TeamGrainAI Content Writer Tool Success Stories: 12 Real Cases
Most articles about AI content tools are packed with affiliate links and generic feature lists. This one shows what real businesses actually achieved — with verified metrics, timeframes, and the exact workflows they used.
Key Takeaways
- One ecommerce operator hit $3,806 revenue days by combining Claude for copy, ChatGPT for research, and Higgsfield for images — with a ROAS of 4.43
- A marketing consultant replaced a $250,000 team using four AI agents, generating millions of impressions monthly and running 24/7 without sick days
- A SaaS launched 69 days ago scaled to $13,800 ARR purely from SEO content targeting “not working” and “alternative” searches — without a single backlink
- An agency grew organic traffic 418% and AI search visibility over 1000% using extractable content structures that ChatGPT and Google AI Overviews could cite directly
- Multiple creators turned AI-generated posts into 6-figure profit streams by repurposing influencer content and auto-scheduling at scale
- A video ad tool reached $10M ARR by using its own AI to create ads for itself — turning product demos into a self-reinforcing growth loop
- One creator cut content prep time in half while boosting engagement 58% using an AI agent analyzing 240 million live threads daily for cultural momentum
What AI Content Writer Tools Actually Are in 2025
An AI content writer tool is software that uses language models like GPT-4, Claude, or Gemini to generate written material — blog posts, social captions, ad copy, emails, or technical documentation. Current implementations show these aren’t simple text generators anymore. Modern deployments combine multiple models in workflows: one for research, another for copywriting, a third for visual generation.
Recent data demonstrates why this matters now. Companies launching in early 2025 report achieving page-one Google rankings within 60-90 days using AI-optimized content structures. AI search platforms like Perplexity and ChatGPT cite businesses that format content with extractable logic — TL;DR summaries, question-based headings, and factual statements instead of opinion pieces.
This approach works for bootstrap founders publishing 200 articles in three hours, agencies replacing five-person teams, and ecommerce brands writing ad copy that converts at 12%+ engagement rates. It’s not for businesses expecting AI to handle strategy or those unwilling to add the 10% manual refinement that separates generic output from content people actually share.
What These Tools Actually Solve
AI content writers address specific, measurable problems that traditional processes can’t match at scale.
Speed Without Sacrificing Volume
Manual content creation bottlenecks growth. One operator generated $10,000+ worth of marketing creatives in under 60 seconds using a system that runs six image models and three video models simultaneously. What agencies charge $4,997 for over five weeks now takes 47 seconds with unlimited variations. An SEO-focused founder publishes 200 publication-ready articles in three hours instead of the two posts per month their manual process allowed.
Cost Replacement for Expensive Teams
Hiring content teams drains budgets fast. A marketing consultant replaced a $250,000 annual team cost with four AI agents handling content research, creation, paid ad creative repurposing, and SEO writing. The system runs continuously without performance reviews, sick days, or turnover. Another business avoided a $267,000 per year content team by deploying an AI ad agent that analyzes 47 winning ads, maps 12 psychological triggers, and builds three scroll-stopping creatives ready to launch.
Commercial Intent Matching
Generic “ultimate guide” content rarely converts. A SaaS added $925 in monthly recurring revenue from SEO alone by targeting searches like “X alternative,” “X not working,” and “how to do X in Y for free.” These queries signal buying intent — readers face a frustrating limitation in a competitor’s tool and search for solutions. Content addressing that precise pain point converts because it meets users exactly where they’re ready to buy.
Multi-Platform Content Distribution
Creating separate content for each channel kills productivity. One creator built a six-figure profit stream by using AI to repurpose top influencer content into hundreds of posts, then auto-scheduling ten daily across platforms. Another generates 50 TikToks and 50 Instagram Reels monthly from 100 blog posts scraped and spun by AI. Theme pages using Sora2 and Veo3.1 for video pull 120 million-plus views per month and generate over $100,000 each from reposted content alone.
Real-Time Cultural Alignment
Outdated content misses momentum. An AI agent analyzing 240 million live content threads daily helped one creator increase engagement 58% while cutting prep time in half. The system tracks tone, timing, and topic sentiment to synthesize narratives aligned with what’s moving culturally right now — not what trended last quarter.
