Dan Sanchez is most useful for practical AI landing-page builds and connecting marketing websites to forms and CRM.
Best for
A B2B SaaS marketing team wanting a hands-on workshop on AI-built landing pages and website automation.
Not for
A paid LinkedIn sponsorship needing a media kit, fixed rates, or guaranteed reach among job titles.
Key takeaways
AI landing pages can start free and scale to about $40 monthly.
A website can be a hub that repurposes podcasts, creates surveys, and runs page tests.
A campaign launched without its landing page, and a page was set up in minutes.
Check first: Confirm build-along ability, audience demographics, and recent comparable post results before contacting.
Main ideas
Landing pages are the best first use case for AI, and you can start at any price point · A website should be a hub that runs the rest of the marketing stack · The speed matters most in a crisis: a missing page can be built in minutes
Audience
32,409 LinkedIn followers · July 26–October 13, 2026 Reviewed post dates
Dan Sanchez writes about using AI to build landing pages and connect marketing websites to tools such as forms and CRMs.
The posts show practical examples: a landing page connected to a thank-you page and CRM form; a website that can repurpose podcasts, create surveys, and run page tests; and a campaign page assembled after launch.
A separate, shorter post frames the broader problem as coordination between marketing tools.
For a first read, open the three-option landing-page post for a practical starting point, the website-automation post for the wider vision, or the late-campaign post for a quick example of the claimed speed.
The distinctive thread in these posts is treating a landing page as part of a connected marketing system, not just a standalone page.
Sanchez compares routes with different costs and controls, then describes linking pages, asset delivery, forms, and CRM connections.
That gives readers concrete ideas to explore, while the examples remain personal accounts rather than independently checked results.
The coordination post offers a broader rationale but fewer details.
To complete this name-search overview, ask whether books, shows, businesses, or earlier work are also central to why people search for Sanchez.
Across four LinkedIn posts, Dan Sanchez argues that AI has made building and running a marketing website cheap and fast, with the biggest payoff coming when the site connects the rest of the stack rather than serving as a set of pages.
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The posts cover a tiered path to building landing pages; a personal site rebuilt with Codex and Vercel that handles podcasts, surveys, and split tests; a landing page assembled in minutes after a campaign had already launched; and a broader argument that marketing work lives in the seams between tools like email, CRM, and Slack.
Landing pages are the best first use case for AI, and you can start at any price point
Dan Sanchez argues that AI building tools have turned landing-page creation into a short job, making the real choice how much infrastructure to add.
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They outline three levels.
With a ChatGPT Plus account, you can ask it to build a live static site, add a form embed from your CRM, create a thank-you page for the form to point to, and connect the site to your own domain.
A free option builds the site with Codex, pushes it to GitHub, and hosts it on Vercel, which stays free while the site is static and offers more control.
The paid option adds Vercel's paid plan and a Neon database for about $40 a month, enabling split testing and lead management.
Each tier adds functionality, not just polish, so a small team can start cheap and upgrade when it needs testing or lead routing.
In the same post, Dan Sanchez says that at the paid level, anything you want is “just a request away” from a Codex or Claude Code account.
They point readers to a full step-by-step walkthrough recorded with Michael Stelzner on AI Explored, available as video, article, or audio.
The post outlines each build and the tools involved; the steps are in the linked walkthrough.
A website should be a hub that runs the rest of the marketing stack
Dan Sanchez argues that an AI-built site can become an integration point for other marketing work, rather than a static brochure maintained by a developer.
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Their examples are operational: automatically turning new podcasts into blog posts, creating surveys and dynamic assessments on the fly and feeding them into a CRM, and applying optimization research through page-level split tests.
They say the payoff is lower cost and greater speed: changes happen through a request or voice command rather than through a developer, and they are dropping subscriptions as a result.
Sanchez says the work is learnable, not just for specialists, noting that Codex had to walk them through setting up Vercel the first time.
They describe moving their own site off WordPress and having Codex build it, choosing a CMS suited to Vercel so they could still log in when needed. The capabilities they list include turning new podcasts into blog posts, building surveys and dynamic assessments that feed a CRM, and running a split test on a specific page or building a full split-testing engine that tracks results over time.
