Comparing Agentic AI Automation Tools: n8n | LangFlow | Flowise AI

Agentic AI Chatbot Development Tools Compared

Agentic AI Chatbot Development Tools ComparedAgentic Artificial intelligence applications are evolving rapidly, and so are the tools we use to build and manage them. Whether you are a business owner, developer, or data professional, you now have access to no-code and low-code platforms that make it easier than ever to design, integrate, and deploy AI solutions.

Not sure where to deploy these AI chatbots? Check out our Use Cases for Agentic AI for Businesses Case Study.

Three popular tools in for Agentic AI are n8n, LangFlow, and Flowise AI. While they may appear similar at first glance, they serve different purposes. Choosing the right one depends on whether you want AI workflow automation, RAG pipeline prototyping, or chatbot deployment.

If you want to learn about AI, join our Agentic AI courses in Singapore with WSQ Funding.

What is n8n?

n8n is an open-source workflow automation tool often compared to Zapier or Make. It connects hundreds of services like Google Drive, Slack, and CRMs, while also offering built-in support for AI integrations such as OpenAI, Pinecone, and LangChain.

  • 🔑 Strengths: 300+ integrations, automation triggers, scheduling, error handling, and logging.

  • ⚠️ Weaknesses: Not ideal for deep experimentation with large language models (LLMs).

  • Best for: AI workflow automation and connecting AI apps to your existing business processes.

Example: Automatically watch a Google Drive folder → extract text → embed into Pinecone → trigger OpenAI to summarize → send results to Slack or email.

What is LangFlow?

LangFlow is a visual drag-and-drop builder for LLM pipelines. It provides an intuitive interface to design RAG (Retrieval-Augmented Generation) applications, agents, and custom prompts. It is perfect for AI engineers and data scientists who want to prototype and tune LLM chains quickly.

  • 🔑 Strengths: Easy experimentation, fine control over prompts, embeddings, retrievers, and memory.

  • ⚠️ Weaknesses: Limited app integrations; not built for enterprise-scale automation.

  • Best for: RAG pipeline prototyping, testing chunking strategies, and refining AI prompts.

Example: Load a PDF → split into chunks → generate embeddings → connect to Pinecone → rerank results → design prompt templates → test chatbot responses.

What is Flowise AI?

Flowise AI is a LangChain wrapper with a user-friendly UI. Like LangFlow, it lets you visually design LLM chains, but its main advantage is quick deployment. You can easily expose your chatbot or RAG pipeline as an API or an embeddable chat widget for your website or application.

  • 🔑 Strengths: Fast deployment, LangChain compatibility, embeddable chatbot interface.

  • ⚠️ Weaknesses: Less polished than LangFlow for experimentation, narrower integrations than n8n.

  • Best for: Chatbot deployment and exposing LangChain-based apps as endpoints or widgets.

Example: Build a knowledge-base chatbot → embed it on your website → connect to Pinecone → answer customer queries using your documents.

Side-by-Side Comparison of n8n, LangFlow, and Flowise AI

Feature n8n LangFlow Flowise AI
Primary Use Workflow automation & ops Visual RAG/LLM prototyping LangChain chatbot/RAG builder
Integrations 300+ apps/APIs Mostly AI/LLM tools LangChain connectors
UI Style Automation-first (Zapier-like) Sandbox/experiment (Node-RED style) LangChain visual interface
Best At Orchestration, scheduling Experimentation, prototyping Quick chatbot deployment
Weak At Prompt tuning, RAG iteration External app integrations Workflow automation
Deploy As Cron jobs, webhooks, APIs API (with more dev ops) API or embeddable chatbot
User Type Ops teams, DevOps, semi-tech AI engineers, data scientists Developers, AI builders

How to Choose Between n8n, LangFlow, and Flowise AI

  •  Choose n8n if you need AI workflow automation and enterprise integrations.

  •  Choose LangFlow if your priority is experimenting and fine-tuning RAG pipelines.

  •  Choose Flowise AI if you want to deploy a chatbot or RAG app quickly with minimal coding.

In practice, many teams use all three:

  • Prototype in LangFlow,

  • Deploy with Flowise AI,

  • Orchestrate and integrate with n8n.

Conclusion

While n8n, LangFlow, and Flowise AI overlap in some areas, each has its own niche. n8n is the king of automation and integration, LangFlow excels at AI design and prototyping, and Flowise AI makes chatbot deployment fast and simple.

Instead of thinking of them as competitors, view them as complementary tools. By combining their strengths, you can build scalable, production-ready AI workflows that are both powerful and practical.

Learn Agentic AI Chatbot Development Without any Coding in Singapore

If you want to learn about AI, join our Agentic AI courses in Singapore with WSQ Funding. You can use SkillsFuture if you are eligible.

This is a full 2 day course, practical, hands-on, and emerge with atleast 3-4 AI chatbots ready by the end of the workshop. Contact us for Agentic AI courses today!

Not sure where to deploy these AI chatbots? Check out our Use Cases for Agentic AI for Businesses Case Study

Cheers,
Vinai Prakash
Founder & Master Trainer
Intellisoft Singapore

Agentic AI for Small Business: Practical Use Cases

Agentic AI use cases in Singapore

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Your Small Business Just Got a Brain: Unlocking Potential with Agentic AI

I. Introduction: Meet Your New AI Teammate

Imagine an employee who never sleeps, learns on the job, anticipates problems, and handles complex tasks without you having to constantly look over their shoulder. Sound like a dream? Or perhaps, a harbinger of things to come? Welcome to the world of Agentic AI!

But what is this “Agentic AI” everyone’s talking about? It’s not just a fancy chatbot or another flavor of automation tool. Agentic AI is a smart, goal-oriented system that makes decisions, plans actions, and learns from its environment—all on its own, with minimal human babysitting.

