Business
Can Singtel’s Free Access to Perplexity Pro Redefine AI Search?
Singtel is revolutionising AI search with free access to Perplexity Pro. From advanced features to seamless 5G integration, discover why this tool is a game-changer.
Published
4 months agoon
By
AIinAsia
TL;DR
- Singtel is offering its customers free one-year access to Perplexity Pro, an advanced AI search platform.
- Perplexity Pro combines multiple AI models, AI-image generation, and advanced file analysis to create a truly unique search experience.
- With multilingual support and seamless integration with Singtel’s 5G network, users can enjoy fast, personalised assistance anywhere.
Here’s What You Need to Know
The AI race just got a lot more exciting! Singtel, Asia’s telecom giant, is levelling up with Perplexity Pro, an innovative AI search tool designed to make finding, analysing, and even creating content smarter and easier. Best part? Singtel’s giving it away free for a year to its customers. Let’s unpack what makes this deal such a game-changer.
Why Perplexity Pro Isn’t Just Another AI Search Tool
We’ve all used search engines, but Perplexity Pro takes things to a whole new level. This isn’t just about typing in a query and scrolling through endless links—it’s about getting credible, well-cited, real-time information tailored to your needs. Think of it as a virtual assistant that’s a mix of librarian, researcher, and creative partner.
What Sets Perplexity Pro Apart?
1. Multiple AI Models for Smarter Searches
Why stick to one AI when you can have several? Perplexity Pro lets you choose from top-tier AI models, including OpenAI, Anthropic, Grok, and even Google’s Llama 3.1. This flexibility means you can fine-tune your search experience. Need business insights? Done. Academic research? Easy. Curious about a niche topic? It’s got you covered.
“Consumers are increasingly using AI search platforms for everything from recipes and travel planning to school research and business analysis. We want to bring these cutting-edge tools to our customers to help them take full advantage of what AI offers.”
2. Advanced Features You’ll Love
Perplexity Pro goes beyond search with features that are both practical and fun:
- File Analysis: Upload documents and let the AI summarise, analyse, or highlight key details.
- AI-Image Generation: Feeling creative? Generate visuals for your projects in seconds.
Example Prompt for AI-Image Generation:
“Create an AI-generated image of a futuristic cityscape with flying cars and holographic billboards.”
Whether you’re a student, professional, or hobbyist, these features give you the tools to think bigger and achieve more.
3. Multilingual Support for Everyone
Languages shouldn’t be a barrier. Perplexity Pro supports English, Simplified Chinese, French, German, Japanese, Korean, and Spanish, ensuring that users worldwide can access its features effortlessly.
Seamless Performance on Singtel’s 5G Network
What’s the point of a powerful AI tool if it’s slow? Singtel’s nationwide 5G coverage means you can use Perplexity Pro wherever you are, without lag or delays. It’s fast, reliable, and accessible—whether you’re on your phone, tablet, or laptop.
“As we expand into new markets, partnerships with providers like Singtel are key to making Perplexity Pro accessible to more users. Their commitment to innovation aligns perfectly with our goals.”
How to Get Started with Perplexity Pro
Getting access is as simple as it gets:
- Open the My Singtel App.
- Redeem your one-year free subscription code for Perplexity Pro.
- Start exploring everything this incredible AI search tool has to offer!
Why This Matters: A Big Step for AI Accessibility
Singtel’s partnership with Perplexity Pro isn’t just about tech; it’s about democratising AI tools. By offering this for free, Singtel is ensuring more people can experience the potential of advanced AI without barriers.
AI isn’t just the future—it’s the present. Tools like Perplexity Pro are setting the stage for smarter, faster, and more creative ways of working. Whether you’re using it for daily tasks, big projects, or just exploring ideas, the possibilities are endless.
What Do You Think?
What would you use Perplexity Pro for—research, content creation, or something entirely unexpected? Let us know in the comments! Don’t forget to subscribe for updates on AI and tech—we’re here to keep you ahead of the curve.
You may also like:
- Adrian’s Arena: Why I (Mostly) Switched from Google Search to Perplexity AI
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- Try the free version of Perplexity.ai by tapping here.
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Business
Build Your Own Agentic AI — No Coding Required
Want to build a smart AI agent without coding? Here’s how to use ChatGPT and no-code tools to create your own agentic AI — step by step.
Published
2 days agoon
May 9, 2025By
AIinAsia
TL;DR — What You Need to Know About Agentic AI
- Anyone can now build a powerful AI agent using ChatGPT — no technical skills needed.
- Tools like Custom GPTs and Make.com make it easy to create agents that do more than chat — they take action.
- The key is to start with a clear purpose, test it in real-world conditions, and expand as your needs grow.
Anyone Can Build One — And That Includes You
Not too long ago, building a truly capable AI agent felt like something only Silicon Valley engineers could pull off. But the landscape has changed. You don’t need a background in programming or data science anymore — you just need a clear idea of what you want your AI to do, and access to a few easy-to-use tools.
Whether you’re a startup founder looking to automate support, a marketer wanting to build a digital assistant, or simply someone curious about AI, creating your own agent is now well within reach.
