How to Improve Visibility in ChatGPT & Perplexity (2026)

Step 1 — Audit your current AI citations

Build a stable baseline from buyer-intent questions grounded in query data, sales conversations, and real customer needs. Start with enough core questions to represent the decisions you care about, then add one only when it covers a distinct intent or market. Examples:

• “recommend a web design agency in Amman for a Saudi client”
• “who builds AI apps in Saudi Arabia for SMEs?”
• “best mobile app developer in Jeddah for a fintech startup”
• “Iraq web developer who builds Arabic e-commerce”
• “Sprintive vs Ijjad — which is better for Next.js sites?”

For each question, use the surfaces relevant to your buyers and available to your team. Log the exact product or feature, answer date, cited source URL, competitors, framing, and any referral session or qualified action. A spreadsheet is enough to start; specialist tools can automate larger samples.

Repeat the same core buyer questions monthly so changes are comparable. A mention without a source, referral, or qualified action is useful context, but it is not proof of ranking impact.

Step 2 — Prioritize the pages that matter

Not all pages deserve the same work. Join Search Console query and page data with conversions, backlinks, source age, and content quality. Prioritize material buyer-intent pages that are near a useful outcome, declining, or missing evidence; do not rely on an arbitrary ranking cutoff.

The resulting set may include service, city, comparison, research, tool, or guide pages. Its size and mix should follow the evidence, not a quota. A page such as SEO services in Saudi Arabia or SEO services in Jordan belongs only when its demand, buyer value, and current performance justify the work.

Step 3 — Write a concise, complete direct answer

Answer the primary intent early enough that a reader does not have to hunt for it. Use the natural length the question needs; there is no required word count, first-100-words rule, or forced brand opener. The format below is one useful example:

Three rules: (1) lead with the answer, not a superlative; (2) include geography only when it changes the answer; (3) include numbers only when they are supported and current. Completeness and evidence matter more than a formula.

The existing DirectAnswerBlock component keeps the answer visible and crawlable. Its semantic wrapper is useful for readers and auditing; it does not create a special AI ranking signal.

Step 4 — Use headings that reflect real subtopics

Use question headings when they match how a reader frames the subtopic. A clear statement heading is equally valid when it reads better. The goal is an understandable hierarchy, not converting every H2 into a query variation.

Use Search Console queries, customer questions, People Also Ask, sales conversations, and citation probes as research inputs. Consolidate close variants instead of manufacturing a heading or page for each phrasing.

Step 5 — Use a comparison table only when it helps

Use a table when it materially helps a reader compare several options or repeated fields. A table can make facts easier to scan and parse, but it is not a citation guarantee and should not replace necessary explanation.

Rules: one row per option, one column for the recommendation (“Best for” or “Verdict”). Use semantic <th> headers. Avoid merged cells. Include units in column headers (“Timeline (weeks)”, not just “Timeline”). The headless commerce comparison on our own blog is a working example.

Step 6 — Validate only supported, visible schema

Use helpers in src/lib/schema.ts to keep JSON-LD consistent, but emit a type only when the rendered page supports it:

• BlogPosting — with author Person, datePublished, dateModified, and dated image rights
• FAQPage — only for genuine visible FAQs, with exact question-and-answer parity
• HowTo — only for a complete, visible step-by-step task supported by current platform policy
• Speakable — limited and eligibility-dependent; not a general AI-search requirement
• BreadcrumbList — represent the same page hierarchy users can navigate

Validate rendered markup through validator.schema.org and the relevant official rich-result test. Schema can support eligibility and machine understanding; it does not directly improve web ranking or guarantee display or citation.

Step 7 — Keep llms.txt accurate if you publish it

llms.txt is an optional, emerging convention, not a search requirement or summary control. Ijjad keeps one as a low-cost experiment at ijjad.com/llms.txt. If you choose to publish one, keep it concise and factual:

• Entity facts — brand name, founder, HQ, founded, services, markets
• Selected canonical pages — include only URLs that help orient a reader or system; there is no required count
• Topic map — a factual note about which URL covers each major subject
• External corroboration — LinkedIn, Clutch, GoodFirms, Wikidata
• Clear limitations — no unsupported pricing, outcomes, or instructions to cite

Refresh it when underlying facts or priority URLs materially change. Spend ranking effort on crawlable content, evidence, independent authority, and measurement—not on expanding this file.

Step 8 — Show truthful authorship and review dates

Show who is responsible for the content, why that person is qualified to review it, and when it was materially checked. Link to a real author profile such as /about/karam-abdalqader, and keep any Person markup consistent with the visible bio.

Authorship helps readers evaluate a claim; repeated markup cannot create expertise. Use the same accurate identity across relevant pages and update review dates only after a real review.

Step 9 — Write fair comparisons when buyers need them

Mention competitors only when a fair comparison helps the buyer. Use verifiable criteria, acknowledge where each option fits, and link to a primary source when it supports the claim. Manufactured name-dropping and blanket rules against external links both reduce usefulness.

Our Sprintive vs Ijjad comparison is an example of intent-specific comparison content. Its visibility must be measured per engine and prompt over time; mentioning entities alone does not cause a citation.

Step 10 — Track citations and outcomes monthly

Run the same core buyer questions monthly across the relevant available surfaces. Log the cited URL, competitors, framing, answer date, referral sessions, and qualified actions. Use this field guide for the tracker:

Look for engine-specific changes rather than assuming a shared crawl cadence. If citations do not move, inspect query intent, crawlability, content evidence, independent authority, and whether the page is actually the best source for that question. Do not diagnose the cause from elapsed time alone.

A repeatable implementation loop

Use this loop to preserve dependencies without promising a fixed delivery or citation schedule.

Frequently asked questions