Think of it as a proactive, autonomous digital worker, capable of navigating the complexities of the modern business landscape with a surprising degree of independence. It’s about shifting from reactive problem-solving to proactive solution implementation, a paradigm shift with potentially transformative implications.

Why should small businesses, in particular, care about this? Because it has the potential to level the playing field, allowing them to do “more with less,” boosting efficiency, and freeing up valuable time for strategic thinking and the big picture. It’s a chance to compete, not just survive, in an increasingly competitive world.

A Whistle-Stop Tour: Agentic AI Through the Ages

The pursuit of artificial intelligence is hardly new. The seeds of what we now call Agentic AI were sown decades ago, germinating slowly, sometimes painfully, but always persistently.

The Seeds of Intelligence (Mid-20th Century): It all started with fundamental questions. From Alan Turing’s simple yet profound inquiry, “Can machines think?”, to early chatbots like ELIZA, which, despite their simplicity, sparked the imagination and hinted at the possibilities of human-computer interaction, the idea of intelligent machines has been brewing for decades.

Rules & Rote (1970s-1980s): The next iteration involved expert systems, which followed strict rules, like a highly specialized, very literal assistant. A step forward, certainly, but still lacking the flexibility and adaptability we associate with genuine “thinking.”

Learning & Beating Chess Masters (1990s-2000s): Then came the era of Machine Learning! AI started learning from data, identifying patterns, and making predictions with increasing accuracy. IBM’s Deep Blue beating Kasparov wasn’t just a symbolic victory; it showed a glimpse of strategic autonomy, a machine capable of outthinking a human grandmaster.

Talking & Driving (2000s-2010s): The rise of Siri, Alexa, and self-driving cars brought AI into our daily lives, showcasing reactive, but increasingly capable, systems. These technologies demonstrated the potential for AI to understand and respond to our needs in real-time, albeit within predefined parameters.

The LLM Revolution (2020s-Today): And now, we arrive at the Large Language Model (LLM) revolution. Models like GPT have supercharged AI’s ability to reason, plan, and generate human-quality text. Agentic AI leverages these powerful LLMs as its “brain,” enabling it to act autonomously across a wide range of complex tasks.

We’re moving from simply asking “tell me what to do” to empowering the AI to “figure it out and do it.” This is a crucial inflection point, one that holds the potential to reshape how we work and live.

Learn how to create Agentic AI chatbots without coding with a WSQ Funded 2 Day practical, hands-on course in Singapore at Intellisoft Systems.

Your AI Co-Pilot: Practical Use Cases for SMBs Today

The potential applications of Agentic AI are vast and varied, spanning virtually every aspect of business operations. It’s not just about automating mundane tasks; it’s about creating intelligent systems that can augment human capabilities and drive innovation.

Customer Service Supercharger

  • Imagine a customer service system that provides 24/7 autonomous support, with “chatbots” that actually resolve issues, understand customer frustration, and even process returns without human intervention.
  • Even more impressively, picture Agentic AI proactively noticing a delayed delivery and automatically offering a discount to appease the customer, preempting a potential complaint and fostering customer loyalty.

Sales & Marketing Magician

  • Agentic AI can act as a tireless sales and marketing assistant, capturing inquiries, scoring leads, sending personalized follow-up emails, and even booking demos, all without human intervention.
  • Furthermore, it can design, launch, and optimize ad campaigns across various channels, adapting its strategies based on real-time performance data, ensuring maximum ROI.

Financial Wizardry

  •  Agentic AI can automate bookkeeping and expense tracking, keeping your financial records tidy, flagging anomalies, and processing invoices with speed and accuracy. It can also detect fraudulent transactions and offer cost-saving suggestions, providing real-time insights to protect your bottom line and identify opportunities for greater efficiency.

Operations & Supply Chain Sensei

  • Agentic AI can optimize your inventory management by alerting you when stock is low, predicting demand spikes (especially crucial during the holiday rush!), and automatically reordering products as needed. It can also dynamically reroute shipments around unexpected traffic or weather conditions, ensuring timely delivery and minimizing disruptions to your supply chain.

HR Helper

  • From recruitment and onboarding to employee support, Agentic AI can streamline HR processes, filtering applicants, scheduling interviews, and guiding new hires through paperwork. It can also answer employee benefits questions and manage vacation requests, freeing up HR staff to focus on more strategic initiatives.

The Big Picture:

 The real power of Agentic AI lies in its ability to automate entire processes, not just single tasks.

By orchestrating multiple AI agents working in concert, businesses can achieve unprecedented levels of efficiency and agility, allowing their human teams to focus on strategic, creative work that requires uniquely human skills and insights.

IV. The Hype, The Hope, and The Headaches: Opinions & Controversies

While the potential of Agentic AI is undeniable, it’s important to acknowledge the challenges and controversies that surround its adoption. A healthy dose of skepticism, coupled with a willingness to experiment, is crucial for navigating this rapidly evolving landscape.

  • Small Business Owners: Excited, But Wary: The sentiment among small business owners is largely positive, with many expressing excitement about the potential of Agentic AI to be a “game-changer” and enable them to “do more with less.” However, concerns persist regarding the cost of implementation, the lack of in-house AI expertise, the quality of available data, and a general fear of the unknown.

The question on many minds is: “Is it really worth the investment?”

  • Industry Experts: The Next Frontier, With a Caveat: Industry experts largely agree that Agentic AI represents the “next evolution” in artificial intelligence, with the potential to transform entire business processes, not just individual tasks. Massive market growth is predicted in the coming years, as more and more businesses begin to adopt this technology. However, experts also caution against unrealistic expectations and warn of a potential “AI backlash” if Agentic AI fails to deliver on its promises. They emphasize that good data is critical for success (“garbage in, amplified garbage out”) and that human oversight remains essential for defining goals and validating outputs.

 

  • Controversies & Ethical Minefields: The ethical implications of Agentic AI are complex and far-reaching, raising a number of important questions that must be addressed proactively. The “black box” problem, where the decision-making processes of AI agents are opaque and difficult to understand, can erode trust and complicate accountability.