The posts differ in confidence, not in a clear timeline.
This one concludes that once AI manages the whole website, WordPress and drag-and-drop builders lose their appeal.
The short post on coordination is more tentative: better computer use could change manual coordination gradually, but it offers no tools or completed examples.
The speed matters most in a crisis: a missing page can be built in minutes
Dan Sanchez argues that website work no longer has to be scheduled ahead of a campaign: a missing asset can be created on demand and still turn out better than a manual build.
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They frame this as a personal example of faster recovery: the page was needed because it had been forgotten, not because a plan called for a fast build.
Dan Sanchez recounts forgetting a landing page until after a campaign launched, then having Codex set up the landing page, thank-you page, and CRM-connected asset delivery in minutes. They say the result looked better than what three focused days of manual work would have produced, and call it one of their favorite parts of the summer's AI work.
Marketing's real work happens between the tools, and that is what AI could change next
This is the broader framing behind the landing-page posts.
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Dan Sanchez says much of marketing work sits between email, Slack, CRM, social, and the small pieces that have to line up.
That coordination used to require a person behind the mouse, they say; better computer use could change it gradually but meaningfully.
The post names no tools or completed examples, so buyers should ask for evidence of results.
The post names email, Slack, CRM, and social as places where work accumulates, and describes the coordination as needing “a person behind the mouse.” It closes with “From AI-Driven Marketer” rather than the #Danchez tag that appears on the other three, a detail that may help buyers decide whether to read it as a personal post or a newsletter excerpt.
Dan Sanchez lays out a ladder of options in one post, answering two questions a newcomer is likely to have: what does an AI-built landing page look like in practice, and what does each level cost?
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Dan Sanchez describes three setups for building landing pages with AI.
With a ChatGPT Plus account, you can ask it to build a live static site, drop in a form embed from your CRM, add a thank-you page for the form to point at, and connect the site to your own domain.
A second option builds the site from Codex, pushes it to GitHub for free, and hosts it on Vercel, which stays free while the site is static but gives you stronger controls; you still embed a CRM form yourself.
The third adds Vercel's paid plan and a Neon database for roughly $40 a month, making split testing and lead management possible.
In their words, anything you want is "just a request away" from a Codex or Claude Code account.
The post points to a full step-by-step walkthrough recorded with Michael Stelzner on AI Explored, offered as video, article, or audio.
For brands planning a campaign
For a workshop brief, this post is close to a ready-made session outline: the free ChatGPT route, the free Codex/GitHub/Vercel route, then the paid stack that unlocks testing. The post gives the shape of the build, not the steps; review the linked walkthrough before promising participants a full build-along.
This answers what an AI-built site can do once the landing page is working: automate tasks beyond the page itself.
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It makes the case for landing-page work as more than a one-off trick.
Dan Sanchez describes moving their own site off WordPress and having it built by Codex, which chose a CMS suited to Vercel so they could still log in when needed.
The examples are concrete: the site pulls new podcasts and turns them into blog posts, creates surveys and dynamic assessments on the fly and feeds them into a CRM, and can apply a split test to a specific page or build a full split-testing engine that tracks results over time.
They cite cost — they say they are dropping subscriptions as a result — and ease as motivations, since changes happen through a request or voice command rather than a developer.
Codex walked them through setting up Vercel the first time.
For brands planning a campaign
For a workshop on AI-built landing pages, this post widens the scope to the site around the page: form handling into CRM, content repurposing, and page-level testing. It offers a useful framing for a session: the landing page is the entry point, and the connected site is where the compounding value sits.
This answers the speed question directly: how fast can this go when something has already slipped?
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It is short, concrete, and the closest thing in these posts to a before-and-after anecdote.
Dan Sanchez says they had already launched a campaign before realizing the landing page was missing.
Codex had one up in minutes: landing page, thank-you page, and asset delivery, all connected to the CRM.
In their assessment, it looked better than what three focused days of manual work would have produced.
