Kiran Shahid

Copywriter

Focusing on creating long-form content that helps establish your B2B SaaS as an authoritative voice within your industry. Client include HubSpot, Helpjuice, and SetSail.

Blogs & articles: Rife with examples, quotes from industry experts, and helpful tips. You'll also get SEO-friendly blog posts designed to help your website rank higher in search engine results. Focusing on creating a content strategy that caters to every stage of the buyer's journey.

Case studies: Highlighting a satisfied customer's experience with your product or service, case studies are an excellent way to build social proof and show off your industry expertise. Detailed interviews with some of your most interesting customers, case studies show off your business in the best possible light.

E-books & whitepapers: Educational resources like e-books and whitepapers position your brand as a go-to source of information for customers and help establish you as an expert in your field. Perfect for gated content offers, these digital downloads can help you generate leads and grow your email list.

B2B Content Writer | www.kiranshahid.com

Zapier

Freelance Writer

Scoro

Sprout Social, Inc.

Grizzle | B2B Organic Growth Agency

Creating SEO-driven articles for Semrush.

Dock

Stratabeat

Creating content assets for Gen3 Marketing and Freshworks.

HubSpot

Omniscient Digital

Flying Cat Marketing

B2B SaaS Content Writer | Blogs, Case Studies, & Ebooks | Helping SaaS businesses create content assets that boost LTV, reduce CAC, & skyrocket growth 🚀

Most B2B content follows the same formula: identify a keyword, compile what's already ranking, repackage it with your brand colors. Now with AI, this problem has multiplied—tools generate entire blog posts that look substantive but offer nothing new.

Your readers can tell. Enterprise buyers at companies like [specific target companies] are skimming past these articles, looking for genuine expertise.

Working with brands like Zapier, Sprout Social, and HubSpot, I've found three elements that make content perform:

🧠 Primary research from direct customer conversations and proprietary data analysis
🧠 Documented experiences from your team's work solving specific technical problems
🧠 Writers who understand the subject well enough to challenge assumptions and ask better questions

When we work together, your content will:

⭐ Feature specific insights your SMEs haven't shared publicly before
⭐ Present your team's actual experiences solving real client problems
⭐ Come from writers who guide the content direction, not just fill in templates
⭐ Generate qualified leads from enterprise companies actively seeking solutions

Ready to create content that shows genuine expertise instead of algorithmic proficiency? 

Let's talk.

Conversational conversion copy for B2B businesses.


I'm really excited about the shift I'm making in my business.

Being an "order taker" was draining me mentally and professionally. I wanted to offer more value to content teams, but l didn't know how to position myself to do that.

A client would say: "Can you write this post about X topic?" or "Can you update this page?" And I'd execute the brief. That was the relationship.

But now?

I'm coming in as a strategist with a clear program: Analyze what's working in your content library, identify quick wins, and prioritize updates based on traffic potential and business impact.

I'm not just delivering content anymore.
I'm delivering improvements to their
organic traffic and conversions.

Even with startups that have smaller libraries, I'm playing a much bigger role in how their content performs overall rather than just focusing on individual pieces.

I have actual ownership over the results now. 

Not just "here's vour deliverable," but '
lle."

And that’s making all the difference.

I help B2B SaaS companies fix content rot before it kills their credibility | Historical Content Optimization Specialist for B2B SaaS

I just finished writing a piece about content decay for Relato, and one stat stopped me cold.

If you have 200 outdated articles in your library, manually auditing them takes 600 hours. That's 15 full workweeks of someone just... checking if things are still true.

And that's one audit. One point in time. Three months later, your sources have shifted again and you're back where you started.

Traditional fact-checking can't keep up. By the time you manually verify one article, dozens more have already started decaying. You're always reactive, never systematic.

But there's a better way now.

Companies are building AI agents that crawl content libraries, extract every verifiable claim, score them for credibility, and flag exactly what needs updating before readers or Google notice.

Relato built something called RECEIPTS that does this at scale. It audits claims against reliability, evidence, currency, expertise, independence, precision, traceability, and sensitivity. Instead of manually checking hundreds of pages, you get sorted lists of high-risk claims with suggested sources.

I know exactly how much time this saves because I've actually been building my own agents to do something similar.

One agent audits messaging consistency across entire libraries.

Another flags missing expert insights.

Another identifies high-authority content that's declining because of outdated data.

It’s made it possible for me to build an entire service line around historical content optimization. Without these agents, I wouldn’t have time to take on several clients at once.

The point, of course, isn't to eliminate human judgment. It's to stop burning human hours on work that shouldn't be manual.

CMI found that 39% of marketers cite resource constraints as their biggest challenge. You don't solve that by hiring more people to manually fact-check. 

You solve it by building systems that prevent decay from spreading in the first place.

Content rot is inevitable. But catching it before it costs you rankings and credibility? That's a choice.

If you want to read the full article, I’ll leave it in the comments.

I used to spend 30-45 minutes analyzing an article before I typed a single word.

When you’re optimizing an entire content library (50+ pieces), that’s 25+ hours of just… looking at stuff. Here’s how I’ve used AI agents to cut that time by 70% (without losing quality):

First, some quick context.

Companies assume the hard part of content refreshes is the rewriting. It's not. 

The real time sink is everything that happens before you touch the draft: 
checking for cannibalization, assessing search intent, figuring out where product mentions make sense, identifying internal link pathways.

And I was asking myself the same questions every single time. For every single article. Toggling between tabs, spreadsheets, and SERPs like a human cross-reference machine.

So I codified my thought process into five agents. Each one solves a specific problem I was seeing over and over:

1/ Product Integration Agent

Most companies either over-promote or don't promote at all. This finds the organic moments where a product mention actually adds value.

