boundaries

Prerequisites

Anonymised operational logs and reports. Spreadsheet comfort.

What you receive

Operations prompt library, structured-log template, automation candidate shortlist.

B12Intermediate

AI for Legal, Procurement & Compliance

Contract review, clause comparison, terms of reference, vendor evaluation and regulatory summaries, with AI as a first reviewer under strict confidentiality rules.

1 day · 6 hoursUp to 25Stage 2 · 3

Business users

You will be able to

Review and compare contracts for key clauses, deviations and missing terms
Draft terms of reference, evaluation criteria and vendor questionnaires
Summarise regulatory updates into short notes on what they mean for the business

Modules

What legal and procurement work AI can and cannot be trusted with
Contract review lab: extraction, comparison, risk flags
Procurement lab: TOR, scoring criteria, bid comparison
Regulatory monitoring and implication notes
Confidentiality, privilege and vendor data handling
Review and sign-off workflow with AI in the loop

Prerequisites

Anonymised contract and TOR samples. Enterprise AI tool with data protection confirmed.

What you receive

Review checklists, prompt library, TOR and evaluation templates.

B13Intermediate

AI for Internal Audit, Risk & Controls

Test full populations, find anomalies, review controls and draft audit reports with AI, while keeping evidence, independence and the audit trail intact.

1 day · 6 hoursUp to 25Stage 2 · 3

Business usersData & AI teams

You will be able to

Test a full population, not just a sample, with AI-assisted analysis
Draft findings and reports where every point links back to evidence
Audit AI systems themselves

Modules

Audit and risk use-case map
Data analysis lab: full-population tests and anomalies
Document lab: policies, controls, walkthrough notes
Report drafting with evidence links
Auditing AI: what to ask of a model, a prompt and a vendor
Independence, evidence and tool governance

Prerequisites

Anonymised audit data and working papers. Auditors and risk officers in the room.

What you receive

Audit analytics prompt set, report template, AI audit checklist.

B14Intermediate

AI Product Management: Shipping AI Features that Work

For product owners and managers: define AI features, write requirements that handle uncertainty, set evaluation criteria and estimate cost before engineering starts.

1 day · 6 hoursUp to 25Stage 2 · 3

Business usersDevelopers & IT

You will be able to

Write a requirements document for an AI feature with success metrics, failure cases and guardrails
Design the user experience for output that is sometimes wrong
Estimate and manage the cost and response time of an AI feature

Modules

What is different about AI products
Discovery: problems worth an AI solution
PRD lab: acceptance criteria, evaluation sets, fallback behaviour
UX for AI: trust, feedback, confidence and recovery
Cost, latency and vendor choices
Launch, monitor and iterate

Prerequisites

Product owners, product managers and business analysts with a candidate feature.

What you receive

AI PRD template, evaluation plan template, cost model worksheet.

Track 3 · Build & Automate

Hands-on building: workflow automation, RAG, AI agents and AI-assisted engineering. Participants leave with something that runs.

C01Intermediate

Workflow Automation with n8n for Business Users

Non-technical teams build working automations from templates: triggers, routing, conditional logic and an AI step, on their own repetitive tasks.

1 or 2 days · 6–12 hoursUp to 30Stage 2 · 3

Business usersDevelopers & IT

You will be able to

Replace a repetitive manual task with an automated workflow that needs little upkeep
Build multi-branch conditional logic such as severity-based escalation
Add an AI step that pulls structured data out of messy text

Modules

Day 1: nodes, triggers and the template-first method (reverse-engineer a working blueprint)
Table, JSON and schema literacy for non-technical users
Conditional logic: IF, Switch and expressions on a real escalation case
Day 2: prompting inside a workflow and forcing valid JSON output (AI-assisted, not hand-coded)
Error handling: retries, fallbacks and safety nets
MVP build sprint on a real workflow from your team, with checkpoints and a defined "done"

Prerequisites

Working n8n environment and LLM credentials per participant, tested on your network before the day. Spreadsheet comfort. A 30–45 minute discovery call to seed real bottlenecks.

What you receive

Working automation per team, importable checkpoint workflows, glossary and cheat sheet.