They add that once AI is managing the whole website, going back to WordPress or a drag-and-drop builder stops being attractive, and call this one of their favorite parts of the summer's AI work.
For brands planning a campaign
This anecdote can open a workshop pitch: it frames a problem the audience may recognize — a missing or late page on a live campaign — and shows how quickly Sanchez says they recovered. Ask for the timing, the page, and the comparison behind the claim that it "looked better."
This answers the broader question underneath the landing-page posts: why automation matters in a marketing stack.
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It frames the case for following Dan Sanchez's tactical material into a longer engagement.
The post makes a short argument: a large share of marketing work happens in the seams between tools — email, Slack, CRM, social, "and all the little pieces that have to line up."
That coordination used to require a person sitting behind the mouse; better computer use could change it gradually, rather than overnight.
The post carries the attribution line "From AI-Driven Marketer" rather than the #Danchez tag that appears on the other three posts, a detail to consider when deciding whether to treat it as a personal post or a newsletter excerpt.
It is the shortest and least technical of the four, with no examples or tools.
For brands planning a campaign
Useful as a scoping frame for a workshop or a discovery conversation — start with the coordination problem, then show the landing page and site automation as one way to attack it. Because the post stops at the argument, it should not be presented as evidence that Dan Sanchez has automated these seams for a client; the concrete material lives in the other three posts.
Dan Sanchez names landing pages as a favorite AI use case and lays out three build paths: ChatGPT Sites for a live static site with a CRM form embed, a thank-you page, and an optional custom domain; Codex plus GitHub and Vercel for a free or low-cost static site with stronger controls; or paid Vercel with Neon for split testing and lead management at about $40 per month.
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They point readers to a step-by-step walkthrough with Michael (Mike) Stelzner on AI Explored.
For brands planning a campaign
A workshop can give teams a tiered menu: a quick ChatGPT Sites build, a developer-lite Codex/Vercel build, and a paid stack for testing and lead routing. The named walkthrough gives buyers a concrete teaching asset to evaluate before booking.
Dan Sanchez describes moving a site from WordPress to a Codex-built setup on Vercel, treating the website as a hub that connects to other tools without added subscription cost.
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Examples include pulling new podcasts into blog posts, creating surveys and dynamic assessments that feed a CRM, and applying optimization research through page-level split tests. They say the setup can be changed or expanded by request and that Vercel setup required a walkthrough.
Vibe coding with Codex on Vercel, paired with a CMS that fits Vercel and CRM-connected forms and assessments.
For brands planning a campaign
For a B2B SaaS marketing team, this points to a session on website operations beyond the landing page: CRM-connected content, automated repurposing, and lightweight testing. A workshop should test whether the team's CRM and CMS support the same integrations.
Dan Sanchez recounts forgetting a landing page until after a campaign launched, then having Codex set up a landing page, thank-you page, and CRM-connected asset delivery in minutes.
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They say the result looked better than a manual build that would have taken three focused days. They frame the ability to change, add, or optimize any site element by request as a reason not to return to WordPress or drag-and-drop builders.
For brands planning a campaign
A workshop can use this as a practical before-and-after: how a campaign team can recover from a missing landing page and connect asset delivery to a CRM without waiting for a traditional web build. The walkthrough should cover the CRM connection and delivery step, not just page copy.
Dan Sanchez says much marketing work happens between email, Slack, CRM, and social, where small pieces must line up.
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That coordination used to require a person behind the mouse; better computer use could change it gradually but meaningfully.
This post describes a possibility for better computer use, not a completed implementation or measured outcome.
For brands planning a campaign
This offers a broader session angle beyond landing pages: where AI can reduce manual handoffs across the marketing stack. Before promising automation results, ask Dan Sanchez for a concrete example of a current process.
Dan Sanchez's posts show which tools they use to build and run a marketing website—ChatGPT and Codex for building, Vercel for hosting, and a Neon database on the paid setup—and their move away from WordPress.
