Writing content that AI search engines actually cite

Most B2B tech content does not get cited by ChatGPT, Perplexity or Copilot. That’s not a slight on the writers. It’s a function of how those engines pick what to quote. The pages they reach for share a small set of structural and stylistic traits, and once you know what they are, the rewrites become straightforward.

We’ve audited dozens of citation patterns for clients across MSPs, SaaS and ERP consultancies through 2025 and into early 2026. Here are the writing habits that, in our experience, raise the odds of getting cited.

Lead with the answer

The single biggest determinant of whether a page gets quoted is whether the answer can be lifted in one or two sentences from near the top of the page. LLMs reward directness. They penalise ramps.

Your opening paragraph should state the claim, name the entity and give the model something quotable. Compare these two openings.

“In the rapidly evolving world of cybersecurity, businesses of all sizes are increasingly turning to managed detection and response solutions to protect their critical infrastructure from emerging threats…”

“Managed detection and response (MDR) is a security service that combines technology and human analysts to detect, investigate and respond to threats on a customer’s network. It typically costs £15 to £40 per endpoint per month for mid-market businesses.”

The second one will get cited. The first one will not. The model can lift it cleanly, the entity is named, the definition is plain and the cost detail gives a Perplexity-style answer something concrete to attribute.

Write with named entities

LLMs index by entity. When you write about a topic, name it. When you write about a product, name the product, the company that makes it, the category it sits in and what it integrates with. We’ve seen pages get cited because they were the only source on the open web that named a specific combination of products in the same paragraph.

This is also why generic, anonymised case studies underperform. A case study that says “we helped a leading manufacturer reduce downtime by 30%” gives the model nothing to attach to. A case study that names the client, the technology stack and the specific outcome gives the model a quotable, attributable claim. We wrote about this from a different angle in case studies that close.

Use definitional sentences

A definitional sentence is one that takes the form “X is Y that does Z”. It is the simplest unit a model can quote. Tech writing tends to drift away from this form because writers want to sound less robotic, but a section that opens with a clean definition before getting more nuanced is far easier to cite than a section that meanders.

For technical topics, we usually advise our clients to draft a glossary of fifteen to thirty key terms and treat each definition as a quotable asset. These can live on a glossary page or be sprinkled into longer pieces. We covered the structural side of this in our piece on topic clusters for tech companies.

Use H2s as questions or claims, not labels

A subhead like “Approach” tells the model nothing. A subhead like “How MDR pricing actually works” or “Why MSPs need 24/7 SOC coverage” gives the model a clear retrieval target. LLMs use heading text heavily when matching passages to queries.

We typically rewrite client subheads to do one of three things:

• Pose a question a buyer would ask.
• State a defensible claim.
• Name a specific framework, process or comparison.

This is one of the cheapest changes you can make to existing content and we’ve seen it shift citation patterns within a few weeks of recrawl.

Keep paragraphs short and self-contained

LLMs retrieve passages, not whole pages. A four-line paragraph that contains a complete idea is more useful to a model than a twelve-line paragraph that develops an argument across multiple turns. This is not a hard rule, but as a working habit it pays off.

If your style guide allows it, treat every paragraph as something that should still make sense if it were the only sentence the model showed in its answer. That’s a high bar. Even getting halfway there improves citation odds noticeably.

Include numbers, ranges and named comparisons

Citations skew towards content with concrete data. Prices, percentages, counts, named tools, named standards. Even rough ranges count. A line that says “MSPs typically charge £50 to £150 per user per month for fully managed IT” will get cited more often than a line that says “MSP pricing varies based on the level of service”. Comparison-style prompts in particular pull this kind of content, which we cover in optimising for compare-X-to-Y prompts.

This obviously has to be honest. Wrong numbers will eventually get spotted, and an LLM repeating your wrong numbers back to your buyers is not a win. In our experience, the highest-citing pages combine concrete data with explicit caveats about scope, geography and assumption. The hedge is part of the credibility.

Make authorship visible

The major LLMs lean on authorship and institutional credibility cues. Pages with a named author, a bio, a clear date and a stable URL outperform anonymous, undated pages on similar topics. We’ve seen Perplexity in particular weight authored content more heavily than unattributed marketing copy.

For B2B tech sites this means putting real names on posts, including credentials where they exist and linking author bios to LinkedIn or other public profiles. It also means adding Article and Person schema, which we cover in structured data for AI search.

Build internal context, not just internal links

Internal linking still helps for retrieval. We covered the basics in internal linking for tech sites. What’s specific to AI search is that a model uses the surrounding sentence to decide what the link is about. “We’ve written more about this elsewhere” is wasted. “We’ve written about how MSPs price 24/7 SOC services in [link]” gives both the user and the model context.

What this looks like in practice

A lightweight rewrite framework we use with clients:

1. Top of page. Replace the introduction with a definitional paragraph. Lead with the answer.
2. H2s. Rewrite subheads as questions or claims, not labels.
3. First sentence of each section. Make it stand alone. Imagine it being quoted by Perplexity with nothing around it.
4. Numbers. Pepper in concrete ranges, prices, counts and named tools.
5. Author and date. Visible on the page, in schema, with a real bio.
6. Links. Inline, contextual, supportive of the surrounding sentence.

Most clients can apply this to ten to twenty existing posts before commissioning a single new piece, and that retrofit is usually where the early wins come from. The companion exercise is identifying what to write next, which we approach through AI search content gaps.

A note on tone

There is a temptation to write everything in flat, model-friendly prose. We don’t recommend it. The goal is to be quotable, not to be lifeless. Posts that read like they were optimised for an algorithm, rather than written by people who know the topic, lose human readers. Human readers are still the ones who buy. A good draft can carry both jobs at once: clear, declarative, named, with a recognisable voice underneath.

We work on this kind of editorial quality with clients as part of our content marketing and AI SEO services. The two have grown closer through 2025, and we expect the lines between them to keep blurring.

If you’d like a second opinion on your AI search strategy, drop us a line.