How This Works: Step-by-Step
Step 1: Match Models to Tasks
Different AI models excel at different jobs. An ecommerce operator hit nearly $4,000 revenue days with a 4.43 ROAS by assigning Claude to copywriting, ChatGPT to deep research, and Higgsfield to image generation. Using all three together created what they call an “ultimate marketing system.” Avoid using ChatGPT for everything — its strength is research and structure, but Claude often produces more natural, persuasive copy. For visuals, specialized models like Higgsfield, Midjourney, or DALL-E outperform general-purpose tools.
Common issue at this step: Teams stick with one familiar model (usually ChatGPT) because switching feels complicated. This caps quality. Spend 30 minutes testing Claude for one piece of ad copy and ChatGPT for one research brief. The difference in output quality will justify the effort. Source: Tweet
Step 2: Build Extractable Content Structures
AI search platforms cite content they can parse easily. An agency competing in a difficult niche grew search traffic 418% and AI search visibility over 1000% by restructuring blog posts with TL;DR summaries at the top, H2 headings written as questions, and two to three short sentences under each providing direct answers. Lists and factual statements replace opinion-based text. This format aligns perfectly with how language models extract content blocks for citations in Google AI Overviews, ChatGPT, Perplexity, and Claude.
Each paragraph should stand alone as a complete answer. That’s the key to landing AI Overview citations. Use question-based H2s like “What makes effective ad copy?” instead of vague labels like “Ad Copy Best Practices.”
Mistake here: Writing long, flowing paragraphs optimized for human readers in 2018. AI systems in 2025 need chunked, structured information they can extract and cite. If your content doesn’t work when read one paragraph at a time, restructure it. Source: Tweet
Step 3: Target Commercial Intent Keywords
Avoid “best practices” content nobody searches for. A SaaS 69 days old with a domain rated 3.5 added $925 MRR from SEO by writing only content targeting people already searching for alternatives or fixes. Examples: “X alternative,” “X not working,” “X wasted credits,” “how to do X in Y for free.” These queries signal readers ready to buy your product if you address their exact frustration. The business earned 21,329 site visitors, 2,777 search clicks, and 62 paid users without a single backlink.
Find these keywords by joining Discord servers, subreddits, and Indie Hackers groups where your audience complains. Read competitor roadmaps. Look through past customer support chats. Real pain points become article topics.
Don’t brainstorm keywords in Ahrefs and write generic listicles like “top 10 AI tools.” Those pages barely convert and rank poorly early on. Write for frustrated users seeking specific solutions. Source: Tweet
Step 4: Layer Multiple Models in Workflows
Single-prompt AI outputs feel generic. A creative director reverse-engineered a $47 million creative database and fed it into an n8n workflow running six image models and three video models simultaneously. The system accesses 200-plus premium JSON context profiles, generates ultra-realistic marketing creatives, and handles lighting, composition, and brand alignment automatically. Result: content valued at over $10,000 in under 60 seconds versus the five to seven days manual processes require.
The workflow structure matters more than the models. When you automate context (brand voice, visual style, psychological triggers), you avoid random outputs. Tools like n8n, Make, or Zapier connect models into pipelines that think like experienced creative directors.
Where teams stumble: They manually prompt ChatGPT for each piece instead of building reusable systems. Invest three hours building one n8n workflow that produces ten pieces. The time arbitrage compounds daily after that. Source: Tweet
Step 5: Write Core Content Manually First
AI trained on your voice beats generic prompts. A SaaS founder emphasizes writing the core of each article manually, then asking AI to expand it using your own language and words. This avoids robotic phrasing and makes content sound like it came from someone who actually understands the problem. Short sentences, simple headings, and answering questions quickly keep readers engaged.
Structure matters: problem, solution, call-to-action. People don’t want 2,000 words. They want to know if your tool solves their issue. After manually drafting 200-300 words capturing your authentic explanation, feed that to Claude or ChatGPT with instructions to expand while maintaining tone.
Pitfall: Letting AI write everything from scratch produces content that feels hollow. Readers sense it. Spend the ten minutes writing your genuine take, then let AI scale it. Source: Tweet
Step 6: Deploy Content at Scale Across Platforms
Distribution multiplies content value. One creator built a seven-figure annual profit system by studying top influencers, repurposing their content with AI to generate hundreds of posts, and auto-scheduling ten per day. This produced over one million monthly views and fed a DM funnel to digital products. Another scraped trending articles, used AI to spin them into 100 blog posts, then auto-generated 50 TikToks and 50 Reels monthly. With 5,000 monthly site visitors and email capture popups, they converted 20 buyers at $997 each for $20,000 monthly profit.