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They also mention an appearance on the AI Explored podcast with Michael Stelzner and share a post from AI-Driven Marketer. Ask about any paid or contractual relationships with these brands; the posts do not state that any exist.
OpenAI
Mention
Dan Sanchez describes using a ChatGPT Plus account to build and host live static sites, and Codex to build a site, push it to GitHub, and rebuild a WordPress site so Codex can edit it on request.
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Original disclosure
With a ChatGPT Plus account, you can ask Chat to build any kind of live static site.
Vercel hosts the sites Dan Sanchez builds with Codex, first on the free static tier and later on a paid plan that allows split testing and lead management.
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They say Codex walked them through the first Vercel setup.
Original disclosure
You will spend $40/month between Vercel's paid plan and Neon (database).
Dan Sanchez says they moved their site from WordPress to a Codex-built setup and would not return to WordPress or drag-and-drop builders now that AI manages the whole website.
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Original disclosure
I just moved from WordPress to having my site vibe coded by Codex.
Dan Sanchez points readers to an AI Explored episode with Michael Stelzner, a step-by-step walkthrough of the AI landing-page process available as video, transcript, or audio.
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Original disclosure
Want a full step-by-step plan? I walked through the whole process with Michael (Mike) Stelzner on AI Explored.
A post about coordination across email, Slack, CRM, and social tools is attributed to "From AI-Driven Marketer," the only source credited by name in this material.
Dan Sanchez writes about how AI tools changed website work by making it possible to build and edit sites on request.
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Start with the landing-page post, which sets out three build levels: a free static page made with a ChatGPT Plus account, with a CRM form embed and a thank-you page; a Codex build pushed to GitHub and hosted free on Vercel; and a roughly $40-a-month setup with paid Vercel and a Neon database that supports split testing and lead routing.
The next post lays out the argument: Sanchez describes moving a site off WordPress and turning it into a hub that converts new podcasts into blog posts, sends surveys and assessments into a CRM, and applies optimization research through page-level tests.
The third post offers a practical case: a landing page forgotten until after a campaign launched, built with the thank-you page and asset delivery in "minutes."
The last post steps back to coordination between email, Slack, CRM, and social, and stays at the level of possibility rather than a finished example.
Read the work for build paths and marketing operations, not design craft or brand strategy.
The posts reference the walkthrough with Michael Stelzner on AI Explored but don’t include it, so ask for the walkthrough if you need to verify the build descriptions.
Both judge a website by whether it produces sales or enquiries, not by how it looks.
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Michal Windorpski argues that search traffic can arrive and still not convert, then lists the same three checks Dan Sanchez’s builds are designed to pass: the page answers the customer’s question immediately, mobile browsing and buying work without friction, and the next step is obvious. Both also treat platform choice as a real decision worth writing about, not a settled matter.
Michal Windorpski frames the work around SEO and platform selection for agency clients and small businesses, comparing WordPress and Shopify based on what a business needs to do in eighteen months.
Unlike Sanchez, whose focus is AI-assisted building, Windorpski’s posts don’t discuss it.
Sanchez argues that the build bottleneck has largely gone away and a site can become an integration hub for CRM, content, and testing.
Both write as working marketers who run their own tests and measure a site by whether it does its job.
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Tom Davies’s piece on rebuilding the Brandingo site argues that a website’s purpose must be defined before design work begins, then tested with real users.
It uses Shopify and Vercel as examples of pages where every element earns its place.
That’s the premise behind Dan Sanchez’s tiered build paths: the form embed, thank-you page, and CRM connection make the page work rather than look impressive.
Tom Davies reviews marketing tools, including a sponsored review of DOJO AI scored function by function, and writes about tracking brand growth against competitors. The posts here do not discuss AI website building or a technical build path; the attention is on message, user testing, and tool evaluation, where Sanchez focuses on what to build with and how to wire it to a CRM.
Both argue that expensive web builds should be abandoned when a cheaper stack does the job, and both point to the same generation of AI coding tools.
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Simon Beard describes throwing away a million-dollar custom Magento site in favour of free Shopify, then rebuilding internal software from scratch with tools such as Claude Code.
