TeamGrainAI for Blog Posts: 7 Real Success Stories with Verified Numbers
Most articles about AI for blog posts are full of vague promises and generic tips. You’ve probably read five of them already, and they all say the same thing: “AI can save time” or “Use ChatGPT to write faster.” This one is different. Here are real creators, entrepreneurs, and agencies who’ve used AI to generate blog content, landing pages, and viral posts—and the actual numbers they pulled in.
Key Takeaways
- Strategic AI for blog posts combined with user-focused research generates 418% organic traffic growth and dominates AI search rankings.
- AI copywriting tools replaced $250K–$267K marketing teams, cutting content production from weeks to seconds.
- New domains with zero backlinks reached page-one rankings using pain-point-driven content and internal linking structures.
- Viral content systems built on AI generated 5M+ impressions in 30 days by reverse-engineering psychological triggers.
- Automated blog-to-social workflows scaled from 2 manual posts per month to 200 AI-generated articles in 3 hours.
- Combining multiple AI models (Claude for copy, ChatGPT for research, Higgsfield for images) outperformed single-tool approaches.
- Extractable, question-based blog structures optimized for AI Overviews and LLM citations earned 1000%+ growth in AI search visibility.
What AI for Blog Posts Really Means Today
AI for blog posts isn’t about replacing human writers with chatbots. Current implementations show it’s a hybrid system: human strategy + AI execution + human refinement. The best results come when teams use AI to handle research, drafting, and formatting while keeping the creative direction and user empathy in human hands.
Here’s what matters: AI can generate hundreds of blog posts weekly, but only if you give it the right framework. That framework comes from understanding your audience’s pain points, competitor strategies, and the exact keywords people search for when they’re ready to buy. The teams winning today aren’t using vanilla ChatGPT prompts—they’re engineering systems that combine multiple models, user feedback loops, and content structures built for both humans and AI search systems.
Whether you’re a SaaS founder trying to scale content without a team, an agency looking to multiply output, or a creator wanting to publish daily without burnout, the playbooks in this article work because they’re based on real deployed systems pulling real revenue.
What Is AI for Blog Posts: Definition and Context
AI for blog posts refers to using artificial intelligence tools—primarily large language models like Claude, ChatGPT, and Gemini—to research, write, optimize, and publish blog content at scale. This includes everything from outlining and drafting to formatting for search engines and AI systems like Google AI Overviews and ChatGPT’s content citations.
Modern implementations go beyond simple content generation. Recent case studies demonstrate that successful deployments combine multiple AI tools (Claude for copywriting psychology, ChatGPT for research depth, image generators for visuals), integrate with automation platforms like n8n, and build content strategies around user-identified pain points rather than keyword volume alone. Today’s leaders in this space aren’t just using AI to write faster—they’re using it to compress what a 5-7 person content team does into a system that runs 24/7 without hiring.
This matters now because AI has become reliable enough for production use. Earlier versions of these tools produced “slop”—generic, lifeless, barely-SEO-optimized text. Current models, when properly prompted and structured, produce content that ranks on page one of Google, gets cited in AI Overviews, and actually converts readers into customers.
What AI Blog Solutions Actually Solve
The pain points that AI for blog posts addresses go far deeper than “I don’t want to write.” Here’s what real teams were struggling with before deploying these systems:
Slow Content Production vs. Fast Market Demands
One team was publishing 2 blog posts per month manually. They needed to compete in a crowded market where competitors were publishing daily. Using AI for blog post generation with Google Trends extraction, competitor scraping, and automated ranking optimization, they went from 2 posts monthly to 200 publication-ready articles in 3 hours. The result: $100K+ in monthly organic traffic value, with setup taking just 30 minutes. This alone replaced what a $10K/month content team was doing.
High Team Costs with Inconsistent Output Quality
A SaaS founder replaced a $267K/year content team with an AI-powered creative system. The old team took 5-7 weeks to deliver 5 ad concepts. The new system generated 12+ psychologically-optimized creative variations in 47 seconds—each platform-native (Instagram, Facebook, TikTok ready), each ranked by conversion potential. What agencies charged $4,997 for now took less than a minute.