The question of who is responsible when an autonomous agent makes an error remains unresolved. Concerns about privacy and data protection are paramount, given the vast amounts of data that AI systems require to function effectively. The potential for bias amplification, where AI systems perpetuate and amplify existing biases in the training data, is a serious concern, particularly in areas such as hiring and lending.

  • And, of course, there are ongoing debates about the potential impact of AI on employment, with some fearing widespread job displacement, while others argue that AI will create new, more strategic roles for humans. The fear of “rogue AI” is perhaps overblown, but the real concern lies in the potential for misaligned objectives, where AI agents pursue their goals too aggressively or in unintended ways.

Implementation Hurdles (Beyond the Ethical):

  • Beyond the ethical considerations, there are a number of practical challenges that businesses must overcome in order to successfully implement Agentic AI. The technical jargon can be daunting, and designing complex logic, ensuring reliability, and debugging autonomous systems requires specialized expertise.

Integrating new AI agents with existing legacy systems can be a major headache. And finally, these systems can be power-hungry and expensive to scale, requiring significant investments in infrastructure and resources.

V. Beyond Today: The Future of Your Autonomous Digital Workforce

The future of Agentic AI is bright, with rapid advancements on the horizon that promise to transform the way we work and live.

  • Smarter, Faster, More Independent: In the years to come, we can expect to see AI agents that are truly autonomous, capable of making decisions and initiating actions with minimal human intervention. These agents will possess deep reasoning capabilities, enabling them to understand nuance, plan multi-step projects, and offer incredibly accurate insights. Furthermore, they will be multi-modal marvels, seamlessly processing and responding to voice, text, images, and video.
  • Proactive & Predictive Power: Agentic AI will move beyond reactive problem-solving, anticipating and resolving issues before they even arise. It will be able to predict customer churn, market shifts, and optimize everything from pricing to staffing, giving businesses a significant competitive advantage.
  • The Rise of the Digital Team (Multi-Agent Systems): We will see the rise of sophisticated multi-agent systems, where specialized AI agents collaborate with each other to tackle huge, complex tasks. Imagine a mini digital business department, all working together seamlessly, integrated into your existing CRM, ERP, and other business platforms.
  • Self-Healing AI: AI systems will become increasingly self-healing, able to detect and fix their own errors, dramatically reducing downtime and improving reliability.
  • The Human-AI Partnership Evolves: The relationship between humans and AI will continue to evolve, with a greater focus on ethical AI design, transparent systems, and robust governance frameworks. Humans will shift from being “doers” to “designers, managers, and strategists” for their AI teams, leveraging their uniquely human skills to guide and augment the capabilities of their AI counterparts.
  • Market Growth Explosion: Billions of dollars are being poured into this space, signaling rapid innovation and increasing accessibility for businesses of all sizes.

VI. Conclusion: Embrace the Autonomous Advantage

Agentic AI is not a futuristic fantasy; it’s a present-day reality with the potential to revolutionize the way small businesses operate. It offers unprecedented opportunities to boost efficiency, enhance customer experiences, and unlock new avenues for growth.

So, what’s your next step? Don’t get left behind! Start small, identify specific problems that Agentic AI can solve, prioritize data quality, and embrace the learning curve.

Remember, the future isn’t about AI replacing humans, but about empowering humans with incredible AI partners. Are you ready to lead your digital workforce into the future? The autonomous advantage awaits.

Join Agentic AI Course Singapore

Learn Excel Lookup Functions Easily

Learn Lookup Functions in Excel at Intellisoft Singapore

Some of the most popular Excel Lookup reference functions are VLOOKUP & HLOOKUP.

A newly added XLOOKUP is becoming very popular too. (XLOOKUP is currently only available in Office 365 versions). At Intellisoft, you can learn it by joining the XLOOKUP Training course in Singapore using Microsoft Office 365.

Learn Lookup Functions in Excel at Intellisoft Singapore
Learn Lookup Functions in Excel at Intellisoft Singapore

For the power users of Excel, the mastery of INDEX, MATCH & OFFSET can be considered vital, as these are considered the advanced lookup functions in Excel.

These functions will help you Analyze Data quickly. You should enroll in the data analysis and interpretation training class in Singapore.

But with the introduction of XLOOKUP, some of the jugglery created by mixing INDEX & MATCH combination is no longer required.

VLOOKUP Function of Excel

The most MUST HAVE Function ever. Even Excel gurus can’t live without it. I polled a group of Excel experts recently, asking if Excel’s VLOOKUP was overrated. I got a severe backlash for even mentioning it.

Almost everyone said that it is their GO TO function, an absolute must-have and that Excel won’t be that useable if this VLOOKUP function was taken away from Excel!

Most people swear by their VLOOKUP functions. It is their GO TO function when they want to lookup value of any type.

According to legend, VLOOKUP mastery is what separates the Pro Excel users from the Amateurs!

Vlookup is akin to using a dictionary. You know the word, and you want to find out the meaning. This dictionary is the range of cells that contain the lookup up value, and its associated value. The V in VLOOKUP stands for the dictionary being a vertical dictionary. So for a vertical lookup, you must use VLOOKUP function only.

=VLOOKUP(word, dictionary, column number of meaning, exact_match_ype)

The first column in the dictionary must contain the lookup up value, and the first row should be of the data. You should not include the headings in the dictionary table. The difficulty most people have with VLOOKUP is the last flag – the logical value of TRUE or FALSE (You can use 1 for True and 0 to indicate the False flag).

Once a matching value is found out, you will be able to get the return value based on the search. The error value of N/A will be generated if there is no exact match until the last row.

The mystery is created because to use VLOOKUP for an exact match, you have to specify the last optional flag, and set its value to a FALSE or a 0. By default, it is set to 1, which is useful for an approximate match type only. So for an exact match of a specific value, the last parameter is not really optional… it is mandatory.