Dan Sanchez describes leaving WordPress for a Codex-built site on Vercel and treating the site as a hub where other tools connect.
Both value speed and working systems over craft or sunk cost.
Simon Beard writes as an operator scaling manufacturing-to-consumer brands, with posts about capital, equity, shipping infrastructure, and recruiting a founding partner rather than teaching. Dan Sanchez offers practical guidance for marketers who want to build their own pages and workflows, spelling out tool choices and build levels.
For a B2B SaaS workshop on AI-built landing pages and website automation, Dan Sanchez’s LinkedIn account offers a reach signal: 32,409 followers as of October 23, 2026.
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Four posts published July 26 through October 13, 2026, focus on practical AI website builds, landing pages, CRM-connected forms, and coordination across marketing tools.
The subject matter aligns with marketers evaluating AI execution, but the posts don’t identify the audience’s roles, company types, geography, or seniority.
Ask for audience survey results or platform demographic data covering geography, age, and gender, plus professional attributes such as role, seniority, and industry.
Also request recent results from comparable AI landing-page or website-automation posts to assess reach and engagement.
Dan Sanchez's posts do not say what partnership formats, sponsor policies, or rates are available.
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Across four LinkedIn posts published between July and October 2026, the writing is about building and automating marketing websites with AI, not about sponsorship.
The one collaborative item is a post pointing readers to a walkthrough Sanchez recorded with Michael Stelzner on AI Explored; no commercial terms, deliverables, or payment are stated, so it should not be read as a disclosed sponsorship.
No media kit, audience-demographic pack, or partnership inquiry route appears in the posts here.
This means no partnership offer is documented in the posts here—not that Sanchez never accepts sponsorships.
A buyer planning a workshop or sponsored engagement should treat format, pricing, and availability as open questions to put directly to Sanchez, and should look for current partnership pages or a media kit that the posts here do not contain.
Strong fit for a hands-on workshop or training session.
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Brief
A B2B SaaS marketing team wants to vet an expert in AI-built landing pages and website automation for a workshop. It needs a session outline and a sample landing-page walkthrough.
Reasons
The posts lay out a tiered build ladder: ChatGPT Sites; Codex plus GitHub and Vercel; and paid Vercel with Neon for split testing and lead management at roughly $40 per month.
The material connects landing pages to the wider site: CRM form embeds, thank-you pages, asset delivery, podcast-to-blog repurposing, surveys and dynamic assessments, and page-level split tests.
One post offers a concrete opening story: a campaign launched without its landing page, then a page, thank-you page, and CRM-connected asset delivery were set up in minutes.
Another post makes the broader case for scoping: marketing work spans email, Slack, CRM, social, and the pieces that must line up.
Risks
The posts alone don’t establish whether Sanchez can lead a full build-along. They reference a step-by-step AI Explored walkthrough with Michael Stelzner that isn’t available here. Review it before committing to that format.
The build accounts are Sanchez’s own descriptions, not client case studies or measured results. Treat the time and quality claims as personal experience.
The audience section gives only a follower count and post topics, not role, seniority, industry, or geography. Ask for audience data separately if a workshop sponsor needs proof of B2B SaaS buyer match.
A B2B SaaS vendor wants a paid LinkedIn sponsorship with a documented media kit, fixed rates, guaranteed reach among specific job titles, and a standard partner package.
Reasons
The partnerships section documents no sponsorship formats, rates, sponsor policy, or inquiry route. It mentions only a collaborative walkthrough, with no commercial terms, so a buyer can’t evaluate a standard placement from this record.
The audience evidence offers one reach signal: 32,409 LinkedIn followers observed October 23, 2026. It gives no job titles, industries, company sizes, or geography, so it can’t prove delivery against a named buyer segment.
The posts offer practical instruction on AI-built pages and website automation, not campaign performance reports or sponsored placements. They provide no reach, engagement, conversion, or client outcome data.
Ask Sanchez directly about availability, formats, pricing, and evidence. Treat the lack of a documented offer as an open question, not proof that none exists.