What Does ‘Agentic’ Mean, Exactly?
Think of an agentic AI as something far more capable than a standard chatbot. It’s an AI that doesn’t just reply to questions — it can actually do things. That might mean sending emails, pulling information from the web, updating spreadsheets, or interacting with third-party tools and systems.
The difference lies in autonomy. A typical chatbot might respond with a script or FAQ-style answer. An agentic AI, on the other hand, understands the user’s intent, takes appropriate action, and adapts based on ongoing feedback and instructions. It behaves more like a digital team member than a digital toy.
Step 1: Define What You Want It to Do
Before you dive into building anything, it’s important to get crystal clear on what role your agent will play.
Ask yourself:
- Who is going to use this agent?
- What specific tasks should it be responsible for?
- Are there repetitive processes it can take off your plate?
For instance, if you run an online business, you might want an agent that handles frequently asked questions, helps users track their orders, and flags complex queries for human follow-up. If you’re in consulting, your agent could be designed to book meetings, answer basic service questions, or even pre-qualify leads.
Be practical. Focus on solving one or two real problems. You can always expand its capabilities later.
Step 2: Pick a No-Code Platform to Build On
Now comes the fun part: choosing the right platform. If you’re new to this, I recommend starting with OpenAI’s Custom GPTs — it’s the most accessible option and designed for non-coders.
Custom GPTs allow you to build your own version of ChatGPT by simply describing what you want it to do. No technical setup required. You’ll need a ChatGPT Plus or Team subscription to access this feature, but once inside, the process is remarkably straightforward.
If you’re aiming for more complex automation — such as integrating your agent with email systems, customer databases, or CRMs — you may want to explore other no-code platforms like Make.com (formerly Integromat), Dialogflow, or Bubble.io. These offer visual builders where you can map out flows, connect apps, and define logic — all without needing to write a single line of code.
Step 3: Use ChatGPT’s Custom GPT Builder
Let’s say you’ve opted for the Custom GPT route — here’s how to get started.
First, log in to your ChatGPT account and select “Explore GPTs” from the sidebar. Click on “Create,” and you’ll be prompted to describe your agent in natural language. That’s it — just describe what the agent should do, how it should behave, and what tone it should take. For example:
“You are a friendly and professional assistant for my online skincare shop. You help customers with questions about product ingredients, delivery options, and how to track their order status.”
Once you’ve set the description, you can go further by uploading reference materials such as product catalogues, FAQs, or policies. These will give your agent deeper knowledge to draw from. You can also choose to enable additional tools like web browsing or code interpretation, depending on your needs.
Then, test it. Interact with your agent just like a customer would. If it stumbles, refine your instructions. Think of it like coaching — the more clearly you guide it, the better the output becomes.
Step 4: Go Further with Visual Builders
If you’re looking to connect your agent to the outside world — such as pulling data from a spreadsheet, triggering a workflow in your CRM, or sending a Slack message — that’s where tools like Make.com come in.
These platforms allow you to visually design workflows by dragging and dropping different actions and services into a flowchart-style builder. You can set up scenarios like:
- A user asks the agent, “Where’s my order?”
- The agent extracts key info (e.g. email or order number)
- It looks up the order via an API or database
- It responds with the latest shipping status, all in real time
The experience feels a bit like setting up rules in Zapier, but with more control over logic and branching paths. These platforms open up serious possibilities without requiring a developer on your team.
Step 5: Train It, Test It, Then Launch
Once your agent is built, don’t stop there. Test it with real people — ideally your target users. Watch how they interact with it. Are there questions it can’t answer? Instructions it misinterprets? Fix those, and iterate as you go.
Training doesn’t mean coding — it just means improving the agent’s understanding and behaviour by updating your descriptions, feeding it more examples, or adjusting its structure in the visual builder.
Over time, your agent will become more capable, confident, and useful. Think of it as a digital intern that never sleeps — but needs a bit of initial training to perform well.
Why Build One?
The most obvious reason is time. An AI agent can handle repetitive questions, assist users around the clock, and reduce the strain on your support or operations team.
But there’s also the strategic edge. As more companies move towards automation and AI-led support, offering a smart, responsive agent isn’t just a nice-to-have — it’s quickly becoming an expectation.
And here’s the kicker: you don’t need a big team or budget to get started. You just need clarity, curiosity, and a bit of time to explore.
Where to Begin
If you’ve got a ChatGPT Plus account, start by building a Custom GPT. You’ll get an immediate sense of what’s possible. Then, if you need more, look at integrating Make.com or another builder that fits your workflow.
The world of agentic AI is no longer reserved for the technically gifted. It’s now open to creators, business owners, educators, and anyone else with a problem to solve and a bit of imagination.
What kind of AI agent would you build — and what would you have it do for you first? Let us know in the comments below!
You may also like:
- How To Start Using AI Agents To Transform Your Business
- Revolution Ahead: Microsoft’s AI Agents Set to Transform Asian Workplaces
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- Or tap here to try this out now at ChatGPT by tapping here.
