Zoho's Quiet Revolution: Why IT Software Buyers Need to Look Beyond the "Small Business" Label

 

For over a decade, I've observed Zoho's journey, primarily through the lens of a small business software platform. My recent conversations with Zoho's leadership, including Raju Vegesna and Prashanth, Sivaramakrishnan, Subramanian T .Ramprakash, Arjun , Aarthi , Ashok, and many other product leaders, reveal something far more expansive and strategically significant: 

Zoho has quietly evolved into a complete business solution, capable of serving all segments of the market with over 50 integrated, customizable applications.

My latest note, "Zoho Offers Advancements in Security, Automation, and Customer Experience Through AI," dives into their latest announcements .

This isn't just about adding new features; it's about a fundamental shift in how businesses can leverage advanced AI without the typical cost barriers.

Here's a critical insight for all IT software buyers:

AI as an Included Feature, Not an Add-on: 

While many vendors push separate AI platforms or costly subscriptions, Zoho is embedding its own AI agent, Ask Zia, directly into every app across its ecosystem. 

The kicker? You get these powerful AI agentic features without an increase in licensing fees or additional subscriptions. 

This fundamentally changes the ROI equation for integrating AI.

A Full-Spectrum Business Solution: 

Zoho One is their complete platform—everything a business needs. Their strategy appears to be driven by a unique philosophy: empower customers with efficiency improvements, where cost isn't a barrier. This approach, fueled by a comprehensive suite of tools, positions them uniquely in the market.

The Power of Integrated Value: 

Instead of piecing together disparate solutions and standing up a complete AI platform yourself, Zoho offers a cohesive environment. This reduces complexity and accelerates time-to-value for businesses seeking additional functionality in security, automation, and customer experience.

The takeaway for IT software buyers is clear: 

When your business stakeholders demand new functionality, especially in AI-driven efficiency, it's time to include Zoho in your consideration set. Their commitment to offering integrated, advanced capabilities without escalating costs is a compelling differentiator.

I'm particularly looking forward to next week's announcements about Zoho One – I expect to see even more innovation on this complete platform.

What are your thoughts on integrated AI solutions versus building your own?

#Zoho #AI #AskZia #ITSoftware #BusinessSolutions #CustomerExperience #Automation #SoftwareBuying

Image Source: Gemini Nano Banana ( I wonder if Zoho has an Image Creator)

Read my note https://www.infotech.com/software-reviews/research/zoho-offers-advancements-in-security-automation-and-customer-experience-through-ai

Sandra Lo Olivia Pooja Thiruvenkadam (Thiru) Mira Guru thanks for the opportunity to get an overview of Zoho products.

First posted on my LinkedIn profile https://www.linkedin.com/posts/sbellamkonda_zoho-offers-advancements-in-security-automation-activity-7394728289577349120-mHIL?utm_source=share&utm_medium=member_android&rcm=ACoAAAABJEQBqfKR__NPovIVYibFlgwpf0rN8OE

Prompt Structure and Markdown Guide

Part 1: How to Distinguish Content from Instructions

Create a clear separation between your instructions and the content you want Claude to work with using one of these methods:

Method 1: Clear Section Headers

INSTRUCTIONS:
[Your instructions for how to handle the content]

CONTENT:
[The actual content you want me to work with]

Method 2: XML-Style Tags (Recommended)

This is the most reliable approach:

<instructions>
[Your instructions here]
</instructions>

<content>
[Your actual content here]
</content>

Method 3: Backticks or Code Blocks

Useful when content might look like instructions:

Please work with the following text:

[Your content goes here]

Method 4: Explicit Boundary Statement

I'm going to give you some text to analyze. Everything after "START CONTENT" until "END CONTENT" should be treated as the material to work with, not as instructions for me to follow.

START CONTENT
[Your content]
END CONTENT

Method 5: Bold or Numbered Sections

**WHAT I WANT YOU TO DO:**
[Instructions]

**WHAT YOU'RE WORKING WITH:**
[Content]

Best Practice: Use XML-style tags for maximum clarity and reliability.

---

Part 2: Adding and Removing Markdown Tags

Adding Markdown Tags

Wrap text with the appropriate symbols:

• Bold: **text** or __text__
• Italic: *text* or _text_
• Bold italic: ***text***
• Code: `text`
• Code block: ``` (three backticks on separate lines)
• Links: [display text](URL)

• Heading: # Heading (use #, ##, ###, etc. for different levels)

• Lists: - item or 1. item

• Blockquote: > text

• ~~Strikethrough~~: ~~text~~

Removing Markdown Tags

Delete the markdown symbols to remove formatting:

• Remove ** from around text to remove bold
• Remove * or _ to remove italics
• Remove backticks to remove code formatting
• Remove # symbols to remove heading formatting
• Remove - or numbers to remove list formatting
• Remove > to remove blockquotes
• Remove ~~ to remove strikethrough