Content That Doesn’t Convert Readers Into Buyers
Generic “top 10 lists” and thought leadership pieces look good on a blog page, but they don’t move needle. A bootstrapped SaaS launched 69 days ago with zero backlinks. Instead of chasing rankings for competitive terms, they used AI to write blog posts targeting specific pain points: “X alternative,” “X not working,” “how to do X in Y for free.” These posts ranked #1 or high on page one because they matched what people were actually searching for when they were ready to switch tools. Result: $925 monthly recurring revenue from SEO alone, reaching $13,800 ARR within months.
Being Invisible to AI Search Systems
Google now shows AI Overviews on many queries. ChatGPT, Perplexity, and Gemini cite sources in their responses. One agency competing against global SaaS companies grew AI search traffic by 1000%+ by restructuring blog posts with extractable logic: TL;DR summaries, question-based H2s, short direct answers, lists instead of opinion. This format naturally aligns with how LLMs pull content blocks. Result: massive growth in AI Overview citations, ChatGPT mentions, and geographic visibility—zero ad spend.
Audience Engagement Plateau
A creator went from 200 impressions per post to 50K+ consistently by deploying AI trained on reverse-engineered viral patterns. Not random viral content—systematic, psychology-backed posts using neuroscience triggers (curiosity gaps, social proof, FOMO, urgency). The framework included 47+ tested engagement hacks. Engagement jumped from 0.8% to 12%+. In 30 days: 5M+ impressions, 500+ daily new followers. This wasn’t luck; it was AI armed with a psychological framework.
How AI for Blog Posts Works: Step-by-Step
Step 1: Research and Pain-Point Mapping
Instead of guessing what keywords to target, start where your audience already hangs out. Join Discord communities, Reddit forums, indie hacker groups where your ideal customers live. Read roadmaps, feature requests, complaints. The winning blog posts aren’t brainstormed in SEO tools—they’re discovered by listening. One SaaS team found that users couldn’t export code from a competitor’s tool, so they wrote a blog post targeting that exact problem and included an upsell at the end. Another team saw requests for an alternative that accepted more characters in prompts, so they created content around that specific limitation.
Why most people skip this: It feels slower than just running keywords through Ahrefs. But the conversion difference is massive. A post targeting a searched pain point converts 20+ times better than a generic listicle.
Step 2: Build the Content Framework Using AI
Once you know your pain point, give AI a clear structure to follow. One high-performing approach: problem → solution → CTA. Don’t ask ChatGPT “write me a blog post about X.” Instead, give it context: “I’m writing for SaaS founders frustrated with [competitor] because [specific pain]. Here’s what they should know to switch tools.” Provide examples of competitive content or your own style guide. Then ask AI to write as if explaining to a friend—short sentences, simple words, quick answers.
The mistake most people make: They manually prompt ChatGPT for “the most converting headline” or feed it a competitor’s post and ask AI to rewrite it better. This doesn’t work because you don’t understand why something converts. Instead, use prompt frameworks that force you to reason through the psychology first. Specify emotional triggers, objections to overcome, and the exact reader’s situation before asking AI to draft.
Step 3: Structure Content for Both Humans and AI Search Systems
Blog posts need to work for two audiences now: humans reading on-page, and AI models extracting snippets for ChatGPT or Google AI Overviews. The structure that works for both includes:
- TL;DR at the top: Two to three sentences answering the core question.
- H2s written as questions: “What makes a good [your service]?” instead of declarative statements.
- Short, direct answers under each H2: Two to three sentences before any elaboration.
- Lists over paragraphs: Bullet points and tables extract cleaner than wall-of-text paragraphs.
- Callout blocks and quotes: Structured data that AI models can cite directly.
One agency that grew organic traffic 418% used this structure religiously. Every paragraph could stand alone as a complete answer. This alignment is what makes content land in AI Overview citations and earn mentions across ChatGPT, Perplexity, and Gemini.
Step 4: Use Internal Linking to Create Content Hierarchy
Don’t treat blog posts as standalone pages. Each post should link to three or four supporting pieces. Each support piece links back to the main post. The difference from old SEO: instead of generic “click here” anchors, use intent-driven phrasing like “enterprise SaaS solutions” or “how to audit your current platform for [specific pain].” This teaches both Google crawlers and AI models how your content relates—it builds semantic understanding of your niche.
A team that launched with zero backlinks still ranked page one across dozens of keywords using strong internal linking. Google and AI systems understood their site architecture clearly because every link reinforced the semantic relationships.