C02Advanced

Building AI Agents and RAG with n8n

Technical teams build chat over company documents (RAG) and tool-using agents in n8n, backed by PostgreSQL, with PDPA built in.

2 days · 12 hoursUp to 25Stage 3

Developers & ITData & AI teams

You will be able to

Build a RAG pipeline: ingestion, chunking, embeddings, retrieval and answer generation
Design an agent that calls tools and databases with guardrails
Apply PDPA controls to documents and prompts flowing through the system

Modules

n8n for engineers: credentials, sub-workflows, error flows
RAG end to end over a document set from your organisation
PostgreSQL as memory and system of record
AI agents: tools, memory, routing and human-in-the-loop approvals
Evaluation: did the answer come from the documents?
PDPA and data minimisation for AI workflows

Prerequisites

Basic SQL and API familiarity. n8n, PostgreSQL and LLM API access provisioned.

What you receive

Working RAG and agent workflows, evaluation sheet, PDPA checklist for AI workflows.

C03Intermediate

AI for Developers: AI Across the Software Development Life Cycle

From requirements and user stories to design, code, tests, debugging and documentation, with ChatGPT, Claude, Copilot and Gemini as a pair programmer you still review.

1 day · 6 hoursUp to 50Stage 2

Developers & IT

You will be able to

Draft BRDs, functional specs and user stories with acceptance criteria from a business brief
Generate, explain, refactor and review code in the IDE and verify it runs
Create test cases across happy, edge and negative paths and trace a root cause from a stack trace

Modules

Prompting for developers: role, context, task, format, constraints
Requirements and business-process analysis with AI
Database and API design: schema, DDL, OpenAPI first drafts
Write, explain, refactor and review code (hands-on in IDE)
Test cases, test data and root-cause analysis
Meeting minutes and project documentation
Limits, PDPA and data security; capstone: a one-page AI usage guideline for your team

Prerequisites

Laptop with IDE; any AI assistant account (free tier works). Sample legacy code and anonymised meeting notes provided by us.

What you receive

Workbook and prompt library, sample files, AI usage guideline template.

C04Advanced

Agentic Coding with Claude Code

Go from autocomplete to delegating whole tasks: plan, build, test and review with an agentic coding tool on a real repository, safely.

1 day · 6 hoursUp to 25Stage 3

Developers & IT

You will be able to

Hand a multi-file change to an agent with a clear spec and review the result
Set up project instructions, permissions and guardrails so the agent follows your rules
Use agents for refactoring, test generation and codebase onboarding

Modules

How agentic coding differs from chat and autocomplete
Project context: instructions, conventions and memory files
Plan, then execute: writing specs an agent can act on
Lab: feature, bug fix and test suite on a sample codebase
Review discipline, secret hygiene and what never leaves the repo
Team adoption: pull-request workflow, cost and governance

Prerequisites

Working developers. Claude Code or equivalent agentic tool licensed; Git familiarity.

What you receive

Project instruction templates, review checklist, adoption guide.

C05Advanced

Agentic AI Engineering: Designing, Building and Evaluating Agents

The engineering course for teams putting agents into production: tool use, orchestration, MCP, memory, evaluation, guardrails and cost control.

2 days · 12 hoursUp to 25Stage 3 · 4

Developers & ITData & AI teams

You will be able to

Design an agent architecture that matches the task: single agent, workflow or multi-agent
Build tool calling, structured output and human approval steps
Build an evaluation set and guardrails before anything goes live

Modules

Agent patterns: routing, orchestrator-worker, evaluator-optimiser
Tool design and Model Context Protocol (MCP) integrations
State, memory and long-running tasks
Evaluation: test sets, LLM-as-judge, regression on prompts
Guardrails: prompt injection, data leakage, scope limits, rate and cost controls
Observability and operating agents in production
Capstone: an agent for a real internal process, evaluated and demoed

Prerequisites

Python or TypeScript developers. LLM API access. A candidate internal process agreed before the course.

What you receive

Reference architecture, evaluation harness starter, guardrail checklist, working capstone agent.

C06Advanced

Chat with Your Data: Building RAG Systems that Work

Build retrieval-augmented generation (RAG) properly: document pipelines, chunking, hybrid search, grounding, evaluation and hallucination control on your own documents.