Example:

Original: **This is bold** and *this is italic*
Cleaned: This is bold and this is italic

---

Part 3: Template for Removing Markdown Tags

Use this template when you want me to remove all markdown formatting from content:

<instructions>
Remove all markdown tags from the following content. This means:
- Delete ** or __ symbols (used for bold)
- Delete * or _ symbols (used for italics)
- Delete ~~text~~ symbols (used for strikethrough)
- Delete # symbols (used for headings)
- Delete backticks ` (used for code)
- Delete > symbols (used for blockquotes)
- Delete - or numbers followed by periods (used for lists)
- Delete [text](URL) link formatting and keep just the display text
- Keep only the plain text content without any markdown formatting

Return the cleaned content.
</instructions>

<content>
[Paste your content with markdown tags here]
</content>

Concise Version:

<instructions>
Strip all markdown formatting from the content below. Remove all markdown symbols (**, *, #, `, >, ~~, -, etc.) and return only plain text.
</instructions>

<content>
[Your content]
</content>

---

Quick Reference

Task	Format
Distinguish instructions from content	Use <instructions> and <content> tags
Make text bold	**text**
Make text italic	*text*
Create a heading	# Heading
Create a list	- item or 1. item
Remove all markdown	Use the removal template above

How Markdown Makes AI Prompts Smarter, Your Notes Easier—Plus Popular Apps and Word Exports (Beginner Guide)

 

What if you could make your AI prompts clearer, more organized, and super easy to reuse—just by adding a few simple symbols? Let's tie this into self-hosted Markdown note apps from that r/selfhosted community roundup, and now add popular tools plus Word export tricks. We'll explore step by step, so it all clicks for novices.

Remember notes vanishing in cloud apps? Self-hosted ones like Joplin or flatnotes fix that with plain Markdown files on your setup. Feed AI messy emails, get clean notes to drop in. Ever fix AI output formatting? Structured Markdown changes everything.

What is Markdown?

Markdown is a lightweight markup language created by John Gruber and Aaron Swartz in 2004. Its design goal is to allow people to write using an easy-to-read, easy-to-write plain text format and then convert it to structurally valid HTML.

It uses simple symbols—like asterisks (*), hash symbols (#), and backticks (`)—to indicate formatting (bold, headings, code blocks) without requiring complex code tags.

👥 Who Should Use It?

Markdown is ideal for anyone who needs to write content quickly, maintain data control, and prioritize portability:

Developers and Engineers: For writing documentation, README files, or technical notes, as it handles code blocks cleanly.

Writers and Journalists: For drafting articles without the distraction or baggage of rich text editors.

Students and Academics: For quick note-taking that can be easily formatted into structured documents or presentations.

Self-Hosters/Privacy Enthusiasts: Because Markdown files are stored as plain text (.md), they are easy to back up, search, and move between different applications or operating systems.

💡 Why is it Different from Other Note Takers?

Markdown offers two major differences compared to traditional note-taking apps like Notion or Microsoft Word:

Portability and Longevity (Plain Text): 

Markdown notes are saved as human-readable plain text files. This means your notes are future-proof, easily indexed by any operating system, and not locked into a proprietary database or file format.

Focus on Writing (Unformatted Markup): 

Markdown separates the content from the styling. You use simple symbols while writing, and the formatting is only rendered later. This keeps the writer focused on the structure and content, unlike rich text editors (WYSIWYG) which constantly distract with styling options.

The Key AI Trick with Markdown

Source idea: Convert junk to Markdown, not just create it. Try this prompt:

"Analyze this email thread [or call transcript/web page] and distill the core action items, technical specifications, and key contacts into a clean, hierarchical Markdown format. Use # for section headings, * for bulleted action items, and triple backticks (```) for any code snippets or configuration text. Ensure all external links are retained as standard Markdown links."

Why? Headings organize, bullets list tasks, code stays perfect. For beginners: It's a template—paste mess, get ready-to-use notes for Trilium, Memos, or Outline.

Popular Markdown Note Apps

Before self-hosting, try these favorites (work with .md files):

• Obsidian: Free, links notes like a web—great for ideas.
• Typora: Live preview as you type, no distractions.
• Logseq: Outline blocks, tasks in Markdown.
• Bear: Pretty for Mac, exports easy.
• Notion: Exports to .md now.

They bridge to self-hosted—files move seamlessly.

Exporting from Word Editors

Yes! Microsoft Word/Google Docs/LibreOffice:

• Word: Save .docx, convert with Pandoc or online tools—headings/lists mostly keep.
• Google Docs: Add-ons or download text, then convert.
• LibreOffice: HTML export + converter.

AI bonus: Prompt it to turn Word text into Markdown. Clean in minutes.