VLOOKUP EXAMPLE:

There are a couple of major shortcomings in using VLookup function of Excel. First of all, the VLOOKUP is really a slow function. It is apparent when you do a lookup on a large list of 100,000 values or more. Secondly, VLOOKUP can only look up up a corresponding value from the columns on the right of the looked-up value. It can’t look to the left!

Make sure you master this Excel function really well.

HLOOKUP Function in Excel

An oft-forgotten cousin of VLOOKUP, this Horizontal Lookup and Reference function in Excel works in a similar way too. The only difference is that in this case, a lookup dictionary is a horizontal dictionary of columns, denoted by the H.

HLOOKUP is most used in range lookups, rather than exact matches, as columns are not the best suited for exact values, because of their limit of 16,000. Where a list can grow vertically to over a million records easily.

In the following formula, this lookup function searches for the closest match, especially when we are not searching for an exact match, but an approximate match. The dictionary is the table array and it is recommended that we use the absolute reference to lock the cells from moving.

=HLOOKUP(A5, $G$2:$K$100, 2)

Here the HLOOKUP will search for the exact or the next smallest value in the lookup table absolute range of $G$2 to $K$100, and return the second row. If you want the third row, you can change the 2 into a 3.

Both VLOOKUP & HLOOKUP return values from a single row or a single column.

Using the XLOOKUP Function in Excel

Did you know that new functions are added to Excel till today, and these are extremely useful functions making approximate matches as well as exact matches.

Finally, after years of backlash at Microsoft for creating the mess with the Match Type (True and False) in VLOOKUP, they got rid of it completely in the Excel XLOOKUP function.

And by default, XLOOKUP is set to do an exact match.

XLOOKUP requires a deeper understanding of the various scenarios. I’d recommend attending our formal ADvanced Excel Training to build a strong foundation in Excel. You can call us at 6250-3575 for more information of our courses and available enrollment dates for classroom training in Singapore.

This new XLOOKUP function of Excel is only available from Microsoft Office 365 users. It does not work on Excel 2016 or Excel 2019 versions.

Using INDEX Function in Excel

If you know the row number, you can find the value on that row or column cell directly.

INDEX can be used as an Array function also. Paired with MATCH, you can find any value on any row or column in a 2-dimensional array.

Index can help you to find the value on the row or the column of the specified number

How To Use Excel MATCH Function

When you want to find an exact match in an array and return the row number in the array, MATCH comes to your rescue. It is one up on VLOOKUP, which requires you to know the column you want to return. MATCH can find a match for a value that is lower, exactly equal or higher than the specified value.

Paired with INDEX, an INDEX & MATCH Function can manage to look up on the left or the right of any array of cells.

Master the OFFSET Function within Excel

To navigate your way in a two-dimensional array of rows and columns, you can use the OFFSET function in Excel. It can traverse any number of Rows or Columns, and get you the value.

How to use the offset function in Excel:

=OFFSET(Starting Cell, Row to move up or down, Columns to move left or right, Number of rows required to be returned, number of columns required to be returned)

I generally use OFFSET more than INDEX and MATCH combinations. Using one super-powerful OFFSET function is more straightforward.

Once you start using Offset in Excel, you wouldn’t want to use other lookup functions of Excel.

When Do I Use the INDIRECT Function of Excel?

The Excel INDIRECT function returns the reference specified by a text string. References are immediately evaluated to display their contents.

Use the INDIRECT function when you want to change the reference to a cell within a formula, without changing the formula itself.

=INDIRECT(A3)

The above Indirect function will check what is in cell A3. And A3 will have the cell reference to another cell. So if A3 contains B35, Excel will then read the value in cell B35.

Thus, we can get the value of the reference in cell A3. The reference is to cell B3, which may contain the value 45.

The INDIRECT can be very useful in creating custom management dashboards and reports.

What does the FORMULATEXT Function of Excel Do?

Displays the text of another formula. This helps to see all formulas next to their values and can be useful to spot mistakes and issues with formulas.

=FORMULATEXT(A3) will provide you with the formula in cell A3 as a Text Value.

This FormulaText function is useful to see the formula without having to go into Editing mode.

View this link for more information on how to get the Formula of another cell in Excel.

How to use ROWS Function of Excel

Displays the row number of a reference cell.

=ROWS(A1:B4)

Will return a 4. This is because there are 4 Rows in the given range.

How to Use the COLS Function in Microsoft Excel?

Displays the column number of a reference cell.

=COLS(A1:B4)

Will return a 2. This is because there are 2 Columns in the given range: A & B

Using the TRANSPOSE Function of Excel like a Pro

Converts rows into columns and columns into rows. Just like the Transpose feature in Paste Special, but done programmatically.

So if you use TRANSPOSE(A1:D3), you have selected 4 columns and 3 rows.

After the Transpose is completed, you will get an array reference of 3 Columns, and 4 Rows. The horizontal table would have flipped and will be visible vertically.

Pretty nice use of hanging values in rows into columns.

When Do I Use the UNIQUE Function of Excel?

The UNIQUE function of Excel generates a list of unique values that automatically spill down. An array function can be used to create data validation lists too. Available from Microsoft Office 365 onwards. This UNIQUE function is not available in Excel 2016 or Excel 2019.

Learning the Lookup Functions in Excel Quickly & Easily

As you can see, there are a lot of LOOKUP functions in Excel, and learning and mastering them takes time. But once you do master them, you can do wonders with your Excel skills.

It is worth the effort to learn the Excel Lookup Functions. Call Intellisoft at 6250-3575 or What’s App at +65 9066 9991 for Excel 365 Training that covers the key Lookup functions of Excel.

You will definitely enjoy it!

Cheers,

Vinai

Founder & Master Trainer at Intellisoft Systems in Singapore.