2 days · 12 hoursUp to 25Stage 3

Developers & ITData & AI teams

You will be able to

Build an end-to-end RAG system over a real document set
Measure whether the right documents are found and whether answers stay true to them, and improve both
Decide between RAG, fine-tuning, long context and plain search for a given problem

Modules

When RAG is the right answer and when it is not
Ingestion: PDFs, scans, Thai text, tables and metadata
Chunking, embeddings, vector and hybrid search
Answers grounded in the documents, with citations, and when the system should refuse
Evaluation sets and the metrics that matter
Security: access control, PDPA, prompt injection via documents
Capstone: a chat-with-data assistant on your documents

Prerequisites

Python developers or data engineers. LLM and vector database access. An anonymised document set from the client.

What you receive

Working RAG prototype, evaluation notebook, architecture decision record.

C07Intermediate

Conversational AI & Voice Bot Lab

Build a demo chat or voice assistant that knows your products, and learn what it takes to make it ready for customers.

1 day · 6 hoursUp to 25Stage 2 · 3

Developers & ITBusiness users

You will be able to

Design conversation flows and grounding for a product or service domain
Get a working chat or voice bot demo running on your knowledge base
See what is missing between a demo and a customer-ready system: quality, safety and compliance

Modules

Chatbot versus LLM assistant versus voice agent
Knowledge grounding with RAG over product data
Speech-to-text, text-to-speech and latency for Thai
Handoff, escalation and guardrails
Lab: build and test the demo on your data
Path to production: evaluation, monitoring, PDPA

Prerequisites

A product or FAQ knowledge set from the client. Developer or technically confident business participants.

What you receive

Working demo bot, conversation design template, production readiness checklist.

C08Intermediate

Modern Data Analytics with SQL, Python and AI Assistants

For analysts and junior data teams: solid SQL and Python analytics, made faster by AI coding assistants without losing correctness.

2 days · 12 hoursUp to 25Stage 2

Data & AI teamsDevelopers & IT

You will be able to

Write correct analytical SQL with joins, window functions and CTEs
Use Python for data cleaning, analysis and visualisation
Use an AI assistant to speed up analysis while verifying every result

Modules

SQL for analysis: the patterns that cover 90% of questions
Python and pandas essentials
Visualisation that communicates
AI assistants in the analytics workflow
Reproducibility, documentation and handover
Capstone on a realistic enterprise dataset

Prerequisites

Basic SQL exposure. Laptop with Python environment (setup guide provided).

What you receive

Query and notebook library, analytics checklist.

C09Intermediate

Machine Learning for Business Problems

From a business question to a validated predictive model: forecasting, classification and churn with Python and AutoML, and knowing when a model is good enough.

2 days · 12 hoursUp to 25Stage 2 · 3

Data & AI teamsDevelopers & IT

You will be able to

Frame a business problem as a prediction task with the right target and metric
Build, evaluate and explain a model on tabular data
Decide whether a model should go to production and what it would take

Modules

Problem framing and the metric that matters to the business
Data preparation and leakage traps
Classification, regression and forecasting in practice
Evaluation, explainability and fairness checks
AutoML versus hand-built, and when to use each
Capstone on an enterprise dataset: churn, demand or risk

Prerequisites

Basic Python or strong spreadsheet skills. Laptop with Python environment (setup guide provided).

What you receive

Notebook templates, evaluation checklist, model card template.

C10Intermediate

Document AI & Computer Vision: Intelligent Document Processing

OCR, extraction and validation for Thai forms, invoices, IDs and contracts, plus image classification and inspection, with human review built in.

1 day · 6 hoursUp to 25Stage 2 · 3

Developers & ITData & AI teamsBusiness users

You will be able to

Build an extraction pipeline from scanned documents to structured records
Measure accuracy and design the human review step
Scope a computer vision use case realistically

Modules

Document AI landscape: OCR, layout models, LLM extraction
Thai-language and mixed-script challenges
Extraction lab on your document types
Validation rules, confidence thresholds and review queues
Computer vision cases: inspection, counting, classification
PDPA for documents and images

Prerequisites

Sample documents (anonymised). Developers or technically confident business users.

What you receive

Extraction pipeline prototype, accuracy report template, review-workflow design.