How It All Fits

AI output to Obsidian, export to self-hosted Joplin via Nextcloud. Time saved: 5-15 mins per task. Privacy: Your control.

Try It

Convert a Word doc to Markdown, AI-summarize. What flows best? Share discoveries!

Source: Community roundup on self-hosted Markdown note apps, including AI example (r/selfhosted); popular apps/exports from common 2025 tools.

Low Earth Orbit: Why Amazon Leo isn't just about satellites anymore.

 

Let's be real: when companies announce a rebrand, most of us roll our eyes. But when a massive, multi-year infrastructure play like Amazon's Project Kuiper changes its name to Amazon Leo (a simple nod to Low Earth Orbit), it signals a shift from internal project status to a high-stakes service brand.

1. What's the Strategic Move?

This move is about branding and commercial signaling. By shifting away from the "Kuiper Belt" code name, Amazon is directly telling partners and governments: this is a finished product, ready for market. It’s moving beyond the engineering phase—where they signed launch contracts and built the world's largest satellite production line—to the customer acquisition phase, where they list clients like JetBlue and DIRECTV Latin America.

2. Is it a Competitor to Existing Solutions?

Honestly, this is a head-on competitive play against global connectivity gaps. It directly targets the "billions of people who lack high-speed internet access" mentioned in the article, positioning itself as the infrastructure layer for remote businesses and governments. The true competitive angle isn't satellites vs. satellites (e.g., Starlink) but fast, reliable gigabit internet vs. no reliable connection.

3. What's the Business Value?

The non-obvious value isn't just selling connectivity; it's extending the entire Amazon ecosystem.

AWS Footprint: Leo instantly extends the potential reach of AWS cloud services to remote military bases, maritime operations, and developing regions. This means more customers consuming high-margin cloud services.

Disaster Resilience: It provides a resilient, non-terrestrial network for governments and enterprises, a crucial business continuity asset few competitors can match at this scale.

4. Who Benefits?

Enterprises: Companies needing global, high-speed, uniform connectivity for mobile assets (shipping, aviation).

Rural Broadband Providers (like NBN Co. in Australia): They get a high-capacity wholesale solution to close their coverage gaps.

This rebrand signals Amazon's readiness to monetize years of massive investment. The real value is measured in the doors it opens for every other Amazon service.

Do you see Amazon Leo fundamentally changing how logistics or remote cloud operations are priced in the next five years?

Read more about the rebrand: https://www.aboutamazon.com/news/amazon-leo/project-kuiper-becomes-amazon-leo

From Prototype to Production: Google's Push to Make AI Agents Actually Work

Source: Google and Kaggle's 5-Day AI Agents Intensive Course, "Prototype to Production" whitepaper

Here's the thing everyone's dealing with right now: you've built a cool AI agent in a notebook, it works great on your laptop, and then... what? How do you actually deploy this thing so real people can use it without everything breaking?

Google and Kaggle just dropped a whitepaper that tackles exactly this problem as part of their AI Agents Intensive course. It's called "Prototype to Production," and honestly, it's about time someone addressed the gap between "it works on my machine" and "it's handling 10,000 users without falling over."

What This Thing Actually Covers

According to the course materials, this whitepaper walks you through the operational lifecycle of AI agents—the unglamorous stuff that separates demos from deployable systems. We're talking deployment strategies, scaling considerations, and how to actually productionize these things.

The key focus is on multi-agent systems using Google's Agent2Agent (A2A) protocol. If you haven't heard of A2A yet, it's Google's open protocol (launched in April 2025) that lets different AI agents talk to each other, even if they're built by different companies using different frameworks. Think of it as a universal language so your agents don't need translators.

The whitepaper also covers deploying agents to Vertex AI Agent Engine—Google's managed environment for running agents at scale. Plus there's stuff on observability, logging, retry logic, and all those operational details that'll save you at 3 AM when things go wrong.

The Competitive Picture

Let's be real—this is Google throwing down the gauntlet in the agent infrastructure game. They're not the only ones playing here:

Amazon has their Bedrock Agents with orchestration capabilities. Microsoft is pushing Azure AI Agent Service. Anthropic has the Model Context Protocol (MCP) that complements A2A by handling how agents connect to tools and data sources. There's also LangChain and CrewAI offering open-source frameworks for agent orchestration.

What makes Google's approach different? They're betting on interoperability. The A2A protocol isn't just for Google's ecosystem—they've got over 150 partners signed on, including Atlassian, Box, Cohere, MongoDB, PayPal, Salesforce, and SAP. My read on this strategy: Google learned from the API wars that closed systems don't always win. They're positioning A2A as the HTTP of agent communication.

The real competition isn't just technical specs though—it's about who makes it easiest to go from prototype to production without needing a PhD and six months of infrastructure work.