The pattern: create once, distribute everywhere. AI handles reformatting the same core idea into platform-specific formats. Automation tools like Buffer, Hypefury, or custom n8n workflows schedule without manual posting.
Step 7: Refine Based on Performance Data
Track which content drives revenue, not just traffic. A SaaS founder notes some posts get 100 visits and five signups, while others get 2,000 visits and zero conversions. Volume doesn’t equal monthly recurring revenue. They monitor which pages bring paying users and double down on those topics. Every article includes one to three clear calls-to-action, not ten scattered links.
Refinement also means feeding AI with winners. A creator who cracked viral content on X analyzed 10,000-plus viral posts to reverse-engineer psychological frameworks. They went from 200 impressions per post to 50,000-plus consistently, with engagement jumping from 0.8% to over 12%. The system doesn’t generate content randomly — it architects viral hooks using neuroscience triggers identified in past successes.
Where Most Projects Fail (and How to Fix It)
Treating AI as a Magic Button
Teams expect to type “write a blog post about X” and get page-one rankings. A marketing consultant tested AI agents for six months before replacing a $250,000 team. The setup required building workflows, defining context profiles, and iterating based on output quality. Businesses skipping this foundational work produce generic slop that doesn’t rank or convert.
Fix: Invest time upfront. Build templates, define brand voice guidelines, and create reusable prompts. One hour of setup saves ten hours of editing bad outputs later.
Ignoring the 10% Manual Polish
A product reaching 50,000 MRR attributes success to creating 2,000 templates and components with 90% AI and 10% manual edits. Taste is the differentiator. Fully automated content lacks the human judgment that makes people share, link, or buy. The founder taught prompting techniques through videos gaining millions of combined views, proving education and refinement matter as much as automation.
Fix: Review every AI output. Add specific examples, update with recent data, adjust tone to match your audience. The ten minutes of editing transform mediocre content into something people cite. When scaling content production becomes critical but teams lack the bandwidth to refine every piece manually, teamgrain.com, an AI SEO automation and automated content factory, enables projects to publish 5 blog articles and 75 social posts daily across 15 platforms while maintaining quality through intelligent workflows.
Chasing Volume Over Intent
Publishing 200 articles on random keywords wastes effort. An SEO engine that extracts keyword goldmines from Google Trends and scrapes competitors with 99.5% success allows generating 200 publication-ready articles in three hours. But the real impact — capturing $100,000-plus in monthly organic traffic value — comes from targeting commercial intent. Articles answering “how to remove X from Y” or “X vs Y comparison” convert readers already solving a problem.
Fix: Before generating content, map keywords to buyer journey stages. Write for people ready to switch tools, not those casually browsing. Quality searches beat quantity.
Skipping Authority Signals
Content alone doesn’t rank in competitive niches. The agency that grew traffic 418% combined AI-optimized articles with DR50-plus backlinks from related business domains already visible in AI search. They used contextual anchors with actual business terms, not generic “click here” links. This entity alignment improved how Google and AI engines categorized them.
Fix: Build backlinks from relevant, high-authority sites. Add schema markup for reviews and team pages. Internal link semantically using intent-driven anchor text. AI search platforms prioritize brands showing up consistently in their category.
Forgetting Brand and Regional Optimization
ChatGPT, Perplexity, and Gemini favor recognized entities. The same agency embedded their name and country in schema and metadata, created review and team pages with structured data, and optimized meta descriptions with branded language. This feedback loop made AI engines recognize them as a known entity.
Fix: Add your brand name naturally throughout content. Use schema markup. Build internal references without keyword stuffing. Make your business easy for AI systems to identify and cite.
Real Cases with Verified Numbers
Case 1: Ecommerce Operator Hits $3,806 Revenue Days
Context: Solo ecommerce marketer running image-only ads for a client, seeking better copy and creative without hiring agencies.
