Matt Warren

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Category: Marketing

Brand building, content strategy, growth, and campaigns

  • Lessons From a Decade of Programmatic SEO

    Lessons From a Decade of Programmatic SEO

    This is the final post in a three-part series on programmatic SEO. Part one covered what it is and whether it’s worth your time. Part two walked through the simplest way to get started. This post is the retrospective — what I’ve learned from building programmatic SEO projects since 2014, what actually works, and what’s coming next.

    Lesson 1: Google Always Catches Up

    In 2014, my Automatic Blog Machine product was making money. Article spinning worked. Keyword stuffing worked. Building a hundred sites with rotated content and pointing links between them worked. For about six months.

    Then Google’s Panda update got smarter, and everything I’d built evaporated. Rankings disappeared overnight. Revenue went to zero. The sites were worthless.

    Every generation of programmatic SEO has its version of this story. Somebody finds a technique that games the algorithm, it works for a while, and then Google closes the loophole. Article spinning died. Exact-match domain networks died. Private blog networks died. Thin template pages with swapped city names and nothing else — those died too.

    The lesson isn’t that Google is unbeatable. It’s that any approach built on fooling the algorithm has an expiration date. The only programmatic SEO that survives long-term is the kind that would still make sense if Google didn’t exist — pages that people actually want to read.

    Lesson 2: The Quality Bar Keeps Rising

    What counted as “good enough” in 2014 would get you penalized today. And what’s acceptable today will probably look thin in three years.

    In the article spinning era, uniqueness was the bar. If the text didn’t trigger a duplicate content check, it was “good enough.” Nobody was reading these pages — they existed to rank, not to serve readers.

    In the template era, usefulness was the bar. If the page had real data — actual business listings, real product specs, genuine local information — it could rank even with a formulaic template. The information was valuable even if the presentation was boring.

    Now, in the AI era, the bar is comprehensive quality. The page needs real data, good writing, proper formatting, useful structure, internal links, and a design that doesn’t scream “this was generated.” Readers expect the same quality from a programmatic page that they’d expect from a hand-written one.

    This isn’t Google being arbitrary. It’s reflecting what users actually want. Every time people complain about search quality — and they complain a lot — Google tightens the screws. The sites that survive each tightening are the ones that were already over-delivering on quality.

    The practical takeaway: build to a quality standard that’s higher than what currently ranks. If the top results for your target query are mediocre, don’t match them — beat them. That margin is your insurance against the next algorithm update.

    Lesson 3: Small Sites Can Win Specific Niches

    The biggest misconception about programmatic SEO is that you need to be Yelp or Zapier to succeed. You don’t. Those companies succeed because they operate at massive scale across broad categories. But scale and breadth aren’t the only ways to win.

    Small, focused sites win by going deeper than the big players bother to. A mega-site might have a page for “plumbing in Austin” but it won’t have a page about Austin’s specific water hardness regulations and what they mean for residential plumbing maintenance. That level of specificity is where the opportunity lives.

    The best small-site programmatic SEO projects share three traits:

    Deep niche expertise. The creator knows the subject well enough to spot what’s missing from existing content. They’re not just generating pages — they’re filling genuine information gaps.

    Specificity that big sites can’t match. A large directory has breadth but not depth. They can’t afford to write 2,000-word deep dives for every long-tail variation. You can — especially with AI handling the research and drafting.

    Willingness to maintain and update. Most programmatic sites get published and abandoned. The ones that win long-term keep their data fresh. If your competitor pages reference 2023 pricing, update yours to 2026 pricing. If a local regulation changed, update your city page. This sounds obvious, but almost nobody does it.

    Lesson 4: Internal Linking Is the Multiplier

    I underestimated internal linking for years. Then I saw the data.

    A set of programmatic pages with no links between them behaves like a hundred isolated blog posts. Google crawls them independently, doesn’t understand the relationship between them, and treats each page as a standalone piece of content competing on its own merits.

    The same set of pages with intentional internal linking becomes a content hub. Google understands the topical relationship. Authority flows between pages. When one page ranks well, it lifts the others. The whole is genuinely greater than the sum of its parts.

    For programmatic SEO specifically, the linking structure should be systematic:

    • Every page links to the hub — the main topic page that anchors the entire collection
    • Related pages link to each other — city pages in the same state, comparison pages in the same category, FAQ pages on related topics
    • The hub links to its best-performing spokes — as you learn which pages rank, link from your strongest page to support the weaker ones
    • External content links in too — your blog posts, your about page, your other site content should all link to relevant programmatic pages

    When I added systematic internal linking to a set of pages I’d published months earlier, some of them jumped from page 3 to page 1 within weeks. The content hadn’t changed. The links made Google understand what it was looking at.

    Lesson 5: Failures Teach More Than Successes

    I want to be honest about the projects that didn’t work, because the failure modes are instructive.

    The 10,000-page experiment (2024). After writing about programmatic SEO as a concept, I decided to test it at scale. Build a large site, publish thousands of pages, see what happens. The content was AI-generated with some data enrichment, but the quality was inconsistent. Some pages were genuinely useful. Many were thin. Google’s March 2024 core update hit the site hard. Traffic dropped 70% in a week. The lesson: volume without consistent quality is a liability, not an asset.

    The comparison site (2023). I built a site with product comparison pages using early ChatGPT-generated content. The information was plausible but not always accurate. Some product features were hallucinated. Some pricing was wrong. Readers complained in comments. Google noticed the bounce rates. The site never gained traction. The lesson: AI content without real data sourcing produces pages that look right but aren’t. Readers can tell.

    The directory that worked (2025). On the other hand, a small directory project — fewer than 100 pages — that aggregated genuinely hard-to-find local information performed well from day one. Each page took longer to produce because the data required real research. But because the information wasn’t available elsewhere in a consolidated format, the pages ranked quickly and stayed ranked. The lesson: less content, more value per page, wins.

    The pattern across every failure was the same: I prioritized quantity over quality. Every success came from the opposite decision.

    Lesson 6: The Maintenance Problem Is Real

    Here’s something nobody talks about in programmatic SEO guides: what happens after you publish?

    Content decays. Prices change. Businesses close. Regulations update. Links break. Data goes stale. A page that was accurate when you published it becomes misleading six months later — and misleading content eventually gets outranked by something fresher.

    For hand-written blog posts, this is manageable. You have 50 posts, you review them periodically, you update what’s outdated. For 500 programmatic pages, the maintenance burden is significant.