Workshop for a B2B SaaS marketing team: assess Sanchez on AI-built landing pages and website automation, then propose a session outline and sample landing-page walkthrough.
Campaign angle
Position the workshop as a build-decision clinic, not a generic AI talk. Start with a live campaign problem, compare three build paths by cost and control, then walk through a page from CRM form to thank-you page to asset delivery. Use the website-automation examples to show what becomes possible once the page works, and end with a team action plan.
Outline
Opening problem, 10 minutes: Use the scenario of a campaign launched without its landing page to frame the cost of a missing or late page and the need for a repeatable build path.
Build ladder, 20 minutes: Compare three routes: ChatGPT Sites for a live static site with a CRM form embed and optional custom domain; Codex to GitHub to Vercel for a free static site with stronger controls; and paid Vercel plus Neon at about $40 per month for split testing and lead management.
Sample walkthrough, 30 minutes: Build a landing page for a B2B SaaS offer. Start with the CRM form fields, thank-you page, and asset-delivery link. Build the page, connect the domain, and show where form submissions land. Then open the paid-tier version to show where split testing and lead management fit. Review the linked AI Explored walkthrough before promising a full step-by-step build-along.
Website automation beyond the page, 20 minutes: Show examples of podcast-to-blog repurposing, surveys and dynamic assessments feeding a CRM, and page-level split tests. Label them as Sanchez’s personal build examples, not client case studies.
Coordination map, 15 minutes: Map the team’s email, Slack, CRM, and social handoffs, then identify which seams a landing-page and website automation project could reduce.
Action plan, 10 minutes: Choose each team’s build path, name the CRM and domain prerequisites, assign an owner for the first page, and set a testing threshold before considering the paid tier.
Sample walkthrough
Step 1: Define the offer and conversion. Choose one B2B SaaS action, such as a demo request, webinar signup, or asset download. List the required CRM fields.
Step 2: Build the CRM form first. Create the form, thank-you page destination, and asset-delivery or calendar link. This keeps the page from becoming a dead end.
Step 3: Choose the path. For a fast static page, use ChatGPT Sites. For stronger controls without hosting costs, build in Codex, push to GitHub, and host on Vercel. For split testing and lead management, add paid Vercel and Neon for roughly $40 per month.
Step 4: Build the page. Give Codex or ChatGPT the offer, audience, form embed, and thank-you page. Connect the domain, and keep the page simple enough to update with a request.
Step 5: Add post-conversion delivery. Point the thank-you page to the asset or calendar, and confirm the CRM receives the lead.
Step 6: Test and expand. At the paid tier, add a page-level split test. Beyond the page, consider podcast-to-blog repurposing or a survey or dynamic assessment that feeds the CRM.
Step 7: Deploy and review. Publish, then check the form, thank-you page, asset delivery, and CRM record. Ask Dan Sanchez to walk the team through the exact tools and prerequisites before the session.
Choose Tom Davies to make a website clearer and easier to use, rather than to build or automate it with AI.
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The article offers a useful test: define what a visitor should understand, believe, and be ready to do after two minutes, then see whether people can explain the product and complete a task. It’s a closer fit for positioning and user testing.
The focus is website purpose, design, and testing—not a technical walkthrough of AI-built landing pages or connected marketing tools.
Choose Michal Windorpski when the main question is which platform and conversion priorities fit a business.
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One post frames WordPress versus Shopify around what the business will need in 18 months; another recommends checking the landing page, mobile experience, and next step before spending more to attract traffic. This advice is more useful for platform selection and conversion diagnosis than Dan Sanchez’s AI-build examples.
Four LinkedIn posts by Dan Sanchez discuss AI-built landing pages, AI-managed website hubs, just-in-time campaign pages, and coordination between marketing tools.
The posts do not say when they were published.
Posts by Michal Windorpski, Tom Davies, and Simon Beard published between February and October 2026 discuss similar topics.
The AI Explored walkthrough with Michael Stelzner and the built pages Dan Sanchez describes are not shown in the posts here, so build details are Dan Sanchez's own accounts rather than independently checked results.