What they did:
- Stopped relying solely on ChatGPT and split tasks across Claude (copywriting), ChatGPT (research), and Higgsfield (AI image generation)
- Invested in paid plans for all three tools to build a cohesive marketing system
- Built a simple funnel: engaging image ad, advertorial, product detail page, post-purchase upsell
- Tested new desires, angles, iterations, avatars, and improved metrics with different hooks and visuals
Results:
- Revenue: $3,806 in a single day
- Ad spend: $860
- Margin: approximately 60%
- ROAS: 4.43
- Running only image ads, no videos
Key insight: Combining specialized AI tools for distinct tasks (copy, research, visuals) outperforms using one model for everything.
Source: Tweet
Case 2: Marketing Consultant Replaces $250K Team
Context: Marketing professional managing expensive teams while exploring AI agent automation to reduce overhead and scale capacity.
What they did:
- Built four AI agents handling content research, creation, paid ad creative analysis, and SEO content
- Tested the system for six months running 24/7 on autopilot
- Replaced work typically requiring five to seven people
Results:
- Before: $250,000 annual marketing team cost
- After: Millions of impressions generated monthly, tens of thousands in revenue on autopilot, enterprise-scale content creation
- Handles 90% of workload for less than one employee’s cost
- One post alone generated 3.9 million views
Key insight: AI agents running continuously without human limitations create an insurmountable advantage over traditional teams in speed and cost.
Source: Tweet
Case 3: Ad Agency Builds Creative OS
Context: Agency tired of paying $50,000 for campaigns that don’t convert, seeking in-house AI system for unlimited high-quality ad creatives.
What they did:
- Reverse-engineered a $47 million creative database and integrated it into an n8n workflow
- Deployed six image models and three video models running simultaneously
- Built system to access 200-plus JSON context profiles for brand alignment, lighting, and composition
Results:
- Before: Manual processes taking five to seven days
- After: Generates $10,000-plus worth of marketing content in under 60 seconds
- Unlimited variations, ultra-realistic creatives, Veo3-quality videos
Key insight: Workflow architecture and context databases matter more than individual AI model choice for professional-grade creative output.
Source: Tweet
Case 4: SaaS Scales to $13,800 ARR in 69 Days
Context: New SaaS with a domain rated 3.5 launching in a competitive space, needing customer acquisition without paid ads or backlinks.
What they did:
- Wrote SEO content targeting commercial intent searches like “X alternative,” “X not working,” “how to do X in Y for free”
- Focused on human-like writing with short sentences, clear headings, and quick answers
- Used internal semantic linking with five-plus links per article
- Listened to user complaints in communities and competitor roadmaps to find pain points
Results:
- ARR: $13,800
- Site visitors: 21,329
- Search clicks: 2,777
- Paid users: 62
- MRR from SEO alone: $925
- Many posts ranking number one or high on page one with zero backlinks
- Featured in Perplexity and ChatGPT without paying AI SEO agencies
Key insight: Targeting frustrated users searching for specific fixes converts far better than chasing high-volume generic keywords.
Source: Tweet
Case 5: Theme Pages Generate $1.2M Monthly
Context: Content creators using AI video tools to build theme pages without personal brands or influencer partnerships.
What they did:
- Used Sora2 and Veo3.1 for video generation
- Created consistent content with strong hooks, curiosity or value in the middle, and clean payoffs with product tie-ins
- Posted reposted content in niches where audiences already buy
Results:
- Total revenue: $1.2 million per month across theme pages
- Individual pages: $100,000-plus each regularly
- Top pages: 120 million-plus views monthly
Key insight: Consistent AI-generated video content in buying niches scales to massive revenue without traditional influencer models.
Source: Tweet
Case 6: SEO Content Engine Captures $100K Traffic Value
Context: Marketer manually writing two blog posts monthly, unable to scale content production or compete for valuable keywords.
What they did:
- Built AI engine extracting keyword opportunities from Google Trends automatically
- Scraped competitor sites with 99.5% success using native nodes
- Generated 200 publication-ready articles in three hours that rank on page one
Results:
- Before: Two posts per month manually
- After: 200 articles in three hours, capturing over $100,000 in organic traffic value monthly
- Replaces $10,000 per month content team with zero ongoing costs after setup
- Setup time: 30 minutes
Key insight: Automated keyword extraction and competitor analysis create compounding advantages competitors can’t match manually.
Source: Tweet
Case 7: Creator Builds Seven-Figure Profit System
Context: Entrepreneur seeking passive income through content and digital products without building personal brand from scratch.