WSQ Excel Courses SkillsFuture Eligible in Singapore

Mastering Microsoft 365 Copilot: Build AI Productivity with Intellisoft

Copilot Courses in Singapore at Intellisoft

Unlock the potential of Microsoft 365 with the Microsoft Copilot course in Singapore. This comprehensive Microsoft Copilot course is designed to enhance your productivity using AI tools that integrate seamlessly across Microsoft applications. Whether you are a novice or looking to deepen your understanding, this course will equip you with the skills to utilize Copilot effectively.

A large screen displays the Microsoft 365 Copilot logo.

Course Overview

The Microsoft Copilot course focuses on the capabilities of Microsoft 365 Copilot to streamline workflows. Participants will learn how to integrate AI tools into their daily tasks. Through hands-on experience, you will discover how Copilot can transform your productivity.

This course covers the functionality of tools like Excel and PowerPoint, showcasing how to automate repetitive tasks. By mastering these applications, you can leverage Microsoft 365 to achieve greater efficiency.

Course Objectives of Mastering Microsoft Copilot

The main objectives of the Copilot course are to provide a solid understanding of Microsoft Copilot’s features and to teach participants how to apply these features in real-world scenarios. You will learn to create prompts that enhance your productivity with generative AI. Additionally, the course aims to familiarize you with the AI-driven capabilities of Microsoft 365.

Participants will also explore the benefits of using Copilot in Office 365 and how it complements Microsoft Copilot. By the end of the course, you will be able to confidently navigate Microsoft 365 applications with AI support, and integrate Copilot in your work.

Course Schedule for Microsoft Copilot Courses

The Microsoft 365 Word Excel PowerPoint with Copilot course duration spans 3 days, with sessions designed to cover the three key Office Tools.  A shorter version of this course, from half a day to a full day or more can be customized for your organization too. Use Copilot to enhance your productivity at work.

This customized course option allows participants to learn based on their time available, while still benefiting from live sessions. Each module focuses on different aspects of Microsoft 365 Copilot, ensuring comprehensive coverage of all essential topics.

Whether you are participating in a shorter course, or a comprehensive course for mastering Microsoft Copilot, the course is structured to maximize learning. You will engage in practical exercises that reinforce your understanding of workflow automation.

If you wish to learn MS-4007 Course – Empower Your Workforce with Copilot for Microsoft 365 Use Cases, we offer this as a 1 day workshop – onsite and at our office too.

Prerequisites for Joining Microsoft Copilot 365 Courses

Microsoft 365 Word, Excel, PowerPoint with Copilot Training in Singapore at Intellisoft with WSQ & SkillsFuture
Microsoft 365 Word, Excel, PowerPoint with Copilot Training in Singapore at Intellisoft with WSQ & SkillsFuture

No prior experience with Microsoft 365 is necessary for this course; however, familiarity with basic computer skills will be beneficial. The course is designed to accommodate both beginners and advanced users looking to enhance your productivity through AI. A willingness to explore new technologies will significantly enrich your learning experience.

For those who aim to deepen their skills, additional resources for Microsoft co pilot will be provided. This includes access to Microsoft Learn and other e-learning platforms that complement the course material.

Intellisoft offers Digital Transformation with AI Tools courses

Join Intellisoft Systems 2 Day WSQ Funded course on Digital Transformation with AI Tools like ChatGPT, Gemini, Claude, FireFlies.ai, Canva, Audio AI Tools, Video AI Tools, Productivity AI Tools in Singapore. 

A separate Canva course with SkillsFuture and WSQ funding from SSG Singapore is available for Singaporeans and Permanent Residents with grants.

We also have a WSQ Approved Create Agentic AI Automations Without Coding course to create your own AI Automations. You can also explore Copilot Pro in and the power of Copilot Studio to create your own AI agents with Microsoft Tools.

Unlock the Power of Microsoft Copilot with Intellisoft Training

✅ Transform the Way You Work with AI

At Intellisoft, we bring you a series of hands-on, instructor-led Microsoft Copilot courses designed to help you harness the full power of AI across Microsoft 365 apps. Whether you’re a business user, a team leader, or part of an operations or HR team, these courses will show you how to work faster, think smarter, and get more done — all with the help of Copilot.

Our Copilot training suite includes:

  • MS-4007: Microsoft 365 Copilot User Enablement Specialist

  • Working Smarter with Copilot in Word, Excel & PowerPoint

Each course is designed using official Microsoft Learn materials and led by expert instructors to ensure practical, real-world learning that you can apply immediately in your workplace.

What is Microsoft Copilot?

Microsoft Copilot is your AI-powered assistant built right into Microsoft 365 apps like Word, Excel, PowerPoint, Outlook, and Teams. It uses large language models (LLMs), Microsoft Graph, and your content to help you draft, summarize, analyze, and automate tasks — all with natural language prompts.

Imagine:

  • Writing a full proposal or policy in Word with just a short prompt

  • Creating charts, insights, and forecasts in Excel without writing formulas

  • Generating entire presentations in PowerPoint from your meeting notes or a report

With Copilot, you’re not just working faster — you’re working smarter.

Courses We Offer

MS-4007: Microsoft 365 Copilot User Enablement Specialist

Learn to champion the adoption of Copilot in your organization. This course is ideal for team leads, change agents, and digital champions tasked with driving AI transformation at work. You’ll learn how to implement Microsoft’s user enablement framework, identify high-value Copilot scenarios, and support colleagues in their AI adoption journey.

Work Smarter with Copilot in Word, Excel & PowerPoint

This 1-day course gives you practical experience using Copilot directly in the three core apps:

  • Word: Draft content, rewrite text, create reports, and summarize meetings

  • Excel: Generate formulas, analyze trends, build dashboards and insights

  • PowerPoint: Instantly turn outlines into full slides, refine layouts, and add visual appeal

Perfect for professionals who use Office daily and want to supercharge their productivity with AI.