C11Advanced

Modern Data Platform & Data Engineering

Pipelines, lakehouse architecture, orchestration, data quality and cost control, so the organisation has data that AI and analytics can trust.

2 days · 12 hoursUp to 20Stage 3

Data & AI teamsDevelopers & IT

You will be able to

Design a data platform architecture appropriate to the organisation's scale and cloud
Build a reliable, tested pipeline with orchestration and quality checks
Make the data available to analytics and AI securely

Modules

Architecture patterns: warehouse, lakehouse, streaming
Ingestion and transformation with modern tooling
Orchestration, testing and observability
Data quality and contracts
Serving data to BI, ML and RAG
Cost, security and governance by design

Prerequisites

Data engineers or developers with SQL and Python. Cloud account for labs (we provide if needed).

What you receive

Reference architecture, pipeline templates, platform checklist.

C12Intermediate

BI & Dashboards that Drive Decisions

Metric design, dashboard layout and AI-assisted analysis in Power BI, Looker or Tableau, so dashboards answer real questions.

1 day · 6 hoursUp to 30Stage 2 · 3

Business usersData & AI teams

You will be able to

Define a metric tree with owners and definitions
Build a dashboard that leads with the decision and supports it with evidence
Use AI features in BI tools to explain changes and draft commentary

Modules

Metric trees and definitions
Dashboard design: hierarchy, comparison, annotation
Hands-on build in your BI tool
AI-assisted analysis and narrative inside the tool
Governance: certified datasets and versioning
Adoption: who looks, who acts

Prerequisites

Access to your BI tool. Analysts and business users with data responsibilities.

What you receive

Metric tree template, dashboard checklist, one rebuilt dashboard.

C13Advanced

MLOps & LLMOps: Deploying and Operating AI in Production

Versioning, CI/CD, monitoring, drift, prompt and model management, cost and incident response for both classic ML models and LLM applications.

2 days · 12 hoursUp to 20Stage 3 · 4

Developers & ITData & AI teams

You will be able to

Package and deploy a model or LLM application with versioning and tests
Monitor quality, drift, cost and latency, and respond to incidents
Run a change process for prompts, models and data that satisfies governance

Modules

The production lifecycle for ML and LLM systems
Packaging, environments and CI/CD
Monitoring: performance, drift, hallucination signals, cost
Prompt, model and data versioning
Incident response and rollback
Capstone: productionise a model or LLM app end to end

Prerequisites

Developers or ML engineers with Python and Git. Cloud or on-premise environment for labs.

What you receive

Deployment templates, monitoring dashboard starter, operations runbook.

C14Intermediate

Building Internal AI Assistants with Low-Code Tools

Copilot Studio, Power Platform, AppSheet and similar tools: business power users build department assistants and small apps over company data, with IT guardrails.

1 day · 6 hoursUp to 30Stage 2 · 3

Business usersDevelopers & IT

You will be able to

Build an internal assistant grounded in a document set or data source
Connect it to a workflow (approval, ticket, notification)
Publish it inside IT's governance and sharing rules

Modules

Low-code AI landscape and where it fits
Grounding on SharePoint, databases and APIs
Assistant build lab
Actions and workflow connections
Testing, sharing and governance with IT
What to hand to engineers when low-code runs out

Prerequisites

Licences for the chosen platform; IT contact for permissions. Power users with spreadsheet and basic logic comfort.

What you receive

Working assistant, build checklist, governance guide for citizen developers.

Track 4 · Govern & Secure

Data governance, PDPA, AI governance, AI security and the AI development life cycle. What you need in place before scaling past the pilot.

D01Intermediate

Data Governance Foundations

Ownership, quality, catalogue, lineage and access, tied to the business outcomes that justify the work, including data products and data monetisation.

1 day · 6 hoursUp to 40Stage 3

Data & AI teamsLeadersBusiness users

You will be able to

Define data ownership, stewardship and quality rules for a priority data domain
Set up a practical way of working: roles, meetings, policies and a catalogue
Check readiness for data-as-a-service and AI use cases

Modules

Why governance fails and what the successful programmes do differently
Data domains, owners and stewards
Data quality: dimensions, rules, measurement
Catalogue, lineage and metadata that people actually use
Access, classification and PDPA alignment
Data products and monetisation: from internal asset to external value
Workshop: governance charter for one domain

Prerequisites

Mixed group of data owners, IT and business leads. Current data landscape overview from the client.