2/ Keyword Cannibalization Agent
Scans for articles competing against each other for the same keywords. Happens constantly in big blogs where multiple writers have worked over years. Maps it all out and recommends consolidation or differentiation.

3/ Search Intent Alignment Agent

If someone searches "project management software" and Google is showing comparison posts, but your article is a guide, you're fighting uphill. This catches those mismatches.

4/ Internal Linking Architect Agent

Most companies link randomly or based on habit. This creates actual architecture.

5/ Answer Engine Optimization Agent

Optimizes content for ChatGPT, Perplexity, and other AI search tools. Restructures content to be more quotable and authoritative for LLMs.

Before I built these, I'd be doing this manually for each article. Pulling it up, reading through, cross-referencing with other content, checking SERP intent, mapping internal links. 45 minutes to an hour per article just for analysis before I even started writing.

Now? I run the agents, get my strategic brief, and jump straight into optimization. What used to take an hour of analysis now takes maybe 10 minutes of active work.

I still check every suggestion. I don't implement everything they recommend. The agents get me 70% of the way there.

But that 70% is how I can realistically handle large-scale optimization projects. If a client has 25 articles that need updates, I'm not spending 50 hours on analysis alone. I'm spending 8-10 hours total, and most of that is the agents working while I do other things.

The hard part was never the writing. It was everything I was doing before I let myself write.

Don't believe the hype. You don't have to choose between optimizing for Google and optimizing for LLMs.

The structural improvements that make content "Al-ready" (clarity, hierarchical structure, authoritative explanations) also make it stronger for traditional search.

It's not an "either/or" situation. It's just tightening the foundation so the content performs across both channels.

I haven't seen a lot of companies doing this with their existing libraries yet. Most are just creating new content for Al.

I think that's a mistake. If you want to prioritize AEO over the next few months, you don't need to reinvent the wheel. You just need to fold it into the optimization work you should already be doing.

Don't wait until you disappear from the results to fix your library. DM me to learn more about how I can help.

Buyers aren’t asking ChatGPT or Perplexity, "What is sales automation?" They're asking, "What's the best sales automation software for small teams?

And where does AI pull answers to buying questions from?

Roundup articles. Listicles. Comparison posts.

It doesn't crawl individual product websites and synthesize the features. It finds the existing BOFU content and quotes from that.

Which means if your "Best X Software" article is outdated, you're invisible or oudated in the exact moment buyers are ready to purchase.

I tested this recently. Asked ChatGPT for e-signature alternatives. It pulled from roundup posts. The information it gave me was based on whatever those articles said, whether accurate or not.

If your BOFU article doesn't mention your updated features, AI can't recommend you properly.

If your pricing is wrong, AI is giving prospects bad information.

If your competitor comparison is stale, you're losing deals you don't even know about.

This is why I think BOFU refreshes should be the first priority for anyone thinking about answer engine optimization. Not because TOFU doesn't matter, but because BOFU is where the money is. And that's where outdated content hurts the most.

Most content teams don't have historical optimization in their job descriptions. 

They're hired to create, not to maintain.

But every day, your outdated content library is quietly working against you. In every search result. In every AI overview that pulls from your outdated information.

The fix isn't complicated: update what's working instead of starting over.
But it requires someone to actually own this process, and most companies just don't have that person.

So the work gets treated like an afterthought and constantly deprioritized.

Until your traffic tanks and you fix one blog post at a time, like whack-a-mole, forever and ever and ever.

😬

Every month you don't address this systematically, the problem compounds. 

You're publishing more content on top of the mess. The backlog gets bigger. 

The inconsistency gets worse.

You never catch up because you're always reacting.

Make 2026 the year you plan for historical optimization instead of reacting to it.

DM me if this resonates and you want to learn more about how I help.

💡A Professor - turned Content Strategist | Copywriter | Ghostwriter | Content Writer for B2B SaaS, Digital Marketing and Ed-Tech Brands ✍️ | Content-Led Storyteller 🏆| A Reader 👩‍🏫 and Minimalist 🪄

Sindhu is a passionate Assistant Professor in Artificial Intelligence and Machine Learning, specializing in Deep Learning, Natural Language Processing (NLP), Computer Vision, and Data Science. With a strong foundation in mathematical modelling, computer control of Industrial drives, algorithm optimization, and AI-driven solutions, She aims to bridge the gap between academia and industry by fostering innovation, research, and technical excellence.

🔹 Academia & Research:
Sindhu is deeply involved in AI/ML curriculum development, research mentorship, and interdisciplinary collaborations. Her expertise extends to neural network architectures, reinforcement learning, and edge AI, where she guides students in developing scalable, real-world AI applications.

🔹 Technical Content Writing & Knowledge Dissemination:
Beyond teaching and research, Sindhu specializes in technical content writing, translating complex AI/ML concepts into structured, insightful, and industry-relevant content. She crafts research papers, whitepapers, blogs, and technical documentation, ensuring that cutting-edge developments are accessible to both academia and professionals.

🔹 Key Interests & Expertise:
✅ AI/ML Research – Deep Learning, NLP, Computer Vision, Graph Neural Networks
✅ Content Development – Research articles, AI blogs, whitepapers, technical documentation
✅ Mentorship & Training – Guiding students in AI projects, hackathons, and research work
✅ Industry Collaboration – Exploring real-world AI solutions and bridging academia-industry gaps

With a passion for knowledge-sharing and technical storytelling, She is always eager to collaborate on AI research, content development, and industry-driven innovation.

Let’s connect and explore the future of AI together!

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