    The solutions I’ve found:

    Build refresh into the pipeline. If your data comes from scrapeable sources, schedule regular re-scrapes. Have the AI compare new data to old data and flag pages that need updates. Automate the parts that can be automated.

    Prioritize maintenance by traffic. Not every page needs to be updated on the same schedule. Your top 20% of pages by traffic deserve monthly reviews. The rest can be quarterly or annual. Focus your attention where it has the most impact.

    Design for easy updates. If your page template separates structured data from narrative content, updating the data is easy — just refresh the numbers. If every fact is buried in flowing prose, updating requires rewriting paragraphs. Think about maintainability when you design your template.

    Remove pages that can’t be maintained. If a category of pages depends on data you can no longer source reliably, it’s better to remove those pages than to let them go stale. A smaller, accurate site outperforms a larger, unreliable one.

    Lesson 7: AI Changed Everything (But Not How You Think)

    The biggest shift in programmatic SEO isn’t that AI can write content. It’s that AI can do research.

    Content generation was always the easy part. Even before AI, you could spin articles, fill templates, generate text. The hard part was getting accurate, specific, useful information for each page. That required actual research — visiting sources, extracting data, cross-referencing facts, understanding context.

    What’s different now is that AI agents can do that research at scale. Claude Code can browse the web, read source documents, extract specific data points, and compile them into structured content — for every row in your spreadsheet. That’s not just faster writing. That’s faster research, which was always the bottleneck.

    This changes the economics completely. A project that would have required weeks of manual research to populate with real data can now be researched in hours. The constraint shifts from “can I gather enough information?” to “is this information worth publishing?”

    But here’s the nuance: AI research still needs human judgment. The AI doesn’t know which sources are trustworthy for your niche. It doesn’t know when a fact is technically accurate but misleading in context. It doesn’t know the difference between a useful page and a page that merely looks useful. That judgment is still yours — and it’s what separates programmatic SEO that works from programmatic SEO that gets penalized.

    Where This Is All Heading

    Three trends are shaping the future of programmatic SEO:

    AI search is changing the game. Google’s AI Overviews, ChatGPT’s search, Perplexity — these tools synthesize information from across the web and present it directly to the user. If an AI can answer the query by reading your page and summarizing it, the user might never visit your site. This means programmatic pages need to offer something beyond summarizable facts — interactive tools, downloadable resources, visual comparisons, or depth that can’t be condensed into a snippet.

    E-E-A-T matters more than ever. Google’s emphasis on Experience, Expertise, Authoritativeness, and Trustworthiness is a direct response to the flood of AI-generated content. Sites with a real author, real expertise, and real experience behind them get preferential treatment. For programmatic SEO, this means connecting your template pages to your broader brand — author bios, links to your other work, evidence that a real person stands behind the content.

    The bar for “unique value” keeps climbing. Aggregating publicly available information into a cleaner format used to be enough. Increasingly, the winning programmatic sites add something genuinely new — original analysis, proprietary data, interactive tools, expert commentary layered on top of the aggregated data. The template is just the delivery mechanism. The unique value is what gets the page ranked.

    The Only Rule That Never Changes

    After a decade of building, failing, rebuilding, and occasionally succeeding at programmatic SEO, one principle has held constant through every algorithm update, every technology shift, and every competitive wave:

    If the page helps the reader, it will eventually rank. If it doesn’t, it eventually won’t.

    Every technical decision — the template structure, the data sources, the publishing pace, the internal linking, the AI tooling — is in service of that one question. Would a real person find this page useful?

    Build for that standard, and the algorithm updates become opportunities instead of threats. The sites that survive Google’s crackdowns are always the ones that were building for readers, not for robots.

    The tools have never been better. AI can research, write, and publish at a scale that was unimaginable even two years ago. But the strategic question is the same one it’s always been: are you creating something of value, or are you just creating more noise?

    If you’ve read all three posts in this series, you have everything you need to answer that question for yourself. Start with the concept. Build with the simplest approach that works. And keep the long view in mind — because the sites that win in programmatic SEO are the ones that are still useful five years from now.

    For more on building AI-powered content workflows, check out how I use AI to write and publish blog posts. And if you want to see the original post that started this whole series, that’s here.

  • The Simplest Programmatic SEO You Can Build Today

    The Simplest Programmatic SEO You Can Build Today

    In the last post, I explained what programmatic SEO is and when it’s worth pursuing. The short version: it’s creating web pages using templates and data instead of writing every page by hand.

    But knowing what it is and actually building it are different things. Most guides jump straight to complex tech stacks — custom databases, headless CMS platforms, expensive plugins — and lose 90% of readers before they publish a single page.

    The reality in 2026 is that AI has collapsed most of those steps. You don’t need to manually copy-paste pages from a spreadsheet. You don’t need to learn a page builder plugin. You can start with an AI assistant, a WordPress site, and a clear idea of what pages you want to create.

    Step 1: Let AI Build Your Data Set

    Every programmatic SEO project starts with a list of pages. The old advice was to sit down with a spreadsheet and fill in rows by hand. That still works — but why would you?

    Instead, start by telling an AI assistant what you’re trying to build. Be specific about your niche and what kind of pages you want. For example:

    “Give me a list of 50 cities in Texas with populations over 50,000, along with their county, population, and top three industries.”

    Or: “Research and list every competitor in the meal prep delivery space, with their pricing, delivery areas, and key differentiators.”

    Or: “What are the 30 most common questions people ask about home solar installation, organized by stage of the buying process?”

    The AI generates your seed data in seconds. Export it to a Google Sheet or CSV file, and you’ve got the skeleton of your project. Each row is a potential page. Each column is a variable that changes between pages.

    Here’s where the multiplication happens. Say you have 20 cities and 5 services. That’s 100 potential pages — “[service] in [city]” — generated from two simple lists. Add industries, and you’ve got another dimension. The data set grows fast.

    Keep a local copy of everything. Download your research, cache your data sources, save reference material to your computer. You don’t want to re-fetch the same information every time you work on the project. A local folder with your spreadsheets, source documents, and reference data becomes your project’s knowledge base.

    Step 2: Design Your Template

    Before you generate a single page, you need to know what a good page looks like. This is the most important step, and it’s worth spending real time on.

    Pick one row from your data set — one city, one product, one question — and build the best possible page for it. Not blindly with AI. By hand. Think about what someone searching for that query actually wants to know, and make sure the page delivers it. Your pages need to be good enough that people stay and read.