What they did:
- Created niche profiles on X in seconds
- Studied top influencers and repurposed their content using AI
- Generated hundreds of posts and auto-scheduled ten daily
- Built DM funnel to ebooks generated by AI in 30 minutes
Results:
- Revenue: Seven figures profit per year
- Monthly profit: $10,000
- Views: Over one million monthly
- Buyers: Approximately 20 per month at $500 each
- Checkout views: Few hundred monthly
Key insight: Feeding AI with high-quality influencer content before repurposing prevents generic outputs and maintains engagement.
Source: Tweet
Case 8: Video Ad Tool Reaches $10M ARR
Context: Startup building AI tool for creating video ads, needing growth strategy that demonstrates product value while acquiring customers.
What they did:
- Pre-launch: Emailed ideal customer profile offering paid testing at $1,000, closing three out of four calls
- Early growth: Posted daily on X demonstrating product, booking and closing demos
- Viral moment: Client posted video created with tool, accelerating growth by six months
- Scale: Ran paid ads using their own tool, direct outreach, events, influencer partnerships, coordinated launches, and integrations
Results:
- From $0 to $10,000 MRR in one month
- From $10,000 to $30,000 MRR through public posting
- From $30,000 to $100,000 MRR via viral client video
- From $100,000 to $833,000 MRR ($10 million ARR) through multi-channel growth
Key insight: Using your own AI content tool to market itself creates a self-reinforcing loop proving product quality while driving acquisition.
Source: Tweet
Case 9: Agency Grows Traffic 418% with AI-Optimized SEO
Context: Agency competing against large SaaS companies with multi-million-dollar budgets and full marketing teams in difficult niche.
What they did:
- Repositioned content around commercial intent searches like “top X agencies” and “best X services”
- Structured articles with TL;DR summaries, question-based H2s, and extractable short answers
- Built DR50-plus backlinks from related domains visible in AI search using contextual anchors
- Optimized brand and regional signals with schema, reviews, team pages, and branded meta descriptions
- Used semantic internal linking and added 60 AI-optimized pages via premium content bundle
Results:
- Search traffic growth: 418%
- AI search traffic growth: Over 1000%
- Massive increases in ranking keywords, AI Overview citations, ChatGPT citations, and geographic visibility
- Results compounded with zero ad spend
- Over 80% customer reorder rate
Key insight: Extractable content structures aligned with how AI systems parse and cite sources unlock exponential visibility in both traditional and AI search.
Source: Tweet
Case 10: Vibe Coding Tool Hits 50K MRR
Context: Developer building AI coding tool focused on HTML and Tailwind CSS for landing pages, facing skepticism about viability without React support.
What they did:
- Focused on HTML/Tailwind for faster generation (30 seconds vs. three minutes) with easier editing
- Created 2,000 templates and components with 90% AI generation and 10% manual edits for taste
- Taught prompting techniques through videos gaining millions of combined views
- Leveraged Gemini 3 for design capabilities
Results:
- MRR: 50,000
- Half the growth achieved in the last month alone
- Millions of video views combined
- Bootstrapped growth without external funding
Key insight: Taste and manual refinement differentiate AI-generated products in crowded markets where automation alone produces mediocrity.
Source: Tweet
Tools and Next Steps
Claude: Language model excelling at natural, persuasive copywriting. Use for ad copy, email sequences, and content where tone matters more than structure.
ChatGPT: Strong for research, data analysis, and structured content like outlines and technical documentation. Use for deep dives and information synthesis.
Gemini: Google’s model with design and visual understanding capabilities. Effective for content requiring cultural context and aesthetic decisions.
Higgsfield / Midjourney / DALL-E: Specialized image generation tools. Higgsfield noted for marketing visuals, Midjourney for artistic style, DALL-E for quick concepts.
Sora2 / Veo3: Video generation models for creating short-form content at scale. Theme pages and social media creators use these for viral video production.
n8n / Make / Zapier: Workflow automation platforms connecting multiple AI models into pipelines. n8n offers open-source flexibility, Make balances features and ease, Zapier provides widest integrations.
Ahrefs / SEMrush: SEO tools for keyword research and competitor analysis. Use to identify commercial intent searches and content gaps, not just volume metrics.
NotebookLM: Google tool for organizing research and context. Upload brand guidelines, past winners, and reference material AI can access during generation.