Free Trial of Microsoft Copilot for AI-Driven Productivity

A free trial of Microsoft Copilot is available to participants who want to try this new AI software in their own lives and workplace for content creation or improving efficiency and productivity. This 30 day free trial can be cancelled at any time, but does require a credit card to be keyed in initially.

Mode of Assessment

Assessment will take place through hands-on projects that allow you to demonstrate your understanding of the course content. You will work on real-world scenarios that require you to apply what you’ve learned about Copilot. Feedback will be provided to help you improve and refine your skills.

By the end of the course, participants will receive a certificate acknowledging their completion. This certification serves as a valuable addition to your professional portfolio, showcasing your proficiency in using Microsoft 365 Co-pilot.

Fees and Funding

The Copilot course is competitively priced to make it accessible to a wide audience. Various funding options are available to support learners in Singapore. You can inquire about potential subsidies and grants that may help cover course fees. Participants can also use their SkillsFuture Credits for the 3 day Microsoft Office with Copilot, and the Digital Transformation with AI Tools courses.

Investing in your skills through this course will open up new opportunities in your career. The knowledge acquired will not only enhance your productivity but also position you as a leader in digital transformation.

The ai course covers several artificial intelligence tools that can help in content creation for marketers, educators, business persons, executives, professionals and managers.

FAQ

What are the prerequisites for the Copilot course?

No prior experience is needed, but basic computer skills are recommended. This is a very practical and hands-on course, one of our best Microsoft copilot courses.

What is the course duration?

The course can be customized for your organization, based on the available time. Usually we do a 1 day course, and we also offer a WSQ funded Microsoft Word Excel PowerPoint with Copilot course which is for 3 full days.

Will I receive a certificate after completing the course?

Yes, a certificate will be awarded upon successful completion of the Microsoft Office course with 365 and Copilot. For corporate trainings on Copilot courses, we will provide a certificate of attendance also.

What is the mode of assessment for Mastering Microsoft Copilot?

Assessment includes hands-on projects based on real-world scenarios. If you opt for a customized corporate training  on Copilot Courses in Singapore without any WSQ Funding, then there is no requirement for any assessment, and we can completely skip it, to focus the entire time on Mastering Microsoft Copilot.

Are there funding options available?

Yes, there are various funding options to support learners. The WSQ Funding comes from SSG Singapore for the 3 day comprehensive course on Microsoft 365 with Copilot. It covers Copilot across Microsoft 365.

Do I need prior AI or technical knowledge to attend these courses?

No. These courses are designed for everyday Microsoft 365 users. If you’re comfortable using Word, Excel, or PowerPoint, you’re ready for Copilot training.

Is Copilot already included in Microsoft 365?

No. Copilot is a premium add-on to Microsoft 365 and must be purchased by your organization. You will need a Copilot-enabled account to practice during the course.

Will I get hands-on experience with Copilot?

Yes! All our sessions are interactive, with guided labs, exercises, and real-world examples using your Copilot-enabled Microsoft 365 apps.

What’s the difference between MS-4007 and the Word/Excel/PowerPoint with Co Pilot course?

The Microsoft MS-4007 focuses on enabling teams to adopt Copilot across organizations. The Word/Excel/PowerPoint course is more about hands-on productivity techniques for individual users.

CONTACT INTELLISOFT for COPILOT COURSES IN SINGAPORE

Call us at +65-6252-5033 or email to training@intellisoft.com.sg with your training requirements. Alternatively, you can fill our Enquiry Form, and tell us what exactly do you need. We will get back to you within 1 business day.

 

Why Learn Python For Data Analysis: An Eye Opener

Learn Python for Data Analysis at Intellisoft Singapore

Python Popularity

Python is best programming language to learn at Intellisoft Training with WSQ Funding

Wondering Why Learn Python?

The workplace has already changed. As technology continues to rapidly transform industries & jobs, staying relevant & competitive requires continuously updating, diversifying, and building completely new skill sets.

Today, Data analysis is no longer the job of IT folks. Everyone is required to analyze the past, adapt to change, and forecast the future. The ability to do so well is increasingly what will keep your job.

Learn Python & Stay Relevant

Python is a great programming language for data science and general data analysis. It is open-source & free to download for anyone, unlike commercial tools like SAS or SPSS. Find out why you must learn Python for your current & future jobs.

Purpose of Learning Python

It is suitable for almost any data science task, from data manipulation and automation to ad-hoc analysis and exploring datasets.

Python is easy to learn, even for complete beginners. You don’t need a background in IT or computer science.

Python Training in Singapore is available for you to get started asap.

Who Uses Python & Why You Should Learn it?

Python is used by people that want to go deeper into data analysis or apply statistical techniques, and by most people who turn to data science.

Python is a production-ready language, meaning it has the capacity to be a single tool that integrates with every part of your workflow!

Why Learn Python Programming
Why You Must Learn Python Programming To Stay Ahead

Why Learn Python: It’s Usability is Great

Whether you work in Manufacturing, Services, Banking, Finance, Logistics, Telecom, Marine, Oil & Gas, Shipping, IT or any other industry, you’ll be able to apply Python to everyday work. And people with a software or engineering background may find Python comes more naturally to them.

  • Coding and debugging is way easier than other programming languages because of the simple syntax of Python
  • Python has a robust ecosystem and is commonly considered one of the easier programming languages to read and learn. Its programming syntax is simple and its commands mimic the English language.
  • Python code is syntactically clear and elegant, easily interpretable, and easy to type.
  • It’s great for building data science pipelines and machine learning products integrated with web frameworks at scale.

Why Learn Python: It’s a Flexible Language

Python is flexible for creating something that has never been done before.

You can also use it for scripting websites, Clean or Scrape Web Data, Merge data from multiple sources, and create games & other applications easily.

Why Learn Python: It is Extremely Easy To Learn

Python’s focus on readability and simplicity means its learning curve is relatively linear and smooth. With this, you can see the difference as you begin to learn Python. Once you know the basics of Python, you should go for Python For Data Analysis Training in Singapore

Python is considered a good language for beginners. No wonder it is the Number 1 Programming language in the world.

