llms.txt plus MCP content corpus for AI discovery
Package your content so both crawlers and AI assistants can query it directly, using llms.txt, sitemap-grade hygiene, and a machine-readable endpoint where it makes sense.
Source-backed tactics for AI search visibility, citations, answer engines, and machine-readable discovery.
Package your content so both crawlers and AI assistants can query it directly, using llms.txt, sitemap-grade hygiene, and a machine-readable endpoint where it makes sense.
Wait until user-generated content is dense enough to create genuinely useful pages, then publish crawlable aggregates on the main domain with guardrails and sitemap support.
Turn each tactic into a structured record with source URL, source name, last-found date, channel, stage, and budget metadata.
Write pages that answer the likely AI-search question first, then back the answer with source URLs, dates, and specific examples.
Once a user-generated content surface proves useful, move it onto the main domain so search equity and product discovery compound in one place.
Detect likely agent requests and return markdown automatically, even when the client does not explicitly ask for it.
Provision every new content surface with its crawl files from day one: sitemap, llms.txt, robots.txt, and a machine-readable install or query endpoint.
Audit your owned profiles, stale taglines, citations, and About page together so branded search and AI answers repeat the right facts about the company.
Publish an official FAQ that states what is false in plain language so answer engines have a first-party page to cite when rumors start circulating.
Protect pages with real Google clicks from bulk AI-search rewrites, and remove pages with no organic or AI demand from the queue.
When a user says AI sent them, ask for the exact prompt in onboarding so the growth team learns from real buyer language instead of synthetic guesses.
Win relevant third-party citations before publishing another stack of self-referential thought-leadership pages.
Turn repeated AI-bot requests to missing URLs into a prioritized list of pages the corpus should actually create.
Put a two-to-three-sentence standalone answer at the top of the page before the reader or model has to parse the full detail.
Build the SEO and community proof layer before chasing standalone GEO wins, because discovery-style AI prompts still reward authority and community presence first.
Write pages in quotable chunks with direct answers, question-led headings, short paragraphs, and specific numbers before you chase giant SEO essays.
Run per-language prompts and localized proof checks before assuming one English content system will travel cleanly into every market.
Put the answer, evidence, and source cue in one tight block before expanding into a long brand essay.
Attack the narrow buyer problem and comparison query before trying to outrank big brands on the category homepage war.
Collapse duplicate video page URLs into one canonical page before asking AI search tools to learn from the corpus.
Rewrite high-interest page titles into clear question form so the page matches how AI search tools and users ask.
Get your own domain into the top cited domains and pages before rewriting brand copy for AI, or the models will keep learning you from everyone else first.
Benchmark competitor mentions and co-citation overlap before rewriting your positioning, so you know which topics the models already reserve for someone else.
Report AEO from a stitched set of sources such as referrer traffic, self-report, bot analytics, and prompt visibility instead of pretending one clean dashboard can settle it.
Write Reddit answers as durable citation assets, because strong threads can rank in Google and feed AI answers even when your domain is weaker.
Measure answer-engine work against an untreated on-domain control group before claiming an AEO lift from the platform tailwind.
Track ChatGPT, Perplexity, Gemini, and other engines separately before you flatten them into one comforting visibility number.
Track the parent brand, product names, and common variants as one entity before celebrating or panicking over scattered AI mentions.
Split AI visibility by platform before averaging the report into one score, because the missing model is often the real repair queue.
Standardize name, address, phone, categories, and hours across map, directory, and assistant surfaces before treating AI local search as a prompting problem.
Fix the pages you own before chasing Reddit threads, Wikipedia dreams, Medium cleanup, and every other AEO side quest.
Throw away the prompt set and start over when synthetic tracking stops matching how real customers actually describe the product.
Check whether AI citations are missing because the source layer is weak before writing more on-site copy.
Treat the homepage, product pages, and utility tools as the first AI-discovery surfaces before assuming informational blog posts will carry the whole answer-engine job.
Cluster keywords by persona and use case before generating AI SaaS programmatic pages.
Check ChatGPT, Claude, and Gemini in private mode with 3-5 plain buyer prompts before you disappear into dashboards, exports, and vendor demos.
Add a few specific FAQ answers and JSON-LD to every comparison page so search and AI engines can parse the decision.
Expose a standards-based API catalog before expecting agents or API crawlers to reverse-engineer your docs from HTML and navigation chrome.
Build the AI visibility audit from real search-backed prompts before trusting a synthetic prompt list that flatters the team.
Audit the pages AI already cites before rewriting the homepage and hoping the new message changes what answer engines repeat.
Ship a Markdown mirror for every docs page before customers and AI tools start copying brittle HTML into prompts, notes, and support replies.
Track which landing pages already receive AI chatbot traffic before rewriting the whole site for AEO.
Publish a curated `llms.txt` file for the docs corpus before agents start scraping random HTML paths, so AI retrieval begins from the pages you actually want reused.
Publish a root `/skill.md` that tells agents what your product can do, which inputs it needs, and which constraints matter instead of forcing them to infer capabilities from scattered docs.
If the product has several workflows, publish separate skill files plus a `/.well-known/agent-skills/index.json` manifest instead of forcing one giant capability blob.
Publish `/.well-known/agent-skills/index.json` with a digest for each skill so agents can verify they fetched the right instructions before they act on them.
Write an explicit `robots.txt` policy before assuming search engines and AI crawlers will interpret a default wildcard the way you meant.
Run a cross-model AI visibility check before turning one cherry-picked chatbot answer into a strategy deck.
Keep a Help Center private to the team while still letting customer-facing AI use those articles as a support knowledge layer.
Turn off help-center indexing when the same answers need to live elsewhere, so support content can still work in Messenger without splitting search authority.
Show when an AI agent is actively handling the conversation so the customer knows which system is replying before the handoff gets blurry.
Mark up core pages with schema so search engines and answer systems can tell whether they are looking at a site, article, dataset, or author page before they guess from layout.
Attach first-hand operator evidence, examples, and constraints to every guide so the page reads like lived work rather than a polished summary of what everybody already knows.
Use `skill.md` frontmatter to tell agents which environment assumptions matter and which tools are allowed before they start making things up.
Teach users to install the product's skill straight from the docs URL with one command so discovery turns into first use while intent is still warm.
Draft support replies from the help center, past conversations, changelogs, and issue history so the first answer starts from company memory instead of whoever happens to be on shift.
Use AI support questions as a publishing queue by reviewing the weekly docs-gap report and updating the article that should have answered the question.
Serve `/.well-known/llms.txt` and `/.well-known/llms-full.txt` alongside the root files so agents that follow the well-known convention can discover your corpus without guessing.
Add `Link` and `X-Llms-Txt` headers to normal page responses so an agent can find your machine-readable corpus before it starts crawling blindly.
Publish an `llms-full.txt` file that bundles the important documentation corpus into one fetch for agents that work better with a single large context payload.