    This manual page becomes your template. Study it:

    • What headings did you use?
    • What data points appear on every page versus what’s unique?
    • How long does it need to be to genuinely answer the question?
    • What internal links connect it to related pages in your set?

    Once you’re happy with the template, describe it clearly — the structure, the sections, the tone, what goes where. This description becomes your prompt for generating every other page.

    Step 3: Establish Your Brand Guide Early

    This is something most programmatic SEO guides skip entirely, and it’s why so many pSEO sites feel like they were stamped out of a factory.

    Before you generate content at scale, decide on your brand voice and visual identity. Write it down. These decisions are hard to change later, and consistency is what separates a site that feels trustworthy from one that feels like spam.

    For writing voice, decide:

    • First person or third person?
    • Authoritative and expert, or friendly and conversational?
    • Technical language or plain English?
    • What phrases or patterns does your brand use? What does it avoid?

    Feed this brand guide to your AI as context for every page it generates. The difference between “write a page about solar installation in Austin” and “write a page about solar installation in Austin using this voice guide” is enormous. Without it, every page will sound like generic AI output. With it, they’ll sound like they came from the same knowledgeable author.

    For visual identity, decide:

    • What style of images will you use? AI-generated, stock photos, custom graphics?
    • Pick a specific image style and dial in the prompt so it’s consistent across all pages
    • Choose a color palette and typography that carries through the site
    • Decide on a layout template before you start publishing

    Spend an afternoon getting your image generation prompt right. Test it on 5-10 variations and make sure the results feel cohesive. A site where every hero image looks like it belongs to the same brand signals quality. A site where every image looks randomly generated signals the opposite.

    Step 4: Generate and Publish With AI

    Here’s where modern tools change the game entirely. You don’t need to manually create pages one by one, and you don’t need an expensive import plugin to do it for you.

    An AI coding assistant like Claude Code can take your spreadsheet, your template, and your brand guide and do the heavy lifting:

    1. Research each row — For every entry in your data set, the AI can search the web, pull real information from multiple sources, and compile facts that are specific to that page. A page about “plumbing services in Austin” shouldn’t contain generic plumbing advice — it should reference Austin’s actual building codes, local licensing requirements, and water quality specifics.
    2. Write the content — Using your template structure and brand voice, the AI drafts each page. Because it’s working from real research rather than generating from memory, the content is grounded in verifiable facts.
    3. Publish directly — Tools like the WordPress REST API let AI publish pages directly to your site, complete with formatting, categories, tags, and featured images. No copying and pasting between tools.
    4. Review each page — And this is the step you never skip. Read every page before it goes live, especially in the beginning. Check that the facts are accurate, the voice is consistent, and the page would pass the quality test from the last post: would a real person feel their time was respected?

    For the first 10-20 pages, review every single one. As you get confident that your template and prompts produce reliable output, you can shift to reviewing a sample — but never stop reviewing entirely.

    Start Slow, Accelerate Later

    There’s a temptation to use these tools to publish hundreds of pages in a weekend. Resist it.

    When a new site suddenly appears with 500 pages, Google notices. And not in a good way. A brand-new domain with a flood of content looks exactly like the kind of spam site that Google’s algorithms are designed to catch — regardless of how good the content actually is.

    The better approach is to start with a handful of pages and grow steadily:

    Week 1-2: Publish 5-10 of your best pages. Obsess over quality. Make sure every fact is right, every image looks good, every internal link works.

    Week 3-6: Add 3-5 pages per week. Monitor which pages get indexed and start appearing in search. Pay attention to what Google seems to like.

    Month 2-3: If pages are getting indexed and attracting some traffic, increase your pace. Maybe 10 pages per week. Keep reviewing quality.

    Month 3+: If the signal is positive, you can ramp up further. But always tie the pace to the quality you can maintain.

    This gradual approach does two things. It gives Google time to build trust in your domain. And it gives you time to learn what’s working — which page structures perform best, which topics attract traffic, and which ones fall flat. That feedback loop is worth more than a thousand pages published blind.

    Picking Your First Project

    The hardest part isn’t the technology. It’s choosing what to build.

    Here are five proven patterns that work well for a first project, ordered from simplest to most ambitious:

    1. FAQ pages for your niche. Take the 20-30 most-asked questions in your field and create a dedicated page for each one. Have AI research the best current answer for each, pulling from authoritative sources. This is the lowest-risk starting point because each page targets a specific long-tail query with clear search intent.

    2. Comparison pages. “[Product A] vs [Product B]” for every meaningful combination in your space. AI can research current pricing, features, and reviews for each product. The data changes, so keep local copies and plan to refresh these periodically.

    3. Location + service pages. “[Service] in [city]” combinations. This is the classic multiplication approach — 10 services across 20 cities gives you 200 pages. AI can research city-specific details (regulations, demographics, local competitors) to make each page genuinely useful rather than just swapping the city name.

    4. Tool or resource directories. Curate every tool, service, or resource in a specific category. AI can research pricing, features, and user reviews from across the web, then present it in a consistent format. The value is in the consolidation — saving the reader from visiting 30 different websites.

    5. Data-driven analysis pages. Turn public datasets into readable insights. Government databases, industry reports, and public APIs contain enormous amounts of information that nobody has bothered to make accessible. AI can process raw data and present it in plain language for specific audiences.

    Pick one. Build 10 pages. See what happens.

    Common Mistakes to Avoid

    Having tried (and failed at) programmatic SEO more than once, here are the mistakes that kill projects:

    Starting too big. Don’t plan 1,000 pages before you’ve proven 10 work. Build the smallest possible version, see if it gets traffic, then scale what works.

    Skipping the brand guide. Without a consistent voice and visual identity, your site will feel like a content farm even if the information is good. Invest the time upfront.

    No quality review. Publishing AI-generated pages without reading them is how sites get penalized. Review every page early on. Spot-check as you scale. Never publish blind.

    Thin content. If your template produces pages with 200 words of generic text and a data table, that’s not enough. Each page needs to genuinely answer the searcher’s question. If you can’t make a page useful, don’t create it.

    Ignoring internal linking. A hundred orphan pages with no links between them won’t perform. Every page should link to related pages in your set, and your set should link back to your main site content. Build the web of connections from day one.

    Sloppy images. Inconsistent or obviously AI-generated images with different styles on every page undermine trust. Pick one style, refine the prompt, and stick with it across the entire site.

