Content that
ChatGPT
quotes back.
Generic blog posts don’t get cited — and “AI content” gets ignored by the very engines it’s named after. I hand-write a complete content system — pillar pages, topic clusters, FAQ blocks, schema and internal links — engineered so ChatGPT, Perplexity and Google AI Overviews quote you by name. Real words, real expertise, built to be the source.
Tanzid · Content LeadPack · 12 weeks →Not blog posts.
A citation
system.
A Generative-Engine Content Pack is a system of pages designed to be retrieved and quoted by LLMs — not just to rank in Google. Each piece earns its place in the cluster, links to its neighbors, and carries the structured data AI needs to trust it.
One pillar.
Nine satellites.
Inside a
citation-ready
article.
The H1 phrases what the page IS, not what it targets. Author byline + Person schema appears below so the LLM knows who is making the claim.
Inverted-pyramid: the citation-worthy answer lives in the opening paragraph. LLMs retrieve passages with high information density at the top.
Article + Person + FAQPage JSON-LD wrapped on the page. The LLM's retrieval layer sees this before the human-readable content.
H2 sections phrased as the question a human would type. Each section self-contained, answerable in isolation — the slot an LLM retrieves and quotes.
A FAQ block with the exact phrasing real users type into LLMs. Wrapped in FAQPage schema. This is the single highest-ROI section.
Anchor text linking laterally to the 8 cluster siblings + back to the pillar. Builds topical authority across the whole graph.
AI search engines cite content that is structurally clear, schema-wrapped, and authored by a recognizable entity. The three signals that matter most are: (1) JSON-LD article + author schema, (2) answer-first formatting, (3) external entity ties via sameAs.
ChatGPT Search retrieves passages based on...
Perplexity exposes its sources inline...
Related: Topic clusters explained · Schema for SaaS · Entity SEO basics
One pillar.
Nine clusters.
One FAQ hub.
What's in a pack
How the pack
gets built.
Topic mapping
Customer-language research, LLM query mining, competitor citation audit. We pick the pillar.
Cluster design
Map the 9 cluster articles, internal link graph, FAQ slots. You sign off before a word gets written.
Drafting
I write each page myself — answer-first, schema-ready. Two pages per week, reviewed live in Slack.
Schema + links
JSON-LD wrapping, internal anchor pass, sameAs entity wiring, technical QA on every page.
Publish + track
Phased publish, citation tracker live, monitor first appearances in ChatGPT / Perplexity / AIO.
What success
looks like.
What lands
in your CMS.
Research + drafting half
Structure + ship half
Is the pack
for you?
Founder-led SaaS
When your buyers are researching in ChatGPT and Perplexity before they ever hit your demo CTA.
Content-thin site
You have a great product but five marketing pages. The pack adds the depth AI needs to recommend you.
Hit by AIO cannibalization
Google's AI Overview now answers your top queries without sending the click. The pack makes you the cited source.
Consultant / personal brand
You need AI to recognize you as THE entity for a topic. The pack builds the author + topical authority for that.
Pre-launch positioning
Stake your topic before competitors do. The pack gets your entity into the LLM's graph before the launch.
E-commerce category leader
Pillar pages on category education + cluster on product comparisons. Citation-ready category SEO.
The honest answers.
No. Every page is hand-written by me, top to bottom. I use AI to mine LLM queries and stress-test phrasing, but no draft is generated by a model. That's the whole point — generic AI content does not get cited.
First AI citations typically appear 30–60 days after publish. Traditional ranking lift follows in 3–4 months. The schema-wrapping accelerates discovery on the LLM side.
Better — clusters work best on narrow expert topics where there's little authoritative coverage. We map the LLM query space, find the underserved angles, and build the pack around those gaps.
Both. The semantic + schema layer is language-agnostic. Bangla packs get the same treatment with bn-BD locale schema and hreflang. Many clients run a bilingual pack to capture both markets.
Then we likely audit first — kill 20–30% of low-yield pages, restructure 5–6 of your existing ones, and build a smaller targeted pack (e.g. 1 pillar + 4 clusters). Sometimes the right move is subtraction.
Yes — I deliver in your CMS (WordPress, Webflow, Shopify, Sanity, Contentful, headless) or in clean markdown for your team to ship. Schema injection is included either way.
Yes — many clients extend into a monthly cadence (1 pillar + 3 clusters per quarter). Pricing for that is discussed during the call once we see your topic space.
It will. The pack is designed around durable principles — entity definition, semantic structure, topical authority — not specific algorithm quirks. The schema and clusters keep working regardless of which LLM is on top.