Python Programming Training Singapore
Python Programming Training Singapore

Advantages of Python Over Other Languages

  1. General-purpose programming languages are useful beyond just data analysis.
  2. Python has gained popularity for its code readability, speed, and many other functionalities too.
  3. It is great for mathematical computation & learning how algorithms work.
  4. Python has high ease of deployment and reproducibility.

Popular Libraries and Packages

  • pandas to easily manipulate data
  • SciPy and NumPy for scientific computing
  • Scikit-learn for machine learning
  • Matplotlib and seaborn to make graphics & charts
  • statsmodels to explore data, estimate statistical models, and perform statistical tests and unit tests

Getting Started in Python

There are many Python IDEs to choose from which drastically reduce the overhead of organizing code, output, and notes files.

Jupyter Notebooks and Spyder are 2 such popular IDE that we use to teach Python at Intellisoft.

Learn Python Step-By-Step: Instructor Led Training

The best approach to get initiated with using Python is to go for our Step by Step, Beginner Course to introduce the Python Language to you.

With an experienced and knowledgeable instructor, you can learn faster. Our fantastic trainers are ready to explain simple to complex topics with ease. It will be a much better experience.

Plus, attending training will significantly reduce the learning curve, and you will thank yourself for having a quick and rapid start to learning the world’s most popular language – Python.

Contact Us for our Python Training Programs.

Call +65-6250-3575 or email training@intellisoft.com.sg for a Python course brochure.

Article Written By: Vinai Prakash, Intellisoft Systems

Learn More About Python Training in Singapore

Want to explore more about Python? Check out these helpful articles:

Data Model Creation and Considerations in Power BI

How To Do Data Modeling in Power BI - Learn at Intellisoft Training Power BI Course by Vinai Prakash

How To Do Data Modeling in Power BI - Learn at Intellisoft Training Power BI Course by Vinai PrakashData is at the heart of business intelligence and analytics.

Data Modeling in Power BI - Dimension

Microsoft Power BI is a powerful tool that empowers users to transform raw data into valuable insights.

At the core of business intelligence lies the creation of a robust data model.

In this article, we will explore the essential aspects of data model creation in Power BI and the considerations that should be kept in mind as you build, and use your data model for data analysis and visualization.

Understanding Data Models

A data model in Power BI is a structured representation of data that enables efficient analysis and visualization.

It serves as the foundation for all your reports and dashboards, ensuring that the data is organized and easy to work with.

Without a well-designed data model, you may find it challenging to derive meaningful insights from your data.

In Power BI, we can create a data model based on

  • Star Schema Modeling Techniques
  • Dimensional Data Modeling 
  • Snowflake Data Modeling

Types of Tables in Data Models

Types of Tables in Power BI - Fact & Dimension Tables & Their Properties

Types of Tables in Power BI – Fact & Dimension Tables & Their Properties

1. MASTER Tables

MASTER tables are the foundational building blocks of your data model. They are characterized by their slow growth over time, making them ideal for storing reference data such as Customer, Products, Country, Business Units, Cost Centers, Calendars and Categories.

Key features of MASTER tables include:

  1. Slow Growth: MASTER tables typically evolve slowly over time, with additions of new data occurring infrequently.
  2. Flexibility: You can easily add extra columns to MASTER tables to accommodate new attributes or information. The more columns, the more ways you can “slice and dice” the data!
  3. Fewer Rows: These tables tend to have fewer rows, often referred to as “short” tables.
  4. “FAT” but Short: MASTER tables may have many columns, making them “FAT” in terms of width but short in terms of height (number of rows). Thus, we don’t hesitate to add new columns as needed, as “Calculated Columns” using DAX (Data Analysis EXpressions in Power BI)
  5. Naming Convention: To distinguish them from other table types, MASTER tables should be named with a prefix, like “d.”, which denotes that this table is a DIMENSION table.

2. TRANSACTION Tables

TRANSACTION tables are designed to capture data that changes rapidly and involves high transactional volumes.

Examples of TRANSACTION tables include Sales, Purchase Orders, Accounting Entries, Call Centre Calls, and Service Tickets.

Key features of TRANSACTION tables include:

  1. Rapid Growth: TRANSACTION tables grow quickly over time, recording numerous data entries.
  2. Limited Flexibility: Adding extra columns to TRANSACTION tables can be costly in terms of increasing table size and impacting query efficiency.
  3. More Rows: These tables contain a substantial number of rows, often referred to as “tall” tables.
  4. “THIN” but Tall: TRANSACTION tables typically have fewer columns, making them “THIN” in terms of width but tall in terms of height.
  5. Naming Convention: To distinguish them from other table types, TRANSACTION tables should be named with a prefix “f.“, to denote a FACT table.

Power BI Modeling & Dashboard Creation Training in Singapore at Intellisoft Systems

Dimension and Fact Tables

In the context of data modeling in Power BI, Dimension and Fact tables play distinct roles. Dimension tables are typically associated with MASTER tables, serving as a reference for attributes you want to analyze. Fact tables, on the other hand, relate to TRANSACTION tables, capturing the core data you want to measure and analyze.

Determining Your Analysis Requirements

Before you dive into data model creation, it’s crucial to identify your analysis needs. Determine the specific data elements you want to analyze.

For example, if you aim to analyze sales by year, by country, by division, and by category, you should identify “sales” as your Fact table and “year,” “country,” “division,” and “category” as your Dimension tables.

Similarly, to analyze expenses by department by country by geography, and by year, quarter or month, the “Expenses” will be the Transactions (Fact Table), and the “geography”, “country”, “department”, “year”, “quarter”, “month” will be the dimensions to slice and dice the expenses.

Building the Data Model in Power BI

Creating a data model in Power BI involves several key steps.