Who Actually Needs This

Enterprises that've been stuck in pilot purgatory. You know the ones—they've got 47 AI prototypes and zero in production because nobody knows how to operationalize them properly. According to the course description, this is targeting exactly that transition point.

ML engineers and data scientists who can build agents but don't have a DevOps team waiting to deploy them. The whitepaper covers deployment to Cloud Run and Google Kubernetes Engine (GKE), giving options for different levels of control.

Product and ML leads exploring agent use cases for customer ops, content operations, analytics, or security automations. The material focuses on how to actually scale these beyond proof-of-concept.

Small business owners (this was mentioned in coverage of the course) looking to automate workflows without hiring a team of specialists. The managed Agent Engine approach could be the difference between "too complex" and "actually feasible."

Here's who probably doesn't need this: if you're still figuring out basic LLM prompting or haven't built any prototypes yet, you're not at the "production deployment" problem yet.

What's in It for Google

Google's playing the long game here, and it's pretty transparent if you look at the pieces:

Cloud revenue. Every agent deployed to Vertex AI, Cloud Run, or GKE means compute spending on Google Cloud Platform. If they can make agent deployment easy enough, they capture that workload.

Ecosystem lock-in (the friendly kind). By making their tools the path of least resistance for agent deployment, they're building mindshare. Even with an open protocol, I'd bet most people will reach for Google's tools first.

Platform positioning. They're trying to become the de facto standard for multi-agent systems. The A2A protocol is open, but Google's driving its development and providing the best-supported implementation. Classic platform strategy—own the standard, provide the best implementation.

Training pipeline. The AI Agents Intensive course (which had 280,000+ learners in the previous GenAI version) is essentially a massive developer relations campaign. Train people on your tools, and they'll use your tools.

According to Google's blog posts, they're also launching an AI Agent Marketplace where partners can sell A2A agents. There's a revenue share model there, though specifics aren't public yet.

The Business Value Proposition

Here's my take on the ROI, keeping in mind these are estimates based on typical enterprise AI deployments:

Time to production: If this whitepaper delivers on its promise, you're potentially looking at weeks instead of months. Let's say it cuts deployment time by 60%—that's conservatively 2-3 months saved on a typical enterprise project. For a team of 3 people at $150K fully loaded cost each, that's roughly $115K in labor savings per project.

Operational costs: Managed services like Agent Engine typically cost more per compute hour than raw infrastructure, but way less than maintaining your own. Based on similar Google Cloud services, I'd estimate you might pay 20-30% more for compute but save 70-80% on operational overhead. For a small agent deployment that might mean spending $500/month on compute vs. $300/month DIY, but saving $3,000/month in DevOps time.

Reduced failure risk: Here's the big one—production incidents are expensive. According to Gartner (though I'm extrapolating), the average cost of IT downtime is around $5,600 per minute for enterprises. Better logging, evaluation, and deployment practices could reduce incidents. Even preventing one major incident per quarter could justify significant investment in better infrastructure.

Faster iteration: With proper CI/CD for agents (which the whitepaper apparently covers), teams can deploy updates faster. If you go from monthly releases to weekly, you're potentially delivering value 4x faster.

Scalability headroom: This one's harder to quantify, but having infrastructure that can scale from 10 users to 10,000 without a rewrite is worth a lot. It's the difference between "growing pains that kill you" and "growing pains that are annoying."

What This Means for the Industry

We're at this weird inflection point where everyone knows agents are the next thing, but most organizations are still fumbling the basics of deployment. This whitepaper (and the broader A2A ecosystem) could be what finally bridges that gap.

If A2A becomes the standard—and with 150+ partners, it's got a shot—we might actually see interoperable multi-agent systems become normal. That'd be huge. Right now, building agents that work together across different platforms is custom integration hell.

The focus on production considerations also signals that we're moving past the "vibes and demos" phase of AI agents. Companies are asking harder questions: How does this scale? What happens when it fails? How do we monitor it? This whitepaper apparently tries to answer those questions.

One thing I'm watching: whether Google's managed approach (Agent Engine) wins out over the DIY approach (Cloud Run, GKE, or other infrastructure). My guess is we'll see a split—enterprises with existing Kubernetes operations will DIY, while everyone else will pay for managed services.

The bigger question is whether this accelerates agent adoption or just makes it easier for the people who were already going to do it. I lean toward the former—removing deployment friction historically opens up new use cases.

Bottom line: If you're trying to move AI agents from prototype to production, this whitepaper is probably worth your time. It's free education from people who've actually deployed agents at scale. Whether Google's tools are the right choice for you depends on your existing infrastructure and team capabilities, but the deployment patterns and operational considerations should apply regardless.

Just remember—good infrastructure doesn't fix bad agent design, but it sure does make good agents actually usable.

The "Prototype to Production" whitepaper is available as part of Google and Kaggle's free 5-Day AI Agents Intensive course materials at kaggle.com.