    Going too fast on a new domain. Publishing hundreds of pages on a fresh domain in your first week is a red flag to Google. Start slow, build trust, accelerate when you see positive signals.

    What to Do This Week

    If this approach sounds interesting, here’s a concrete starting point:

    1. Pick a pattern from the five options above that fits your expertise or business
    2. Ask an AI assistant to generate your seed data — cities, competitors, questions, whatever your pattern requires
    3. Build one perfect page by hand — this becomes your template and quality benchmark
    4. Write your brand guide — voice, tone, image style, what to avoid
    5. Search for your target queries and compare your template page to what’s already ranking

    If your page is better than what’s currently out there, you’ve found your project. The tools to scale it are available right now — and most of them are free or close to it.

    In the next post, I’ll share lessons from a decade of building programmatic SEO projects — what actually works long-term, what gets penalized, and where this is all heading as AI gets more capable. For more on how AI fits into content workflows, check out my AI-assisted content strategy. And if you’re a builder looking for the technical deep dive, growth engineering with Claude Code covers the pipeline side in detail.

    But start with the pattern and the brand guide. Everything else follows from those two decisions.

  • What Is Programmatic SEO (And Is It Worth Your Time?)

    What Is Programmatic SEO (And Is It Worth Your Time?)

    A decade ago, I launched a product called Automatic Blog Machine. The idea was simple: use natural language processing to find synonyms and rotate sentence structures so that scraped content wouldn’t get flagged as duplicate text. Spin a paragraph enough times and Google’s algorithms couldn’t tell it was the same article published across a hundred different sites.

    It worked — for about six months. Then Google got smarter, the rankings disappeared, and I learned an expensive lesson about building on a foundation of trickery.

    That was my introduction to programmatic SEO. And while the tools have changed dramatically since then, the core question hasn’t: can you create content at scale without it being garbage?

    What Programmatic SEO Actually Is

    Programmatic SEO is creating web pages using templates and data instead of writing every page by hand. That’s it. No magic, no dark art.

    Think about it this way. A real estate site with a page for every neighborhood in a city — those pages aren’t hand-written. They pull from a database: median home price, school ratings, walkability score, recent sales. The template is the same, but the data makes each page unique and useful.

    That’s programmatic SEO at its simplest. You define a pattern, plug in data, and generate pages that target specific search queries.

    Some real-world examples that are probably already in your life:

    • Yelp has a page for every “best [restaurant type] in [city]” combination
    • Zapier has integration pages for every app pairing — thousands of them
    • NerdWallet has comparison pages for financial products across every category
    • Tripadvisor has pages for every hotel, restaurant, and attraction in every city on Earth

    These aren’t hand-crafted blog posts. They’re templates filled with structured data, and they drive millions of organic search visits every month.

    The Spectrum of Complexity

    Here’s where people get intimidated. They hear “programmatic SEO” and picture a team of engineers building complex data pipelines. But the spectrum is much wider than that.

    The simple end: A Google Sheet with 50 rows of FAQ questions, turned into individual pages on a Wix or WordPress site. Each page targets a specific long-tail search query. No code required.

    The middle: A WordPress site with a template that pulls in data from a spreadsheet or simple database. Maybe you’re building city-specific landing pages for a local service, or comparison pages for products in your niche.

    The advanced end: A full pipeline that scrapes data sources, enriches it with AI, generates unique content for each page, and publishes automatically. This is where tools like Claude Code come in — but you don’t need to start here.

    The point is that programmatic SEO isn’t binary. You don’t need a sophisticated tech stack to benefit from the approach. You need a repeatable pattern and data to fill it.

    A Decade of Cat and Mouse

    My Automatic Blog Machine story isn’t unique. The history of programmatic SEO is really the history of people trying to create content at scale and Google trying to separate the valuable from the worthless.

    The early era (2010-2015): Article spinning, keyword stuffing, link farms. Content was generated to game algorithms, not to help readers. Google’s Panda and Penguin updates torched most of it. My product was part of this wave, and it deserved to get squashed.

    The template era (2016-2022): Smarter operators moved to database-driven templates. If you had genuinely useful structured data — business listings, product specs, local information — you could build pages that actually served a purpose. This worked better because there was real information behind each page, even if the presentation was formulaic.

    The early AI era (2023-2024): ChatGPT arrived, and suddenly everyone could generate “unique” text at scale. But GPT-2 and GPT-3 era content had obvious problems. The hallucinations were rampant. There was no way to connect the model to real data sources, so it would confidently make up facts, invent statistics, and fabricate references. If you read enough AI-generated content from that period, you developed a sixth sense for it — the same vague structure, the same filler phrases, the same lack of specificity.

    Some people tried to work around this. I experimented with using web search APIs to pull real content, then feeding it to ChatGPT to create summaries and rephrase things in a more natural way. It was better than pure hallucination, but still produced that unmistakable AI voice. And Google was getting better at detecting it.

    Where we are now (2025-2026): This is where things genuinely changed. The current generation of AI tools — particularly agent-based systems like Claude Code — can do something the earlier models couldn’t: go out on the internet, find ten real references for every claim, consolidate and synthesize that information, and present it in a way that actually helps the reader.

    That’s a fundamentally different value proposition than spinning synonyms or generating hallucinated text.

    The Real Turning Point

    Here’s the thing that changed my mind about programmatic SEO after years of skepticism.

    When you can connect AI to real data sources — web scraping, APIs, databases, live search results — you’re not faking content anymore. You’re doing genuine research at scale. The AI becomes a research assistant that can:

    • Pull together information from dozens of sources for a single page
    • Take complicated language (legal documents, scientific papers, technical specs) and rephrase it for different audiences
    • Cross-reference facts across multiple sources to reduce hallucination
    • Tie together related concepts in ways that would take a human researcher hours

    Could someone get this information by doing a Google search themselves? Maybe. Could they have a conversation with an AI chatbot and get similar answers? Possibly. But if the value you’re providing involves pulling together many sources, consolidating scattered information, and presenting it in a clear format — that’s real work, even if a machine is doing it.

    Think about a directory site that aggregates local business information from public records, review sites, and social media — then presents it in a clean, searchable format with plain-language summaries. That’s providing genuine value. The information exists on the internet already, but it’s scattered across dozens of sites in inconsistent formats. Consolidating it is the service.