Start by importing data from various sources, such as databases, spreadsheets, or online services. There are hundreds of sources that can be loaded into Power BI. CSV, Text, PDF, multiple files from a single folder can all be loaded easily.

For how to load such data, join our Power BI Course in Singapore.

Next, define relationships between tables, specifying how they are connected in the Model View in Power BI.

Arrange the tables in a Star Schema fashion. Another way is to keep the master tables on the top, and the Transaction table at the bottom. This way, you can “look up” the values for any transaction from the Dimension tables above.

Data Model in Power BI Training Class in Singapore by Intellisoft Master Trainer Vinai PrakashFinally, structure your data to ensure it is ready for analysis.

Best Practices for Building the Data Model:

  • Choose the Right Data Sources: Select data sources that are relevant to your analysis and ensure they are structured in a way that is conducive to modeling.
  • Normalize Data: Normalize your data model to reduce redundancy. This means avoiding duplication of data and creating relationships between tables.
  • Understand Data Types: Be aware of the data types used in your model to optimize performance and avoid compatibility issues.
  • Define Hierarchies: Create hierarchies within Dimension tables to allow for more flexible and intuitive drilling down into data.
  • Use Calculated Columns and Measures: Instead of adding unnecessary columns to Fact tables, leverage calculated columns in Dimensions, and measures in Fact tables to perform calculations as needed.

Relationship Management

Establishing and managing relationships between tables is crucial for successful data modeling. Power BI’s intuitive interface makes it easy to define these relationships, which help in connecting data from different tables to answer complex questions.

In Power BI, relationships between Fact and Dimension tables are essential for combining and analyzing data from multiple sources. These relationships come in different cardinality types:

  1. one-to-one (1:1),
  2. one-to-many (1:M), and
  3. many-to-many (M:M).

Let’s explore each of these relationship types:

  1. One-to-One (1:1) Relationship:
    • In a one-to-one relationship, each row in one table corresponds to exactly one row in another table.
    • This relationship is used when you have tables that share a common field, and the relationship between them is unique and direct.
    • One-to-one relationships are not very common in Power BI but can be useful in specific scenarios, such as when you have a customer table and an address table, and each customer has only one unique address.

    Example: A “Person” table with a unique “Employee ID” linked to an “Employee Details” table with one record per employee.

  2. One-to-Many (1:M) Relationship:
    • In a one-to-many relationship, each row in one table can relate to multiple rows in another table, but each row in the second table is related to only one row in the first table.
    • This relationship is the most common in Power BI, as it represents a parent-child relationship, where one table contains the unique values (parent) and another table contains related values (child).
    • One-to-many relationships are used for scenarios where you have one primary table and related information in a secondary table.

    Example: A “Customer” table with unique customer IDs related to a “Sales” table with multiple sales records for each customer.

  3. Many-to-Many (M:M) Relationship:
    • In a many-to-many relationship, multiple rows in one table can relate to multiple rows in another table, and vice versa.
    • Many-to-many relationships are less common and usually require an intermediate table to resolve them. This intermediate table is often referred to as a “bridge” or “junction” table.
    • This relationship type is used when you have situations where multiple values in one table can be related to multiple values in another table, and you need to establish these connections.

    Example: An “Orders” table related to a “Products” table through an “OrderDetails” bridge table, as each order can contain multiple products, and each product can be a part of multiple orders.

    Another example is where an author can have many books authored, and each book can be authored by one or many authors.

Best Practices for Relationship Management:

Understand Cardinality: Cardinality defines the relationship between tables, including one-to-one, one-to-many, or many-to-many. Ensure that the cardinality matches your data structure.

Use Bi-Directional Filters Sparingly: Bi-directional filters can be useful, but they should be used with caution to avoid unintended consequences.

Create DAX Measures: Use Data Analysis Expressions (DAX) measures to create calculations that span multiple tables, enhancing the depth of your analysis.

DAX Measures are the new name for Formulas in Power BI. They calculate the value in the formula in-memory, without taking up any additional space in the data file. Thus, the data file stays small, and the in-memory calculations can be performed quite fast.

A Power BI file is like a Zip file, compressing the data at least 10 times or more in most cases. The extension of Power BI files is .pbix.

Data Model Considerations for Scalability

As your data volume grows, it’s essential to consider the scalability of your data model. Techniques such as data compression, partitioning, and aggregations can be employed to handle larger datasets while maintaining model performance.

Best Practices for Scalability:

  • Data Compression: Implement data compression techniques to reduce the size of your model, which will lead to faster query performance.
  • Partitioning: Use data partitioning to manage large datasets more efficiently, making it easier to work with historical or archived data.
  • Aggregations: Create aggregations to pre-calculate summary data, allowing for quicker responses to user queries while dealing with vast datasets.

Conclusion:

In conclusion, data model creation is a fundamental step in Power BI that can significantly impact the quality of your data analysis and reporting.

By understanding the types of tables, defining your analysis requirements, and following best practices, you can build effective data models that lead to valuable insights and informed decision-making.

Mastering the art of data modeling is essential for anyone looking to harness the full potential of Power BI’s capabilities. With careful consideration of scalability, relationships, and best practices, you can ensure that your data model remains efficient and effective as your data and reporting needs grow.

To Learn more about Data Modeling in Power BI, join our Power BI MasterClass in Singapore here. It is conducted by our Founder & Master Trainer, Mr. Vinai Prakash.

Article Written by Vinai Prakash, MBA, PMP, GAP, ACTA Certified, EXCEL & Power BI Enthusiast!

Additional Resources for Power BI

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Data Analytics & Visualization with Power BI

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Power BI Tip #2: Reference Query Results in Another Query With Power Query [Video Tutorial]

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Power BI Tip #6: Fixing The Vertical Axis in Power BI Visualisations

Power BI Tip #5: All About Slicer Controls in Power BI

Power BI Tip#4: Enter Data Into Power BI Quickly [Video]

Power BI Tip #3: Quick Formatting of Power BI Visuals

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