AI search optimization services: get cited in ChatGPT, Perplexity and AI Overviews

What is AI search optimization?

AI search optimization is the work of making a brand's pages easy for AI answer engines to find, understand, quote and recommend. Azimuthforge, an India-based AI and software engineering company, delivers it for businesses that want to be cited in ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot. It is also called AEO or GEO.

AI answer engines work differently from a list of ten blue links. They break a question into several sub-searches (often called query fan-out), read passages from many pages, and write one answer that names a few sources. Being one of those sources depends on how clearly your page answers a specific sub-question, and on whether the engine can trust and verify who you are. A study by researchers from Princeton, Georgia Tech and other institutions, Generative Engine Optimization, presented at KDD 2024, found that adding statistics, quotations and cited sources to content improved its visibility in generative answers.

What do we do to get a brand cited in AI answers?

Azimuthforge works on eight areas that decide whether AI answer engines can cite a site: answer-first content, coverage of the sub-questions behind each topic, structured data, crawler access, server-rendered pages, consistent entity information, mentions on other trusted sites, and measurement so the effect is visible.

• Answer-first content. Each page and each section opens with a short, self-contained answer that can be quoted on its own, followed by the detail.
• Query fan-out coverage. We map the sub-questions an engine is likely to ask for a topic and make sure your site answers each one, with a dedicated passage or page.
• Structured data. Company, service, FAQ, article and product markup in JSON-LD that matches the visible page, so engines can read facts without guessing.
• AI crawler access. A robots.txt and edge configuration that deliberately allow the search and retrieval crawlers you want, such as OAI-SearchBot, PerplexityBot and Googlebot, and a considered decision on training crawlers.
• Server-rendered pages. Key content delivered as HTML on the first response, because many AI crawlers do not run JavaScript.
• Entity consistency. The same company name, description, address, profiles and facts everywhere, so engines resolve you as one clear entity.
• Off-site mentions. Accurate listings, reviews, directories, partner pages and press, because engines weigh what other sites say about you.
• Measurement. A fixed prompt panel, described below.

We also publish an llms.txt file when a client wants one. It is a low-priority hedge, not a ranking factor, and we do not rely on it.

How do we make AI search work hold up in production?

Azimuthforge treats AI search optimization as engineering with a feedback loop: technical access is fixed first, content is rewritten to be quotable, and results are tracked over time against a fixed set of prompts. Because AI answers vary from run to run, we compare trends across repeated runs, not single screenshots.

1. Fix access before content. If a crawler cannot fetch or render a page, no amount of writing helps. We check robots.txt, firewall and bot-protection rules, status codes and rendered HTML first.
2. Build the prompt panel. We agree a set of real buyer questions with you, covering brand, category and comparison queries. The panel stays fixed so changes are comparable.
3. Record a baseline. We run the panel across the engines that matter to your market and log whether you are mentioned, cited with a link, recommended, or absent, and which competitors appear.
4. Change one thing at a time where we can. Content, markup and access changes are logged against dates, so a shift in citations can be traced to a cause.
5. Re-run on a schedule. Answers are probabilistic, so we repeat each prompt and report the trend and the range, not a single result.
6. Report honestly. We report where you are cited, where you are not, and what we believe is the reason. We do not promise rankings in any engine.

SEO vs AI search optimization: what is the difference?

Traditional SEO and AI search optimization share most of their foundations, but they differ in what is being won. SEO competes for a ranked position on a results page; AI search optimization competes to be quoted inside a generated answer. The table below sets out where the two overlap and where they diverge.

	Traditional SEO	AI search optimization
Goal	Rank a page for a keyword	Be cited and recommended inside a generated answer
Unit of competition	The page	The passage
Query shape	Short keyword phrases	Long conversational questions, expanded into sub-searches
Content style	Comprehensive pages with keyword targeting	Answer-first passages that stand alone when quoted
Technical needs	Crawlability, speed, indexation	The same, plus access for AI crawlers and server-rendered HTML
Off-site signals	Backlinks	Backlinks plus brand mentions, reviews and consistent entity data
Measurement	Rankings, clicks, impressions	Prompt-panel mentions, citations, recommendations and referral traffic
Predictability	Relatively stable	Varies between runs and across engines
Relationship	The foundation	Builds on it and cannot replace it

AI engines often retrieve from conventional search indexes, so strong SEO remains the base. AI search optimization adds the passage-level and entity-level work on top. Our website development service builds both into a site from the start.

Proof from our own work

Azimuthforge applies AI search optimization to its own properties before recommending it to clients, including its own website and the Restrofi platform. Both are properties where we control the whole stack, so we can test changes in crawler access, markup and content directly.

• azimuthforge.com runs on Next.js on Cloudflare's edge network, with server-rendered pages, JSON-LD structured data, answer-first content and deliberate AI crawler access.
• Restrofi, the restaurant and hotel operations platform we build and operate, uses the same approach: answer-first pages, structured data and crawler access.
• Fatten Media and ANM Prefabs are client websites we built that are live in production.

Where a project involves a custom assistant or search feature as well, see RAG and LLM development.

How does an AI search optimization project work?

An Azimuthforge AI search optimization project runs in six stages: an audit, a prompt-panel baseline, a prioritised plan, implementation of technical and content changes, an off-site entity pass, and ongoing measurement. We work with clients in the UK, US, Australia and the UAE, with a daily overlap window in their time zone.

1. Audit. We review crawler access, rendering, structured data, content structure and entity information.
2. Prompt panel and baseline. We agree the questions and record where you appear today.
3. Plan. You receive a prioritised list of changes, each with its reason and effort.
4. Implement. We change code, markup, content and configuration, or hand a precise specification to your team.
5. Entity and off-site pass. We correct inconsistent profiles and identify sources worth earning mentions from.
6. Measure and iterate. We re-run the panel on a schedule and adjust the plan from what the data shows.

What drives the cost of AI search optimization?

The cost of AI search optimization depends on how many pages and topics are in scope, how much of the site needs technical change, how much content must be written or restructured, how many engines and markets the prompt panel covers, and whether work continues after the first phase. Azimuthforge prices from a written scope.

• The size of the site and the number of topics to cover.
• Whether pages are already server-rendered or need re-engineering.
• The amount of content to write or restructure.
• The number of prompts, engines and markets measured.
• One-off improvement versus ongoing monitoring.

Every engagement is priced from a written scope after discovery, so you approve a fixed price before the build starts.

How do we handle security, ownership and data protection?

Azimuthforge keeps AI search work inside your own accounts: changes are made in your repository or content system, you own everything we produce, and we ask only for the access the work needs. Measurement uses public prompts and does not require customer data. We design for India's Digital Personal Data Protection Act, 2023.

• Code, markup and content are delivered to you and owned by you.
• Access is scoped to what is needed and removed at the end of the engagement.
• Analytics and tracking are configured with consent in mind, with UK GDPR and GDPR considered as a design requirement for overseas audiences. This is a design consideration, not a certification.
• Bot-access decisions, such as whether to allow AI training crawlers, are made with you and recorded.