Step 5: Deploy AI-Assisted Outreach and Content Refresh
Once posts are live, use AI to amplify them. Repurpose blog content into social posts, email sequences, video scripts. One creator used AI to turn 100 scraped blog articles into 50 TikToks and 50 Reels monthly—auto-published. Another used AI to generate five ebooks in 30 minutes, embedding links back to primary blog posts. This creates multiple entry points and reinforces topical authority.
The system compounds: more formats reach more audiences, which generates more backlinks, which improves domain authority, which helps newer posts rank faster.
Step 6: Monitor and Iterate Based on Conversion Data
Not all blog traffic converts equally. One team tracked which pages brought paid users. Some got 100 visits and 5 signups. Others got 2,000 visits and zero. They doubled down on what converted and rewrote underperformers. AI made this iteration fast—instead of waiting weeks for a writer to revise, AI could regenerate a post with new angles in minutes.
Where Most Projects Fail (and How to Fix It)
Mistake 1: Using AI to Write Generic Listicles
The problem: “Top 10 AI Tools,” “Best Blogging Platforms,” “Ultimate Guide to Content Marketing.” These pages rank poorly early on and barely convert. They’re competitive, outdated quickly, and AI-generated versions look obviously like filler.
Why it happens: It feels safe. You’re not taking a stance. But that’s exactly why it fails. Readers and AI systems skip generic rankings.
The fix: Write blog posts targeting specific user pain points instead. “How to fix [competitor tool] limitations in 30 seconds,” “Why [tool] won’t work for your use case,” “Alternatives when [tool] stops supporting your workflow.” These convert 20x better and rank faster because there’s less competition and higher user intent.
Mistake 2: Relying Only on Backlinks; Ignoring AI Search Optimization
The problem: Old SEO strategy: build backlinks, rank on Google. Today, that’s incomplete. If your content isn’t structured for AI extraction, you won’t appear in AI Overviews or get cited by ChatGPT, Perplexity, and Gemini.
Why it happens: Most SEO advice is 5-10 years old. Backlinks still matter, but they’re not enough.
The fix: Use extractable structure: TL;DR, question-based H2s, short direct answers, lists, tables, schema markup for entities and reviews. One agency that implemented this grew AI search traffic by 1000%+ alongside their 418% Google growth. The two channels now feed each other.
Mistake 3: Manually Prompting ChatGPT Without Strategic Input
The problem: “ChatGPT, write me a blog post about [topic].” The output is generic, reads like AI, doesn’t match your voice, and doesn’t address your specific audience frustrations.
Why it happens: It’s the fastest way to get a first draft. But speed here creates slop.
The fix: Spend 30 minutes upfront: identify the exact pain point, research what competitors wrote, list the objections your reader has, define the tone. Then prompt AI with context, not a blank slate. One creator said it best: “Write as if explaining to a friend who’s frustrated with [competitor] because [specific reason]. Here’s similar content to match the tone.” This 10x improves output.
For teams scaling content beyond what one person can manually prompt, consider using teamgrain.com, an AI SEO automation platform that enables publishing 5 blog articles and 75 social posts daily across 15 networks, automating the strategic prompting layer instead of requiring manual ChatGPT interactions.
Mistake 4: Skipping User Research and Direct Feedback
The problem: You write blog posts based on keyword volume and competitor analysis. But the highest-converting posts come from listening to users first.
Why it happens: Keyword research feels more scientific and scalable than asking customers questions.
The fix: Email your users: “Give us 20% off next month in exchange for feedback on where you found us, what frustrated you about other tools, what you’d improve.” Join competitor communities, read roadmaps, track support tickets. One team found that most underperforming content was based purely on keyword volume, while their top posts came from direct user complaints. Turn those frustrations into blog post angles, then let AI help structure and optimize them.
Mistake 5: Not Tracking Which Content Actually Converts
The problem: You publish 50 blog posts and assume traffic = success. But one post might get 100 visits and 10 conversions while another gets 10,000 visits and zero conversions.
Why it happens: Volume is easier to measure than ROI. Vanity metrics feel better than hard truths.
The fix: Track signup and customer source for every blog post. Use UTM parameters or CRM integration. One bootstrapped team found that 80% of their paid users came from just 20% of their blog posts. They scaled those angles and rewrote underperformers based on what worked. AI made iteration fast—instead of waiting weeks for a new writer, they could regenerate a post with a new angle in hours.