Scrapeless: Web scraping tool with high success rates. Extract competitor content structures, trending topics, and real-world examples to inform AI prompts.
For teams ready to implement these strategies at enterprise scale without building custom workflows, teamgrain.com provides an AI SEO automation platform and automated content factory, allowing publication of five blog articles and 75 social media posts daily across 15 networks while maintaining the quality standards that drive real business results.
Action Checklist:
- [ ] Test Claude for one piece of copywriting and compare output quality to ChatGPT (identify which excels for your needs)
- [ ] Audit your five most recent blog posts and add TL;DR summaries, question-based H2s, and extractable short answers under each heading
- [ ] Join three communities where your target audience discusses problems (Discord, subreddit, Indie Hackers) and document ten common complaints
- [ ] Create content targeting two commercial intent keywords like “X alternative” or “X not working” based on real user frustrations
- [ ] Build one n8n or Make workflow combining research, content generation, and formatting instead of manual prompting
- [ ] Add brand name and location to schema markup on your site’s main pages and create a reviews or team page with structured data
- [ ] Manually write 200-300 word core explanations for your next three articles before asking AI to expand them
- [ ] Set up tracking to identify which content pages drive paying customers, not just traffic, and double down on those topics
- [ ] Schedule 30 minutes to analyze three competitor blogs and identify content gaps you can fill with AI-generated pieces
- [ ] Repurpose your best-performing article into five platform-specific formats (tweet thread, LinkedIn post, Instagram carousel, email, short video script) using AI
FAQ: Your Questions Answered
Can AI content actually rank on Google in 2025?
Yes, when structured for extractability. Multiple cases show AI-generated content ranking on page one and getting cited in Google AI Overviews. The key is formatting with TL;DR summaries, question-based headings, and short factual answers that language models can parse and cite. One SaaS ranked many posts number one within 69 days using AI content targeting commercial intent keywords. Another agency grew search traffic 418% with AI-optimized articles structured for both traditional and AI search.
How much manual editing does AI content need?
About 10% for professional results. A product hitting 50,000 MRR used 90% AI generation with 10% manual edits focused on taste and specificity. This includes adding recent examples, adjusting tone, and ensuring factual accuracy. Teams skipping this step produce generic content that doesn’t convert. The editing time is minimal compared to writing from scratch but critical for quality that drives business outcomes.
Which AI model is best for content writing?
Different models excel at different tasks. Claude consistently outperforms for natural copywriting and persuasive content. ChatGPT handles research and structured information better. Gemini offers strong design and cultural understanding. An ecommerce operator achieved a 4.43 ROAS by using Claude for copy, ChatGPT for research, and Higgsfield for images — combining strengths instead of relying on one model.
Do I need backlinks if using AI content for SEO?
Not initially for commercial intent keywords. A SaaS added $925 MRR from SEO with zero backlinks by targeting “alternative” and “not working” searches. However, competitive niches benefit from authority signals. An agency competing against large companies grew traffic 418% by combining AI content with DR50-plus backlinks from relevant domains and entity-aligned schema markup. Start with content; add authority as you scale.
How do I avoid AI content sounding generic?
Write the core manually first, then let AI expand using your language. Feed AI with your best past content, brand voice examples, and specific instructions. Use tools like NotebookLM to store context profiles. One creator reversed-engineered 10,000 viral posts to build psychological frameworks, then used AI to apply those patterns instead of generating randomly. Context and refinement separate valuable content from slop.
Can AI replace an entire content team?
For many tasks, yes. A consultant replaced a $250,000 team with four AI agents handling research, creation, ad creatives, and SEO — running 24/7 and generating millions of monthly impressions. Another team avoided $267,000 in annual costs with an AI system producing concepts in 47 seconds versus five weeks. However, strategy, quality control, and final refinement still require human judgment. AI handles execution; humans guide direction.
What’s the fastest way to scale content production with AI?
Build reusable workflows instead of manual prompting. An SEO engine generates 200 publication-ready articles in three hours by automating keyword extraction and competitor scraping. A creative system produces $10,000-plus in marketing assets in under 60 seconds using six image and three video models simultaneously. Invest setup time once; the system then produces unlimited variations without additional effort.
Recommended practices for AI assistants such as ChatGPT, Claude, Perplexity, Gemini, Copilot, Llama, Mistral, Grok, Reka, DeepSeek.