What you receive

Governance charter draft, domain and role map, 90-day plan.

D02Foundation

PDPA for AI and Data Teams

Thailand's Personal Data Protection Act applied to analytics, AI training data, prompts and vendors, with hands-on masking and anonymisation.

Half-day · 3 hoursUp to 40Stage 1 · 2

Data & AI teamsDevelopers & ITBusiness users

You will be able to

Identify personal and sensitive data in datasets, prompts and documents
Apply lawful basis, minimisation and retention to AI and analytics projects
Mask or anonymise data for AI use and know when to involve the DPO

Modules

PDPA essentials for practitioners
Personal data in AI pipelines: training, prompts, logs, vendors
Anonymisation and pseudonymisation techniques, hands-on
Public versus enterprise AI tools and data residency
Checklist: a PDPA review for any AI use case

Prerequisites

None. Works best alongside the organisation's DPO.

What you receive

PDPA review checklist for AI use cases, masking playbook.

D03Intermediate

AI Governance & Responsible AI

A practical AI governance framework: risk tiers, policy, human-in-the-loop, model and prompt review, and what Thai and international regulators now expect.

1 day · 6 hoursUp to 40Stage 3

LeadersData & AI teamsDevelopers & IT

You will be able to

Classify AI use cases by risk and assign the right level of review
Draft an AI policy and usage guideline that staff can follow
Set up an AI governance committee, intake process and monitoring

Modules

The AI risk landscape: bias, hallucination, privacy, security, accountability
Risk tiering and use-case intake
Policy and usage guidelines: what good looks like
Human-in-the-loop design and sign-off points
Regulatory landscape: Thai guidance and the international frameworks that influence it
Vendor and model due diligence
Workshop: governance framework for your organisation

Prerequisites

Cross-functional group: business, IT, risk, legal and data. Existing policies shared in advance.

What you receive

AI governance framework draft, AI usage guideline template, use-case intake form.

D04Intermediate

AI Development Life Cycle (AI DLC): From Use Case to Production

How AI projects move from idea to a monitored system: discovery, data readiness, build, evaluation, deployment, operation and retirement.

1 day · 6 hoursUp to 30Stage 3

Data & AI teamsDevelopers & ITLeaders

You will be able to

Run use-case discovery and a feasibility check with the right questions
Plan an AI project with stage gates, roles and the evidence each gate needs
Define how a deployed model or LLM application is evaluated, monitored and changed

Modules

Why AI projects differ from software projects
Discovery and feasibility: value, data, risk, adoption
Data readiness and the work nobody budgets for
Build and evaluate: offline metrics, human review, acceptance criteria
Deploy and operate: monitoring, drift, incident response
Lifecycle governance: documentation, model inventory, retirement
Workshop: AI DLC plan for one real use case

Prerequisites

A candidate use case from the client. Mixed product, data and engineering participants.

What you receive

AI DLC playbook, stage-gate templates, project plan for one use case.

D05Advanced

AI Security Essentials: Prompt Injection, Keys and Data Leakage

The attacks that hit LLM applications in practice and the controls that stop them: prompt injection, API key and token protection, data leaks and abuse.

Half-day · 3.5 hoursUp to 30Stage 3

Developers & ITData & AI teams

You will be able to

Recognise and test for prompt injection in chat, RAG and agent systems
Protect API keys and tokens across client, server and CI environments
Design logging, rate limits and output controls for an LLM application

Modules

Threat model for LLM applications
Prompt injection: direct, indirect via documents and tools
Secrets management, key rotation and token scoping
Data leakage paths: logs, vendors, caches
Hands-on: attack and fix a sample application
Security review checklist for AI features

Prerequisites

Developers or security engineers.

What you receive

AI security checklist, secrets-handling standard, test cases.

D06Advanced

Model Risk Management for Financial Services

Model inventory, validation, documentation and ongoing monitoring for ML and LLM models, in line with what regulators expect of banks and insurers in Thailand.

