An AI workflow company needs public explanations that make its method understandable before a sales call: what a skill is, when work is ready, how it is tested, where it stops, and how a team buys or operates it. A connected library compounds those answers.
A 90-day system should balance problem, method, comparison, use-case, buyer, and proof content. Each page needs one clear question, a direct answer, internal routes to a relevant product path, and a useful next conversation.
Part 01 · Build the public library
Build the public library
Why a workflow company needs a public library of explanations
Buyers need to understand the difference between prompts, skills, workflows, testing, guardrails, and custom delivery before they can evaluate Grokkit. A public library makes that expertise searchable and sendable.
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Pillar pages: skills, custom workflows, examples
Use skills for ready-made jobs, custom workflows for bespoke process work, and examples for concrete transformations. Link each article to the product page that matches its reader’s next decision.
Cluster map of topics 1–39
Group the library around foundations, method, trust, tool fit, use cases, operating systems, and buying. Link hub concepts to specific jobs and return use-case readers to the underlying method.
Search intent mix: problem, method, comparison, use case, buyer
Publish across the buying journey: define the problem, explain the method, compare options, show a role-specific use case, and answer the purchase question. A single intent cannot carry the whole funnel.
Internal linking to custom workflows, examples, skills, and contact
Add one relevant product or conversation path where it helps the reader act: custom work for company-specific processes, skills for common jobs, examples for proof, and contact for scoped discovery.
Part 02 · Design pages to be found and used
Design pages to be found and used
Title patterns that match how operators search
Use specific titles that name the job, audience, or decision: “AI workflow for support escalation” beats vague category language. Match the primary query in the title, first answer, headings, description, and URL.
Proof blocks: test suite, versioning, five tools
Reuse honest proof mechanisms across articles: real-work tests, visible unknowns, versioning, source control, review gates, and multi-tool installation. Do not invent customer metrics to make a page feel credible.
Example transformations as reusable diagrams
Show a simple transformation from messy input to reviewed output with source and reviewer labels. Examples make the method concrete while avoiding claims about results you cannot substantiate.
Case-shaped posts without fake metrics
Use the challenge, workflow design, evidence, guardrails, and review structure of a case study when no verified outcome metric exists. Be clear that the post is a method example, not customer proof. The content planning engagement is written to that shape: real process, named constraints, no invented numbers.
FAQ schema from real discovery objections
Turn repeated sales and implementation questions into natural-language FAQs with concise answers. Keep FAQ content visible on the page and grounded in the actual workflow, not merely added for markup.
Part 03 · Capture proof and distribution
Capture proof and distribution
Update cadence when the product adds a skill category
Review affected hubs, comparisons, product links, examples, FAQs, and llms.txt when a new category launches. Freshness means meaningful connection to the current product, not changing dates without content.
Avoiding generic “top ChatGPT prompts” content
Avoid broad prompt-list content that attracts readers with no need for a tested workflow. Focus on jobs, decisions, risks, and operating constraints where Grokkit has a distinct perspective.
Capturing comparison queries vs incumbents
Publish fair comparisons when buyers need to distinguish skills from prompts, agents, automation tools, libraries, and custom builds. Use clear criteria and acknowledge where another option is appropriate.
Sales enablement: which post to send after which call
Map articles to common conversations: readiness after an early discovery call, testing after trust concerns, a role-specific workflow after a use-case call, and the buyer guide during evaluation.
Measuring assisted conversions, not only traffic
Track which articles appear in journeys that reach examples, skills, custom workflows, or contact; which ones sales sends; and which questions they answer. Traffic alone does not show business relevance.
Part 04 · Run the 90-day system
Run the 90-day system
Repurposing one topic into a sample run script
Turn a strong article into a short sample input, output outline, review checklist, or discovery worksheet. This gives sales and operators a tangible way to understand the method without gating the explanation.
llms.txt / machine-readable summaries of the method
Maintain a concise llms.txt that identifies Grokkit, core pages, major topics, and canonical links. It complements crawlable HTML, structured data, sitemaps, and clear on-page answer blocks; it does not replace them.
Editorial calendar for 90 days
Assign a weekly rhythm for one commercial or buyer article, one role-specific use case, and one method or proof refresh. Use sales questions and search performance to decide the next cluster, not a random idea backlog.
What to write next after 40 posts
Prioritize gaps revealed by search queries, sales objections, customer roles, product additions, and internal-link dead ends. Expand clusters with evidence rather than repeating broad AI commentary.
How published examples feed new custom-workflow conversations
A useful example helps a buyer recognize their repeated job, articulate inputs and review pain, and arrive at a custom-workflow conversation with concrete scope evidence.