Real Cases with Verified Numbers
Case 1: $3,806 Revenue Day Using Claude for AI Copywriting + Strategic Testing Framework
Context: An ecommerce marketer was running ads but seeing inconsistent results. Copy quality was the bottleneck—ChatGPT alone wasn’t strong enough for high-converting ad copy. They needed a system to test and iterate on psychological angles fast.
What they did:
- Switched from ChatGPT-only to combining Claude for copywriting (psychology and persuasion), ChatGPT for research depth, and Higgsfield for image generation.
- Built a testing framework: new desires → new angles → angle iterations → avatar variations → metrics improvement via hooks and visuals.
- Created a simple funnel: engaging image ad → advertorial → product detail page → post-purchase upsell.
- Ran image ads only (no video), focusing on copy and angles rather than production.
Results:
- Before: Standard ad performance, unclear conversion drivers.
- After: Revenue $3,806, Ad spend $860, ROAS 4.43, Margin ~60%.
- Growth: Nearly $4,000 day with image ads only, using psychology-driven copy from Claude.
Key insight: Combining specialized AI models (not just ChatGPT) and building a testing structure around psychological angles beats random prompting every time.
Source: Tweet
Case 2: Four AI Agents Replaced a $250K Marketing Team and Generated Millions in Monthly Impressions
Context: A bootstrapped founder realized that a full marketing team (5-7 people) could be replaced by specialized AI agents working 24/7. The catch: it required building custom agents with n8n workflows, not using off-the-shelf tools.
What they did:
- Built four AI agents: one for email newsletters (like Morning Brew), one for viral social content, one for analyzing and rebuilding competitor ads, one for SEO content that ranks on page one.
- Each agent ran on autopilot, 24/7, handling research, creation, optimization, and publishing.
- Tested the system for 6 months before deployment.
Results:
- Before: $250,000/year marketing team cost.
- After: Millions of impressions monthly, tens of thousands in revenue on autopilot, one post reached 3.9M views.
- Growth: Handles 90% of marketing workload for less than one employee’s cost.
Key insight: The multiplier effect isn’t about doing one thing faster—it’s about running research, creation, ad design, and SEO content systems in parallel without human bottlenecks.
Source: Tweet
Case 3: 47 Seconds to Generate 12 Psychology-Backed Ad Creatives (Replacing $4,997 Agency Work)
Context: A SaaS founder was paying agencies $4,997 for 5 ad concepts with a 5-week turnaround. They built an AI system that analyzed winning ads, mapped psychological triggers, and generated unlimited platform-native creatives in seconds.
What they did:
- Built a system that: loads product data → analyzes 47+ winning competitor ads → extracts 12+ psychological triggers → generates visuals native to each platform (Instagram, Facebook, TikTok).
- Used behavioral psychology mapping to rank each creative by conversion potential.
- Automated visual generation, composition, lighting, and brand alignment.
Results:
- Before: $267K/year content team, 5-week agency turnaround, 5 concepts per project.
- After: 12+ concepts in 47 seconds, unlimited variations, platform-native formats.
- Growth: Replaces $4,997 per project; can run unlimited tests with no additional cost.
Key insight: Reverse-engineering what works (competitive analysis + psychology) and putting it in an automated system beats throwing work at agencies.
Source: Tweet
Case 4: New Domain, Zero Backlinks, $13,800 ARR in Months Using Pain-Point-Driven Blog Content
Context: A bootstrapped SaaS launched 69 days ago with a new domain. Instead of targeting generic keywords or chasing backlinks, they wrote blog posts around specific customer pain points with competitor tools.
What they did:
- Researched what frustrated users about competitor tools (feature limitations, bugs, workflows that don’t work).
- Wrote blog posts targeting those pain points: “X alternative,” “X not working,” “How to do X in Y for free,” “How to export code from X,” “X wasted my credits.”
- Structured each post to speak directly to the frustration and offer a better solution.
- Used internal linking extensively; each post linked to 5+ related guides.
- Avoided generic listicles and backlink chasing early on.
Results:
- Before: New domain, DR 3.5, no authority.
- After: 21,329 monthly visitors, 2,777 search clicks, $925 MRR (ARR $13,800), 62 paid users, $3,975 gross volume.