    Or consider taking dense regulatory documents and creating simple, city-specific guides for small business owners. The source material is public, but it’s written in legal language that most people can’t easily parse. Making it accessible is the value.

    When Programmatic SEO Is Worth It

    Not every site or business benefits from this approach. Here’s a honest framework for deciding.

    It’s probably worth exploring if:

    • You can identify a clear pattern of search queries (like “[thing] in [place]” or “
      vs
      “)
    • Structured data exists that could populate those pages (public databases, APIs, scraped information)
    • Each generated page would genuinely answer someone’s question
    • You’re willing to invest upfront in building the pipeline, knowing the payoff is gradual
    • You have some technical comfort, even if it’s just spreadsheets and a basic website builder

    It’s probably not worth it if:

    • Your topic requires deep original thought or personal experience on every page
    • The search queries you’d target are already dominated by massive sites with real authority
    • You can’t identify a repeatable template that works across many variations
    • You’re only interested in tricking Google rather than helping readers
    • You need results next week (programmatic SEO is a long game)

    The honest truth: Most people who attempt programmatic SEO either give up before publishing enough pages to see results, or they cut corners on quality and get penalized. The sweet spot is finding a niche where you can provide genuine value at scale — and that niche is more specific than you think.

    The Quality Test

    Before I invest time building programmatic pages for any topic, I apply a simple test:

    If a real person landed on this page from a Google search, would they feel like their time was respected?

    Not “would they click around the site.” Not “would Google’s algorithm reward it.” Would an actual human being read this page and think, “Good, that’s what I needed to know”?

    If the answer is yes, the approach is sound regardless of how the content was created — by hand, by template, by AI, or by some combination. If the answer is no, no amount of technical sophistication will save it. Google is remarkably good at figuring out when people are disappointed by what they find.

    This is the real shift in programmatic SEO. It’s no longer about creating content that fools algorithms into thinking you’re providing value when you’re not. It’s about actually providing value — and using automation to do it at a scale that would be impossible manually.

    Where to Start

    If you’re curious about programmatic SEO but don’t want to build a complex pipeline on day one, start here:

    1. Find your pattern. What questions do people search for in your space that follow a repeatable format? Use Google’s autocomplete, “People also ask” boxes, or a tool like AlsoAsked to spot templates.
    2. Check the competition. Search for a few variations of your pattern. If the top results are from massive sites with huge authority, pick a more specific niche. If the results are thin or unhelpful, you’ve found an opportunity.
    3. Build one page by hand. Before automating anything, manually create the best possible version of one page in your template. This becomes your quality benchmark.
    4. Then scale gradually. Start with 10-20 pages, not 1,000. See how they perform. Adjust your template based on what works. Only then consider building automation.

    The tools available today — from simple no-code builders to full AI agent pipelines — make the scaling part easier than ever. But the strategic thinking that goes into choosing what to build? That’s still on you.

    I’ve written more about the technical side of building these pipelines in my post on programmatic SEO, and if you’re interested in how AI fits into a broader content workflow, take a look at how I use AI to write and publish blog posts. For the growth-minded builders, growth engineering with Claude Code gets into the deeper technical possibilities.

    But honestly? Start with the pattern. Everything else follows from that.

  • How to Use AI Agent Teams to Optimize Your Product Pages

    How to Use AI Agent Teams to Optimize Your Product Pages

    Most product pages are built once and forgotten. Someone writes a description, uploads photos, sets a price, and moves on. Months later, the page is still converting at 1% and nobody’s touched it because “it’s fine.”

    The problem is that a good product page isn’t one skill. It’s copywriting, conversion rate optimization, visual design, and brand consistency all at once. No single AI prompt holds all of those disciplines in focus simultaneously.

    I’ve written about the adversarial agent approach before — assembling specialized AI agents into a team, giving each one a scoring rubric, and iterating until they all agree the work is good. I recently applied this to a real Shopify product page with a four-agent team: a copywriter, a CRO specialist, a branding expert, and a visual designer. The conversion rate doubled in seven days.

    Here’s how to adapt this for your own pages.

    Score First, Then Build a Task List

    The key adaptation for product pages is turning agent feedback into a concrete task list you can work through.

    Point your agent team at the current page and have each specialist score it out of ten against their rubric. You’ll get feedback like: “6/10 — Add to Cart button blends into the background, social proof is buried below three scrolls” from the CRO agent, and “5/10 — product descriptions are feature lists, not benefit statements” from the copywriter.

    Combine all of their recommendations into a single prioritized list. This is your improvement backlog. The types of changes that consistently surface across e-commerce pages:

    • Primary action prominence — more contrast, higher placement on mobile, larger touch target for the CTA. Almost always the highest-impact change.
    • Mobile layout — product images eating too much vertical space, pushing price and CTA below the fold.
    • Benefit-oriented copy — shifting descriptions from “what this is” to “what this does for you.”
    • Social proof repositioning — moving reviews and trust signals closer to the point of purchase decision.
    • FAQ expansion — every unanswered objection is a reason to leave the page.

    Work through the list with yourself in the loop. Don’t hand everything to the AI and walk away. Agents occasionally recommend changes that score well on their rubric but don’t fit your broader context — aggressive urgency tactics that feel off-brand, or rewrites of sections you’ve crafted for a specific reason.

    After each batch of changes, re-score. You’ll see numbers climb, and you’ll see new issues surface that weren’t visible before. If you’re not familiar with the challenges of split testing, this iterative approach with agent scoring is a practical alternative — you get structured feedback without needing statistical significance on every change.

    Build Features Instead of Buying Apps

    One thing that came out of this process: AI agents can build small features that would normally cost $10 to $20 a month as a Shopify app.

    CRO agent suggested a social proof notifications — the little popups showing recent purchases. Instead of installing an app, an AI agent wrote a script that pulls real order data from the Shopify API, stores it in metafields, and displays it with a liquid snippet. Twenty minutes of agent time, no monthly fee, no bloated JavaScript, no third-party tracking.

    This works for a surprising number of app store features. Countdown timers, stock warnings, cross-sell blocks, announcement bars. If the feature is simple enough to describe, an agent can build a lightweight version that does exactly what you need. This is the same growth engineering approach I’ve been using across my marketing stack — treating your code editor as the platform instead of buying SaaS for everything.

    Then Work on the Economics

    A better-converting page is only half the equation. If margins are thin and average order value is low, you can’t scale paid advertising profitably.