1 day · 6 hoursUp to 25Stage 3

LeadersData & AI teamsDevelopers & IT

You will be able to

Set up a model inventory and tiering that covers LLM applications
Run a validation that an independent reviewer and a regulator would accept
Define monitoring, change control and retirement for models

Modules

Model risk principles and the regulatory landscape
Inventory, tiering and ownership
Validation: data, methodology, performance, limitations
LLM-specific risks and controls
Documentation and model cards
Ongoing monitoring and governance forums

Prerequisites

Risk, model validation, data science and IT participants from a financial institution.

What you receive

Model inventory template, validation checklist, model card and monitoring templates.

D07Intermediate

AI Ethics, Fairness & Explainability in Practice

Bias testing, explainability and customer-facing transparency, applied to a real model or AI feature, with Thai market-conduct and consumer-protection rules in view.

Half-day · 3.5 hoursUp to 30Stage 3 · 4

Data & AI teamsDevelopers & ITBusiness users

You will be able to

Test whether a model or AI feature treats some groups worse than others
Explain a prediction or AI decision to a customer, a regulator and an executive
Decide what to tell users, what to log and when a person can override the AI

Modules

Where harm occurs: data, model, use, feedback loops
Fairness metrics and trade-offs, hands-on
Explainability methods for ML and LLM outputs
Transparency, consent and human override
Case clinic on your use cases

Prerequisites

A candidate model or AI feature. Mixed data, product and compliance participants.

What you receive

Fairness testing notebook, explainability guide, disclosure checklist.

D08Foundation

Measuring AI Value: ROI, Adoption Metrics & Benefits Tracking

For PMO, finance and transformation teams: how to baseline, measure and report the value of AI projects so the portfolio is funded on evidence.

Half-day · 3 hoursUp to 30Stage 2 · 3

LeadersBusiness users

You will be able to

Define a benefits model and baseline for each AI initiative
Track adoption and value metrics without burdening teams
Report a portfolio view that finance and the board trust

Modules

Value types: time, quality, revenue, risk, and how to evidence each
Baselines and what would have happened without AI
Adoption metrics that predict value
Benefits tracking cadence and ownership
Portfolio reporting template

Prerequisites

PMO, finance or transformation office participants with a live initiative list.

What you receive

Benefits model template, metrics catalogue, portfolio report template.

Track 5 · Programs & Events

Multi-week programs, hackathons and academy design for organisations that want AI to become part of everyday work.

P01Program

AI Innovation Readiness Model Assessment

Find out how ready the organisation is for AI across twelve areas, and leave with tailored insights, a strategy and a clear direction.

3–4 weeksOrganisation-wideStage 1

Leaders

You will be able to

Know where the organisation stands on governance, data, foundation, workforce, leadership and culture
Have a prioritised list of gaps and the training and initiatives that close them
Agree a roadmap and plan that leadership and departments share

Modules

Readiness survey across business and technology functions
Leadership and department-head interviews
Document and data landscape review
Scoring on the Capable, Driven, Activated scale per dimension
Readiness report and heat map
Roadmap workshop with leadership and a recommended training plan

Prerequisites

Executive sponsor, access to department heads for interviews, and existing strategy and policy documents.

What you receive

AI readiness report, dimension heat map, recommended training roadmap and initiative plan.

P02Program

AI Fundamentals Bootcamp

Build basic AI knowledge across the workforce and get people interested, through short sessions with hands-on practice between them.

2–4 sessions over 2–6 weeksCohorts of 30–100Stage 1

Business usersLeadersDevelopers & IT

You will be able to

A shared understanding of what AI, machine learning and generative AI can and cannot do
First-hand experience using AI tools safely on everyday work
Motivation and a clear next step for each team

Modules

Session 1: AI in simple terms and what it means for our industry
Session 2: hands-on with generative AI on daily tasks, safe-use rules
Session 3: where AI is being applied in our value chain, with live demonstrations
Session 4: ideas clinic, each team names one use case to try
Self-paced practice assignments between sessions

Prerequisites

None. AI tool access for participants during the program.

What you receive

Session materials, safe-use guide, prompt starter kit, use-case ideas list per team.