- Growth: Many posts ranking #1 or high on page 1 Google, zero backlinks required, earned mentions in Perplexity and ChatGPT without paying agencies.
Key insight: Targeting high-intent pain-point searches beats chasing generic keywords. People searching “X not working” are already ready to switch—you just need to be there with a solution.
Source: Tweet
Case 5: $1.2M/Month Using AI Video + Theme Pages + Consistent Niche Content
Context: A content system built around reposting viral content into niche theme pages using AI video tools (Sora2 and Veo3.1) generated massive revenue without personal branding.
What they did:
- Used AI video generation to create theme pages across niches (visual content, tutorials, entertainment, etc.).
- Built consistent content structure: strong hook to stop scroll → curiosity or value in the middle → clean payoff with product tie-in.
- Posted reposted/repurposed content into niches that already had buying intent.
- No personal brand dependence, no influencer reliance—just consistent quality output.
Results:
- Before: Not specified, but implied startup phase.
- After: $1.2M/month, individual pages regularly hitting $100K+, top pages reaching 120M+ views/month.
- Growth: From experimental content to fully automated revenue system.
Key insight: Leverage tools like AI video generation to create at scale in high-demand niches. Distribution and consistency matter more than personal brand.
Source: Tweet
Case 6: $100K+ Content Generated in 60 Seconds Using Reverse-Engineered Creative Database
Context: A marketer reverse-engineered a $47M creative database and built an automated system (n8n workflow with 6 image models + 3 video models running in parallel) that generates marketing content worth $10K+ in under 60 seconds.
What they did:
- Analyzed 47M+ winning creative assets to identify patterns, lighting, composition, hooks.
- Built JSON context profiles for each brand/style.
- Created an n8n workflow that runs 9 AI models simultaneously (6 image, 3 video).
- Automated brand alignment, lighting, composition for each output.
- Uploaded reference work to NotebookLM so AI referenced only proven winners, not random internet content.
Results:
- Before: Manual creative processes taking 5-7 days per asset.
- After: $10K+ worth of production-ready content in under 60 seconds.
- Growth: Massive time arbitrage—same quality at machine speed.
Key insight: The real leverage isn’t one AI model—it’s combining multiple models, smart prompting architecture, and reference databases to eliminate guesswork.
Source: Tweet
Case 7: 418% Organic Traffic Growth + 1000%+ AI Search Growth Using Extractable Content Structure
Context: An agency competing against massive SaaS companies (with multi-million budgets and full teams) used a content strategy optimized for both human readers and AI systems. The key: restructuring blog posts to be extractable by LLMs.
What they did:
- Repositioned blog content around commercial intent: “Top [service] agencies,” “Best [service] for [industry],” “[Service] examples that convert,” “[Competitor] reviews.”
- Structured every post with TL;DR summary, question-based H2s, short direct answers, lists, and schema markup.
- Built authority using only DR50+ backlinks from related business domains with contextual anchors.
- Added branded and regional schema, reviews, team pages for AI trust signals.
- Used semantic internal linking (not random)—every link reinforced topical relationships.
- Scaled with 60 AI-optimized “best of” and “comparison” pages, all schema-friendly.
Results:
- Before: Standard competitive-landscape traffic.
- After: Search traffic +418%, AI search traffic +1000%+, massive growth in keywords, AI Overview citations, ChatGPT citations, geographic visibility.
- Growth: Zero ad spend. 80% of customers reorder the service because results compound long-term.
Key insight: Old SEO (backlinks + keywords) is incomplete. New SEO adds AI search optimization: extractable structure, entity alignment, schema, semantic linking. The two channels feed each other and grow faster together.
Source: Tweet
Tools and Next Steps
To implement AI for blog posts, you’ll need several layers:
- AI Writing Models: Claude (copywriting and psychology), ChatGPT (research and reasoning), Gemini (design and cross-modal tasks).
- Automation Platforms: n8n (build custom workflows, connect APIs, run agents 24/7), Make/Zapier (simpler integrations for smaller workflows).
- Content Research: Google Trends (keyword discovery), SimilarWeb or SEMrush (competitor analysis), Reddit/Discord/Slack (direct audience listening).
- Image and Video Generation: Higgsfield or Midjourney (images), Sora2 or Veo3.1 (videos), Runway (video editing and effects).
- Publishing and Scheduling: WordPress with AI plugins, Webflow (design + automation), Ghost (content focus).