    Once conversion improvements stabilize, shift the agent team to pricing structure. Have them model bundle configurations, free shipping thresholds, COGS at different quantities, pick and pack costs, and shipping rates across weight breaks. The goal is maximizing contribution margin per order while maintaining conversion rates.

    What came out of this for me was more aggressive than I would have tested on my own. The AI ran the numbers without the emotional anchoring that comes from having set the original prices yourself. No bias. Just math.

    The structural changes worth considering:

    • Bundle incentives inside the cart — present options the moment someone adds a product, not on a separate page.
    • Tiered thresholds — make each additional item feel like an obvious deal. Free shipping at one level, a percentage off at the next.
    • Higher price points — if your page is now doing its job with strong copy and visible social proof, customers may tolerate more than you assume.

    Measure Patiently

    Page layout changes show results fast. My conversion improvements were clear within the first week.

    Avoid changing too much at the same time. It’s hard to isolate what changes were improvement and which were duds.

    Give it a shot on your site – let me know how it goes.

  • Give People What They Want: Entertainment

    Give People What They Want: Entertainment

    I work in the sports industry. We sell tickets, sponsorships, media rights. But what we’re actually creating is entertainment. That’s the core product. Everything else is a derivative.

    Most content creators forget this.

    They produce tips and tricks. How-tos. Educational content. And there’s a place for that (you’re reading one right now). But scroll through your feed. How much of what you’re actually consuming is educational? How much of it is making you feel something in the moment?

    People don’t open TikTok to learn. They open it to feel.

    The Hey Al Experiment

    Yesterday, I rebooted an old concept I’d been sitting on for years. A short-form video series called “Hey Al.”

    The premise: I have conversations with an AI assistant named Al (voiced by a cheerful feminine AI), and things go sideways. Al takes instructions literally. Al lacks the context that makes human requests make sense. Al is helpful to a fault, which is exactly what makes it funny.

    It’s fictional comedy. Not a tutorial. Not tips. Not “5 ways to use AI better.”

    The first episode (about having a productive day) went out yesterday and performed better than anything educational I’ve posted in months. Not because the production was better. Because people wanted to watch it. They wanted to see what Al would do next.

    That’s entertainment.

    The Content Creator Trap

    Most of us creating content online default to education mode. It feels safer. It feels valuable. You’re giving people information they can use.

    Businesses creating content tend to create announcements and ads – boring!

    Information is abundant. Entertainment is scarce.

    Scroll your own feed. Most of what stops you isn’t a tutorial. It’s something that made you feel curious or surprised. The educational content you actually consume is usually wrapped in entertainment. The YouTuber who makes you laugh while teaching. The thread that opens with a story before the lesson.

    Give people what they want. They’re holding a device that used to be called a television. They want to be entertained.

    AI-Assisted Production

    The irony isn’t lost on me: I’m using AI to produce entertainment about AI.

    For Hey Al, Claude Code helped me manage the production pipeline. Script development, extracting audio from video files, converting my voice recording to Al’s voice character, organizing the batch filming schedule.

    These aren’t creative decisions. They’re boilerplate labor. The automation frees me to focus on what actually matters: making the joke land.

    The ideal state is producing multiple episodes per day, batched and scheduled. We’re not there yet. But the direction is clear.

    Quality vs. Quantity Is a False Dichotomy

    The world is flooded with content. You’ve heard the advice: focus on quality, not quantity. Or: volume wins, ship more.

    But it’s not actually a seesaw where you trade one for the other. Better tools give you better trade-offs on both.

    Everything we produce today is higher quality than what was possible in the 1980s. Obviously. But it’s also faster to produce. Both lines went up, because the tools improved.

    The bar is always rising. The low bar of yesterday is buried. But if you’re using modern tools, you’re not giving up quality for speed. You’re getting both.

    The game isn’t quality or quantity. It’s using the right tools to stay ahead of the rising floor.

    The Job

    If you’re creating content, you’re in the entertainment business. Whether you like it or not. Whether you’re selling sports tickets or SaaS products or your own personal brand.

    Education is a delivery mechanism. The wrapper matters.

    Give people what they want. They want to feel something. They want to be entertained.

    That’s the job.

  • Growth Engineering with Claude Code: Why Your Next Marketing Platform is a Code Editor

    Growth Engineering with Claude Code: Why Your Next Marketing Platform is a Code Editor

    Claude Code was built for software engineers. It’s a CLI tool that helps developers write, debug, and ship code faster with AI assistance.

    I’m using it to run the entire marketing operation for Psychedelic Water.

    Not the coding parts—though there’s some of that. I’m using it to create content, coordinate campaigns, maintain brand voice across six channels, and build a self-improving system where analytics feed back into strategy. The file system is the CMS. Markdown files are the content. CLAUDE.md files are the strategy documents. And AI is the executor.

    Here’s why I think this is where growth engineering is headed.

    The Problem with Marketing Tools

    Modern marketing requires presence everywhere: Instagram, Twitter, TikTok, YouTube, email, blog, third-party publications. Each platform has its own dashboard, its own analytics, its own content format.

    The result is fragmentation. Your Instagram strategy lives in one place. Your email campaigns live in another. Your content calendar is a spreadsheet that’s always out of date. And maintaining consistent brand voice across all of it? Good luck.

    Most teams solve this by hiring more people. A social media manager, a content writer, an email specialist, someone to pull analytics together. Each person becomes the keeper of their channel, and coordination happens through meetings, Slack, and hope.

    What if the coordination layer was built into the system itself?

    The File System as Marketing Infrastructure

    At Psychedelic Water, I’ve built a folder structure that serves as the entire marketing operation:

    psychedelic-marketing/
├── CLAUDE.md                    # High-level strategy and goals
├── products/                    # Product info, photography, specs
├── brand/                       # Voice guidelines, visual assets
├── channels/
│   ├── instagram/
│   │   ├── CLAUDE.md            # Instagram-specific strategy
│   │   ├── scripts/             # Posting, analytics, scheduling
│   │   └── drafts/              # Content in progress
│   ├── twitter/
│   │   ├── CLAUDE.md
│   │   ├── scripts/
│   │   └── drafts/
│   ├── email/
│   │   ├── CLAUDE.md
│   │   ├── scripts/             # Klaviyo integration
│   │   └── campaigns/
│   ├── blog/
│   │   ├── CLAUDE.md
│   │   ├── scripts/             # Shopify publishing, analytics
│   │   └── posts/
│   └── ...
├── analytics/                   # Performance data, reports
└── campaigns/                   # Cross-channel coordinated efforts
    └── 2026-01-functional-focus/
        ├── strategy.md
        ├── instagram/
        ├── twitter/
        └── email/

    Every channel has its own CLAUDE.md file that defines the strategy for that platform. When I work in the Instagram folder, Claude understands the Instagram strategy. When I work in email, it understands the email strategy. The context is built into the structure.