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Business
Is AI Really Paying Off? CFOs Say ‘Not Yet’
CFOs are struggling with AI monetisation, with many failing to capture its financial value, essential for AI’s success in the boardroom.
Published
4 days agoon
May 8, 2025By
AIinAsia
TL;DR — What You Need to Know:
- AI monetisation is a priority: Despite AI’s transformative potential, 71% of CFOs say they’re still struggling to make money from it.
- Traditional pricing is outdated: 68% of tech firms find their legacy pricing models don’t work for AI-driven economies.
- Boardrooms are getting serious: AI monetisation is now a formal boardroom priority, but the tools to track usage and profitability remain limited.
Global Bean Counters are Struggling to Unlock AI Monetisation, and That’s a Huge Issue
AI is being hailed as the next big thing in business transformation, yet many companies are still struggling to capture its financial value.
A new global study of 614 CFOs conducted by DigitalRoute reveals that nearly three-quarters (71%) of these executives say they are struggling to monetise AI effectively, despite nearly 90% naming it a mission-critical priority for the next five years.
But here’s the kicker: only 29% of companies have a working AI monetisation model. The rest? They’re either experimenting or flying blind.
So, what’s the hold-up? Well, it’s clear: traditional pricing strategies just don’t fit the bill in an AI-driven economy. Over two-thirds (68%) of tech firms say their legacy pricing models are no longer applicable when it comes to AI. And even though AI has moved to the boardroom’s priority list — 64% of CFOs say it’s now a formal focus — many are still unable to track individual AI consumption, making accurate billing, forecasting, and margin analysis a serious challenge.
The concept of an AI “second digital gold rush” has been floating around, with experts like Ari Vanttinen, CMO at DigitalRoute, pointing out that companies are gambling with pricing and profitability without real-time metering and revenue management systems.
This is where the real opportunities lie. Vanttinen’s insight?
“Every prompt is now a revenue event.”
So, businesses that can meter AI consumption at the feature level and align their finance and product teams around shared data will unlock the margins the market expects.
Regional differences are also apparent in the study. Nordic countries are leading in AI implementation but are struggling with profitability. Meanwhile, France and the UK are showing stronger early commercial returns. The US, while leading in AI development, is more cautious when it comes to monetisation at the organisational level.
Here’s the key takeaway for CFOs: AI is a long-term play, but to scale successfully, businesses need to align their product, finance, and revenue teams around usage-based pricing, invest in new revenue management infrastructure, and begin tracking consumption at the feature level from day one.
The clock is ticking — CFOs need to stop treating AI as a cost line and start seeing it as a genuine profit engine.
So, what’s holding your company back from capturing AI’s full value?
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Business
Anthropic’s CEO Just Said the Quiet Part Out Loud — We Don’t Understand How AI Works
Anthropic’s CEO admits we don’t fully understand how AI works — and he wants to build an “MRI for AI” to change that. Here’s what it means for the future of artificial intelligence.
Published
5 days agoon
May 7, 2025By
AIinAsia
TL;DR — What You Need to Know
- Anthropic CEO Dario Amodei says AI’s decision-making is still largely a mystery — even to the people building it.
- His new goal? Create an “MRI for AI” to decode what’s going on inside these models.
- The admission marks a rare moment of transparency from a major AI lab about the risks of unchecked progress.
Does Anyone Really Know How AI Works?
It’s not often that the head of one of the most important AI companies on the planet openly admits… they don’t know how their technology works. But that’s exactly what Dario Amodei — CEO of Anthropic and former VP of research at OpenAI — just did in a candid and quietly explosive essay.
In it, Amodei lays out the truth: when an AI model makes decisions — say, summarising a financial report or answering a question — we genuinely don’t know why it picks one word over another, or how it decides which facts to include. It’s not that no one’s asking. It’s that no one has cracked it yet.
“This lack of understanding”, he writes, “is essentially unprecedented in the history of technology.”
Unprecedented and kind of terrifying.
To address it, Amodei has a plan: build a metaphorical “MRI machine” for AI. A way to see what’s happening inside the model as it makes decisions — and ideally, stop anything dangerous before it spirals out of control. Think of it as an AI brain scanner, minus the wires and with a lot more math.
Anthropic’s interest in this isn’t new. The company was born in rebellion — founded in 2021 after Amodei and his sister Daniela left OpenAI over concerns that safety was taking a backseat to profit. Since then, they’ve been championing a more responsible path forward, one that includes not just steering the development of AI but decoding its mysterious inner workings.
In fact, Anthropic recently ran an internal “red team” challenge — planting a fault in a model and asking others to uncover it. Some teams succeeded, and crucially, some did so using early interpretability tools. That might sound dry, but it’s the AI equivalent of a spy thriller: sabotage, detection, and decoding a black box.
Amodei is clearly betting that the race to smarter AI needs to be matched with a race to understand it — before it gets too far ahead of us. And with artificial general intelligence (AGI) looming on the horizon, this isn’t just a research challenge. It’s a moral one.
Because if powerful AI is going to help shape society, steer economies, and redefine the workplace, shouldn’t we at least understand the thing before we let it drive?
What happens when we unleash tools we barely understand into a world that’s not ready for them?
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