Every team has work that eats the week. The repetitive, manual assembly you do the same way every time before the actual thinking can start. That’s the work worth automating, and you don’t need to invent something new to do it. You need to take what you already do and wire it up.
That’s exactly how these builds happened. We didn’t buy a shiny “AI tool.” We connected Claude to the things we already run every day, like Ahrefs, our AI-visibility tracker, and the live pages themselves, then taught it our own rules and let it do the assembly. A person still makes the calls and signs off. What changes is that they start from a finished draft instead of a blank spreadsheet.
Here are five we built.
1. Schema audit and recommendations
What it is: a build that reads a page and identifies the structured data it’s missing. Structured data is the markup that tells search engines what a page actually is (a product, an author, an article), which is what makes it eligible for rich results in the SERP.
What it’s plugged into: a custom scraper that opens each page in a real browser, waits for everything to load, and reads what’s actually on screen, so we work from the page as Google and a visitor really see it, not a stripped-down version. It then generates validated schema.org markup for that exact page type and outputs a branded audit doc you can hand straight to a developer.
How we use it: to fix schema issues fast when a client’s rich results aren’t showing, and to make new pages eligible for them from launch. For organizations and software we go further, applying expanded schema templates that give Google extra context most sites leave out. It’s the difference between hoping Google gets it and telling Google plainly.
2. Search quality grader
What it is: a second opinion, on demand, against the standard Google actually grades content on.
What it’s plugged into: Google’s own Search Quality Rater Guidelines. That’s the E-E-A-T, YMYL and “Needs Met” framework, baked into the workflow. Feed it a draft or a live URL and it returns a straight Pass/Fail plus the top things to fix.
How we use it: as a gate before anything ships. A draft gets graded, the weak spots get fixed, and only then does it go live. It’s also how we QA a batch of existing pages fast, instead of reviewing them one by one from memory.
3. Competitor Analysis
What it is: the build that maps a whole competitive landscape in one pass. SEO, PPC, and brand signals for 20+ competitors, pulled, classified, and written up as a client-ready sheet. What took a consultant half a day now takes about five minutes.
What it’s plugged into: Ahrefs for organic and backlinks, SpyFu for paid spend and ad history, a live scrape of each competitor’s homepage for schema, GTM and positioning, and SerpAPI for their G2 ratings. Claude does the synthesis: it classifies each competitor, writes the thesis, and pulls six key learnings with recommendations, all formatted in Flow colors across six tabs.
How we use it: at the start of a new engagement, to walk into the first call already knowing the landscape instead of promising to “look into it.” One caution we built in: Ahrefs data is real, SpyFu’s paid numbers are modeled estimates, so we use them to rank and spot trends, never to quote a competitor’s actual budget.
4. Keyword Research and Clustering
What it is: the build that turns a market into a plan. It finds the keywords worth chasing, then groups them into pages (one cluster, one page) with priority, intent and content angle attached.
What it’s plugged into: live Ahrefs data for volume, difficulty and, crucially, your current rankings. The clustering itself runs on rules we wrote in-house, not handed off to a generic model and hoped for.
How we use it: whenever there’s a market and no plan. And here’s the part that earns its keep: it pulls out the pages you already almost rank for, positions 4 to 20, where the fastest wins hide. Those come out at the top of the roadmap, ready to brief.
5. Digital Footprint Audit
What it is: the build that shows what the internet says about a brand before a buyer ever reaches the site. Not just where you rank, but whether you show up, and show up accurately, across review sites, listicles, social profiles, and AI answers.
What it’s plugged into: the full Google results page (organic, the AI Overview and who it cites, video, and ads), the AI platforms themselves (ChatGPT, Perplexity, Claude), our AI-visibility tracker for citation share, and crawl data for what’s actually indexed under the brand, including the old pages and dropped taglines you forgot were still live.
How we use it: on a brand’s top high-intent queries against its top three competitors, to find the gaps: outdated profiles, missing listicle placements, inconsistent messaging, weak AI visibility. It’s usually where an engagement starts, because it shows exactly where a brand is losing ground before any strategy work begins.
The point isn’t five tools, it’s that they share a spine
Same live data. Same standards. Same human sign-off. A footprint audit finds where you’re invisible, competitor analysis shows who’s taking that ground, keyword research turns it into a plan, the quality grader keeps what we publish to standard, and schema makes sure crawlers can read it. One connected system, not five gadgets, with a person steering at every step.
We can build the same kind of workflow around your data, your tools, and your standards. Get in touch to talk it through.