    Strategy as Code

    Here’s what a channel-level CLAUDE.md might contain:

    • Audience: Who we’re talking to on this platform
    • Voice adjustments: How brand voice adapts for this channel
    • Content types: What performs well here
    • Posting cadence: Frequency and timing
    • Scripts available: What automation exists
    • Success metrics: What we’re optimizing for

    When I ask Claude to draft an Instagram caption, it doesn’t start from zero. It reads the strategy document, understands the voice, knows what’s worked before. The strategic context is embedded in the file system.

    The top-level CLAUDE.md contains the overarching marketing goals—what we’re focusing on this month, what story we’re telling, what campaigns are active. This creates consistency. If the focus is on functional ingredients this month, every channel knows it. Instagram, Twitter, email, blog—they’re all telling the same story in their own way.

    Scripts as Integrations

    Each channel folder contains scripts that handle the platform-specific work:

    Blog scripts connect to Shopify to publish content and pull performance data. I can ask Claude to check how last week’s post performed relative to historical averages, and it runs the analytics script, interprets the results, and incorporates that into future recommendations.

    Email scripts integrate with Klaviyo to schedule campaigns and pull engagement metrics.

    Image generation scripts use AI to create visuals that match the brand aesthetic, then resize them appropriately for each platform.

    These aren’t complex applications. They’re small, focused tools—often just a few dozen lines of Python—that bridge Claude Code to the platforms where content lives. The AI orchestrates them; the scripts do the platform-specific work.

    Content as Dated Folders

    Every piece of content lives in a dated folder:

    channels/instagram/drafts/
├── 2026-01-20-functional-energy/
│   ├── caption.md
│   ├── image.jpg
│   ├── notes.md
│   └── analytics.json
├── 2026-01-21-behind-the-scenes/
│   ├── caption.md
│   ├── images/
│   └── notes.md

    This creates a natural archive. I can look back at what we posted, see what performed, understand what we were thinking at the time. When analytics data comes in, it gets saved alongside the content. The system learns from itself.

    Cross-Channel Coordination

    The hardest part of multi-channel marketing is consistency. You want the same story told everywhere, adapted for each platform’s format and audience.

    The campaigns/ folder solves this. A campaign is a coordinated effort across channels:

    campaigns/2026-01-functional-focus/
├── strategy.md          # The core message and goals
├── instagram/           # Instagram-specific executions
├── twitter/             # Twitter-specific executions
├── email/               # Email-specific executions
└── results.md           # What happened

    The strategy.md defines what we’re saying and why. Each channel folder contains the platform-specific adaptations. Claude understands that these are connected—if I’m working on the Instagram content, it knows the overarching strategy and can ensure the messaging aligns.

    If someone misses the Instagram post, they might catch it on Twitter. If they’re not on social media, they’ll get the email. The story reaches them somewhere.

    Why This Works

    Claude Code wasn’t designed for this. It was built to help developers write software. But the core patterns translate perfectly:

    File system as memory: Just like code lives in files, content lives in files. The structure is the organization.

    Markdown as content: Developers write documentation in markdown. Marketers can write content in markdown. It’s portable, version-controlled, and AI-friendly.

    Scripts as integrations: Instead of API calls to deploy code, scripts make API calls to publish content or pull analytics.

    AI as executor: Instead of writing code, the AI writes content, following the strategic guidelines embedded in the folder structure.

    The gap between “AI coding assistant” and “AI marketing operations platform” is smaller than it looks.

    What’s Missing

    This system isn’t fully automated. Some platforms don’t have good APIs for posting. Some content needs human review before it goes out. The analytics integrations are still being built.

    But the bones are there. The organizational structure exists. The strategy is embedded. The feedback loops are forming.

    Right now, I work alongside Claude in this system—reviewing drafts, approving posts, adjusting strategy based on what the data says. But the system is designed to become more autonomous over time. As the AI gets better, as the integrations get more complete, the human involvement shifts from execution to oversight.

    The Future of Growth Engineering

    I think this is where marketing operations is headed. Not more dashboards. Not more point solutions. Not more people managing more tools.

    Instead: AI-native systems where the file system is the source of truth, strategy is embedded in the structure, and AI handles the execution across every channel.

    Claude Code is a code editor. But it turns out that growth engineering looks a lot like software engineering—just with different outputs. Instead of shipping code, you’re shipping content. Instead of deploying to production, you’re publishing to platforms. Instead of monitoring systems, you’re tracking engagement.

    The tools built for one translate surprisingly well to the other.

    I’m building this system for Psychedelic Water, where I’m President and Co-Founder. If you’re thinking about AI-native marketing operations, I’d be interested to hear what you’re building.

  • Programmatic SEO

    Programmatic SEO

    A couple weeks ago I was inspired to revisit an idea I had launched a business into a decade ago, but failed and shut down. 10 years ago, I built a service called AutomaticBlogMachine, it would deploy wordpress to a new server, set up the theme, install some plugins, and publish content.

    However, 2 major things caused it to fail.

    1. Shortly after launch, Google rolled out a series of updates – Penguin, Panda etc that was able to identify the content as machine generated and ignore these website from any SEO impact with ease.
    2. The natual language approaches available at the time were crud and resulted in low value spam. Basic word swapping, translations and gramatical rewording of existing content. Visitors didn’t like it.

    Today with LLMs it’s possible to create genuinely value-add content automatically that answers people’s questions, links to the appropriate resources and has unique imagery to accompany it. It can be engaging content.

    Researching, generating and publishing content automatically using these new approaches is now possible, and so I’m starting an experiement to see how it works.

    I’d like to see what happens with a website that has 10,000+pages. How fast should this site grow content? and if there is a growth in traffic, can it be sustainable and monetized?

    The bigger idea is to build a system (again) that can be used as a domain parking service. It grows the domain rank over time passively, and ideally generates revenue along the way.

    Register your interest with the form: https://forms.gle/QsgyGMbHDJ87Zmv39

    Join Interest Group
  • Founder Fuel: How do you measure the success of your marketing efforts?