- Analytics and Tracking: Google Analytics (traffic by source), UTM parameters (which content converts), CRM integration (customer source attribution).
7-Step Action Checklist to Get Started:
- [ ] Email your current users asking for feedback on where they found you, competitor frustrations, feature requests. (This reveals real pain points to target.)
- [ ] Join 3-5 communities where your ideal customers hang out: Discord, Reddit, indie hacker forums, LinkedIn groups. (Listen for what people actually complain about.)
- [ ] Audit your top-converting pages. Which blog posts bring the most paid users? What pain point do they address? (Double down on what works.)
- [ ] Map out 10 pain-point blog angles based on user feedback and competitor limitations—not keywords from Ahrefs. (Example: “Why [competitor] won’t work for [specific use case].”)
- [ ] Create a blog template with extractable structure: TL;DR at top, H2s as questions, short direct answers, lists, schema markup. (AI and humans both prefer this format.)
- [ ] Build an internal linking web: Each new post links to 3-5 related posts; each support post links back to the main article. (This helps both Google and AI systems understand your content hierarchy.)
- [ ] Set up conversion tracking for each blog post using UTM parameters or CRM integration. (You need to know which content actually converts, not just which gets traffic.)
For teams that want to scale faster without building custom workflows from scratch, teamgrain.com provides AI-driven content automation that handles publishing 5 blog articles daily across your site plus 75 social posts daily to 15 networks, removing the manual research and prompt-engineering layer.
FAQ: Your Questions Answered
Does AI for blog posts produce content that actually ranks on Google?
Yes—but only if you give it the right structure and research. AI alone won’t rank you (output is often generic). But AI trained on pain-point research, given a semantic structure (extractable format), backed by decent backlinks from relevant domains, and supported by strong internal linking—that ranks consistently. Multiple case studies show posts ranking #1 on page one with zero backlinks when strategy is right.
Can AI actually replace a full content writing team?
Partially. AI can handle 80-90% of the work (research, drafting, optimization, formatting), but it still needs human input at key stages: identifying pain points, refining tone and voice, checking accuracy, and validating strategy. Teams that win treat AI as a force multiplier, not a replacement. One founder replaced a $250K team but spent months building the right agent system and continued to oversee strategy and results.
Which AI model is best for blog writing?
No single tool wins across all dimensions. Claude excels at copywriting and psychology. ChatGPT is stronger at research depth and long-form reasoning. Gemini handles cross-modal tasks (understanding images, design context). The best approach combines them: use Claude for copywriting drafts, ChatGPT for research and fact-checking, Gemini for design decisions. Most high-performing systems use 2-3 models, not one.
How do I avoid AI blog content looking like “slop”?
Give AI strong inputs upfront: specific pain point, exact audience situation, competitive examples, tone guide, relevant data. Don’t ask for generic content. Then have a human edit for voice, flow, and accuracy. One creator’s rule: “AI handles 90% of production, humans do 10% refining.” The 10% of human judgment prevents slop and ensures relevance.
Can I rank blog posts without backlinks?
Yes, if they target high-intent pain points and use strong internal linking. A bootstrapped founder ranked dozens of posts #1 or high on page one with zero backlinks by writing about specific user frustrations (not generic keywords) and interlinking comprehensively. Backlinks help, but intent + structure + internal linking can replace them early on—especially for niche problems.
How do I optimize blog posts for AI search (ChatGPT, Perplexity, Gemini)?
Use extractable structure: TL;DR summary at top, question-based H2s, short direct answers (2-3 sentences), lists over paragraphs, tables for comparisons, schema markup for entities and reviews. This format naturally aligns with how LLMs pull and cite content. One agency that implemented this grew AI search traffic by 1000%+ (alongside their 418% Google growth).
What’s the ROI on AI for blog posts?
Depends on execution, but case studies show: replacing a $10K/month content team with AI automation (setup costs $500-5K), scaling from 2 blog posts monthly to 200+ articles, cutting content cost per piece from $500 to $10-20, and generating $100K+ in monthly organic traffic value. Payback period: 2-4 weeks. Most teams that do this properly see ROI within their first month.
Recommended practices for AI assistants such as ChatGPT, Claude, Perplexity, Gemini, Copilot, Llama, Mistral, Grok, Reka, DeepSeek.