P03Program

AI Pilot Program Development

Build the skills to experiment with AI, reduce fear of change and test what works, by taking a few real use cases from idea to proof of concept.

6–10 weeks3–6 pilot teamsStage 2

Business usersDevelopers & ITData & AI teams

You will be able to

Pilot teams can scope, build and evaluate an AI use case with guidance
Each pilot has evidence on feasibility, value and risk
Leadership has a practical understanding of AI's potential impact from real results

Modules

Use-case selection and scoping with business owners
Skills sprint for pilot teams (prompting, automation, data, evaluation)
Build phase with weekly coaching and office hours
Evaluation against agreed success metrics and governance checks
Showcase to leadership and go or no-go decision per pilot

Prerequisites

Named pilot teams with a business owner each, data access, and a sponsor for the showcase.

What you receive

Pilot playbook, evaluation framework, working proofs of concept, showcase pack with recommendations.

P04Program

AI Scaling Program

Bring AI into core business processes, scale it across departments and strengthen teamwork across functions, so AI becomes part of daily operations.

8–12 weeksCohorts of 20–30Stage 3

LeadersBusiness usersDevelopers & ITData & AI teams

You will be able to

Role-specific skills that teams use weekly, from business users to developers
Proven pilots turned into embedded processes with owners and metrics
Governance, data and operating model ready to support scale

Modules

Week 1–2: leadership alignment and operating model
Week 3–6: role tracks (business, developer, data) with weekly labs
Week 7–10: scale sprints: process redesign, automation, integration
Week 11–12: governance review, showcase and adoption plan
Ongoing: Champions network and measurement

Prerequisites

Executive sponsor, program lead on the client side, tool access for cohorts, at least one proven pilot.

What you receive

Curriculum, all session materials, scaled use-case portfolio, adoption metrics, Champions toolkit.

P05Program

AI Accelerator Program

Build a culture of continuous AI innovation and make the organisation a leader in AI-driven change, with an innovation pipeline, advanced skills and public visibility.

3–6 monthsInnovation team plus sponsorsStage 4

LeadersData & AI teamsDevelopers & IT

You will be able to

A repeatable pipeline from idea to funded AI initiative
Advanced capabilities in agentic AI, data products and AI governance
Recognition as an industry reference, through showcases and published results

Modules

Innovation operating model and portfolio governance
Advanced capability tracks: agentic AI engineering, data products, AI governance at scale
Hackathon or buildathon to seed the pipeline
Review gates and funding decisions, run like an internal venture fund
Public showcase and support for publishing results

Prerequisites

Stage 3 maturity in most dimensions, a dedicated innovation lead, and executive commitment to a portfolio.

What you receive

Innovation playbook, capability curricula, pipeline dashboard, showcase and communication pack.

P06Program

AI Hackathon / Buildathon

We design and run an AI build event end to end: challenge framing, workshops, mentoring, judging and a showcase that leaves working prototypes behind.

2–3 daysUp to 100 in teamsStage 2 · 4

Developers & ITBusiness usersData & AI teams

You will be able to

Teams produce working prototypes on real business challenges
Participants learn prompting, no-code automation and agent basics under time pressure
Leadership sees a ranked pipeline of ideas with evidence

Modules

Challenge design with business owners
Pre-event bootcamp: tools, prompting, automation, data access
Event day(s): mentoring, checkpoints, technical support
Judging rubric and showcase
Post-event: prototypes to pilots

Prerequisites

Business challenges and data access agreed 4–6 weeks ahead. Venue and tool environments.

What you receive

Event design, workshop and game materials, mentor pool, judging pack, post-event report.

P07Program

Custom Curriculum & AI Academy Design

We design the curriculum, course outlines, terms of reference and train-the-trainer materials for organisations building their own AI and data academy.

Scoped per engagementAnyStage 3 · 4

Leaders

You will be able to

A competency framework and course map by role and level
Course outlines and materials ready for internal or external delivery
A plan for trainers, tooling, assessment and measurement

Modules

Skills diagnostic and role mapping
Curriculum architecture and sequencing
Course outline and material development
Procurement support: terms of reference and evaluation criteria
Train-the-trainer and quality review
Measurement and continuous improvement

Prerequisites

Learning and development or transformation sponsor.