    Founder Fuel: How do you measure the success of your marketing efforts?

    At different times in a business success means different things. Sometimes it’s measured in Likes and views, other times in ROAS or ACOS, other times it’s in brand recall. But as with most things in business, if you don’t measure you aren’t in control of it. So KPIs for your marketing efforts is an important aspect of understanding if things are working as intended.

    I have been drawn to sales and direct marketing approaches in the past because the metrics are easy to collect. I think most small companies should start here. The direct testing enables quicker learning which can then form the backbone of a brand. Where as jumping straight into brand marketing usually requires a lot of assumptions.

    With one-on-one sales you get immediate and clear annecdotal feedback about what customers want. Then expand with direct marketing ads to scale those up to 10,000+ people to further refine the messaging and targeting, and test the market further. Finally layering in the branding in a way that applies everything you’ve learned so far.

    Easier said than done. People feel compelled to start with a logo.

    As you get more sophisticated, something I have not yet explored is Marketing Mix Modeling approaches to measure the effectiveness of marketing. With a complex set of marketing channels in which to budget marketing spend this is probably the best way to assess how it all is affecting sales.

    That’s how I think about measuring the success of marketing. This journaling prompt came from Daily Founder Fuel a very short daily newsletter that contains a journaling prompt for founders, entreprenurs and business owners.

  • Copy My AI Assisted Content Strategy

    Copy My AI Assisted Content Strategy

    Getting and growing attention is the core of any marketing strategy. But standing out is harder than ever when everyone is equipped with high quality cameras, microphones and great software.

    Today I’m going to tell you about how I’m leveraging AI to accelerate the production of short form video content that I’m cross posting to TikTok, Instagram, Facebook and YouTube.

    When thinking about producing short-form video here’s what I consider important:

    Develop a format and style that can repeat. This reduces the amount of decisions that need to be made and streamlines the production. It also makes the videos more binge-worthy, since people who like one will be highly likely to enjoy the other videos.

    The structure should have a strong hook – most videos on these platforms have 1 second to grab your attention. If you manage to keep 50% of people past the 3rd second you’re doing well.

    Make the content valuable – entertainment value or educational value. High value content is more likely to get shared

    Don’t spend too much time/money on producing a short video. These things have a short life-span. Going super viral is a lot of luck so embrace “internet ugly”.

    Always be testing – use the short lifespan to your advantage – re-edit and re-post often. Remember, 90% of people who saw the video, didn’t watch the whole thing, the remaining 10% will have forgotten about it by next week.

    So, here’s a strategy I’ve started to use to develop a personal brand presence. I developed a structure for the videos that include (in my case) 5 stages:

    Opening/hook

    Conflict

    Escalation

    Resolution or cliffhanger

    Closing

    Then I add some additional constraints:

    Must be easily recorded with just myself and a phone camera

    very few if any props or setting changes

    no special effects required

    With the help of ChatGPT, I asked for help developing the first 10 video ideas that can fit this criteria and BOOM! There’s a list of concepts.

    Just a little bit of workshopping these ideas to turn them into short 8-10 line scripts.

    In my case I decided to have an AI character in my scripts. This adds to the complexity of editing. But it’s kind of fun, so here’s what I did for that:

    Use the voices from elevenlabs.io to generate the audio files. Interesting note here – The speech-to-speech AI option can match the tone and cadence but with another character’s voice – which helps with telling jokes.

    Used CapCut video editor – this is significantly easier to use than Adobe’s professional tools. It layers in the video with the extra audio track. A short video can be edited in less than 10 minutes.

    Take advantage of automated AI caption generation – they’re usually 95% correct and the timing is aligned for you. People often watch with the sound off – so captions are important.

    SEO is a part of the process with publishing videos. I use ChatGPT to help write a video title and description that matches the video and provides enough textual content for indexing the video.

    Putting this together and a bit of practice it’s possible to script, record, edit and publish a decent short video in as little as 15 minutes.

    If you want to see some of the results – subscribe to my YouTube channel

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  • The Importance Of Email Segmentation For Your Campaigns

    The Importance Of Email Segmentation For Your Campaigns

    Email segmentation is going to be a sexy topic in 2024. But perhaps you don’t believe me yet.

    Email segmentation helps you sort your subscribers into different groups. For e-commerce this is critical. Not all contacts are at the same part of the journey with your brand, and not all benefit from the same messages.

    Sending poorly targeted emails does considerable harm to your ability to contact your customers in the future. Doubly so as inbox providers like Google and Yahoo clamp down on spam to reduce the amount of noise in people’s inboxes. An unopened and unclicked email is a signal to Google that you send bad emails. Don’t train Google’s spam filters this way.

    Send the right email to the right person.

    Email is not dead. Email has been the biggest driver of growth and revenue for most businesses. It is the audience that you have the most direct control over. Unlike social media followers, email is less likely to be blocked due to closed accounts or algorithm changes.

    Email continues to grow. Here’s some stats:

    Segmented emails drive 30% more opens and 50% higher CTRs than unsegmented ones

    Email drives an impressive ROI of $36 for every $1 invested

    81% of B2B marketers say their most used form of content marketing is email newsletters

    there are 4 billion daily email users globally, expected to climb to 4.6 billion by 2025

    38% of brands are increasing their email budget, just 10% are cutting.

    Today, marketers use AI for emails to help in the writing of copy. Crafting a better subject line is something AI is great at. Great copy can help engage more readers to open and click. But it’s only part of the equation.

    Using AI to segment your contacts opens many new possibilities. Advanced segmentation is hard data analysis and as a result mostly a tactic used only by sophisticated marketing teams. AI democratizes this kind of analysis. It helps even smaller brands focus their marketing at the people most likely to appreciate it.

    AI has the ability to take what would be complex software logic and turns it into the business question.

    Given a customer profile you can ask questions:

    has this customer churned?

    does this customer like to use coupons / are they price insensitive?

    do they live in the North East?

    is this customer a VIP?

    Each of these questions could be answered with various logical checks – written in code, or implemented in spreadsheets. AI models like GPT-4 can answer if provided with english.

    It won’t be long before solutions like this are scaled up and available to email marketers for defining segments.

    If this is something you find intriguing lets connect – I’m looking for beta testers for case studies. Let me segment your customers for you! I’m accepting 5 test clients to run this system with and prove it out.

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