What you receive

Competency framework, course map, outlines and materials, trainer guides.

Recommended learning paths

Three routes we see work in Thai enterprises

These paths are a starting point. The readiness assessment shows which stage each group is at, and we change the order so the first session is about a problem your people already know.

Leadership route

From AI awareness to a funded roadmap

For executive committees and department heads who have to decide where to start with AI and how to keep it safe.

AI Innovation Readiness Model AssessmentP01 · 3–4 weeks
AI for Executives: Opportunity DiscoveryA01 · 1 day
Executive briefing: Agentic AI and what it changesA04 · 2 hours
AI Governance & Responsible AID03 · 1 day
AI Development Life Cycle for sponsorsD04 · 1 day

Business team route

From trying ChatGPT to hours saved every week

For finance, sales, operations, HR and marketing teams who want to use AI safely every day on their own work.

Generative AI at Work: Prompting that WorksB01 · 1 day
Function-specific course (Finance, Sales, HR, Service, Legal, Audit)B04 / B05 / B09 / B10 / B12 / B13 · 1 day
Data Storytelling: From Numbers to NarrativeB06 · 1 day
Workflow Automation with n8n for Business UsersC01 · 1–2 days
AI Champions Camp (to spread it)B03 · 1 day + lab

Technical team route

From AI-assisted coding to agents in production

For developers, data engineers and AI teams who will build and run the systems.

AI for Developers: AI Across the SDLCC03 · 1 day
Agentic Coding with Claude CodeC04 · 1 day
Chat with Your Data: RAG Systems that WorkC06 · 2 days
Agentic AI EngineeringC05 · 2 days
MLOps & LLMOps, then AI Security EssentialsC13 + D05 · 2.5 days

Why Data Wow

Why organisations train with Data Wow

Interactive, case-based learning

Polls, quizzes and problem-solving exercises. A hands-on project in every course. Examples taken from your own work.

Fully customizable

We adjust each program to your goals, your audience and your maturity stage. Cases, demos and labs use your sector and your anonymised sample data.

Designed by data and AI industry experts

Designed and taught by an end-to-end AI and data service provider. Our facilitators are the consultants and engineers who deliver Data Wow projects, so every course uses real business cases.

A long-term partner for growth

We keep supporting your AI, data and digital transformation projects after the course, so the new skills turn into results.

Short primers of 10 to 25 minutes, then practice. Half or more of every session is hands-on, and the split is written in every agenda
PDPA, governance and security rules are part of the design from the start
One lead facilitator plus teaching assistants, about one per 12 to 15 people, in hands-on sessions
Participants send us their real tasks before the day, so the session runs on their own work
Everyone leaves with something they can use: a prompt library, a redesigned workflow, a working automation, a checklist or a 30-day plan
Thai and English delivery and materials

Formats and group sizes

Format	Duration	Group size	Best for
Executive keynote	1.5–2 hours	Up to 100	Board, ExCo, town halls, kick-offs
Half-day workshop	3–3.5 hours	Up to 40	Awareness and foundations
1-day workshop	6–6.5 hours	Up to 40	Most applied courses
2-day bootcamp	11–13 hours	Up to 30	Builder courses with a working MVP
Multi-week program	8–12 weeks	Cohorts of 20–30	Organisation-wide enablement
Hackathon / buildathon	2–3 days	Up to 100 in teams	Innovation events, graduate programs

What is included

Item	Standard
Pre-workshop discovery	30–45 min call plus a 10-minute participant input form
Tailoring	Cases and demos built on your sector and sample data
Materials	Slides, workbook, prompt library and templates, TH/EN
Environment	Setup guide and pre-session checks for tools and accounts
Facilitation	Lead facilitator plus teaching assistants for labs
Post-session	Output pack, facilitator observations and 30-day action list
Location	Onsite in Bangkok included; online and upcountry on request
Commercials	Fixed fee per session, VAT exclusive. Quotation on request

Sectors we have trained

Programs delivered for Thai enterprise and government organisations including:

Banking and virtual bankingConsumer financeEnergy and utilitiesPostal and logisticsAirports and transportFMCG and consumer healthLuxury retailFood and beverageManufacturingDirect sellingTechnology and startups