If you run a professional services firm in Malaysia, the fastest way to know where you stand against competitors is to run the same 15-20 buyer questions through ChatGPT and Perplexity, log which firms get named, and score the pattern. This is the same core method behind a proper llm visibility audit: structured prompts, consistent scoring, and a side-by-side view of who the models trust. Firms that have run this exercise for professional services clients in Malaysia typically find that one or two competitors dominate the answers, not because they are better firms, but because they have more content the models can cite [aitraining2u.com]. That gap is measurable, and once measured, it is fixable.

TL;DR

• ChatGPT and Perplexity answer buyer questions by citing sources they trust, not by ranking websites the way Google does, so visibility inside these tools requires a different kind of audit.

• A basic benchmark needs 15-20 realistic buyer prompts, run against your firm and three named competitors, scored on mention frequency, sentiment, and source type.

• The three most cited AI assistants in current market comparisons are ChatGPT, Google Gemini, and Microsoft Copilot, with Claude and Perplexity close behind for research-style queries [blackstoneintelligence.com.my].

• Generative engine optimization and Google AI Overview optimization are related but distinct disciplines: one targets conversational LLMs, the other targets Google's summary box, and firms need both.

• Content formatted specifically for LLM extraction, not just classic SEO copy, is what tends to move the needle in these scorecards [webthreeconsulting.com].

About the Author: This article draws on Simaia's work running AI search audits and competitor gap analysis for B2B firms across APAC, including professional services companies benchmarking themselves against named competitors inside ChatGPT, Gemini, Claude, and Perplexity.

What Is an AI Visibility Scorecard and Why Does It Matter for Professional Services Firms?

An AI visibility scorecard is a structured comparison showing how often your firm, versus named competitors, gets mentioned, recommended, or cited when someone asks an AI assistant a buying-relevant question in your category. For a Malaysian professional services firm, that means questions like "best corporate secretarial firm in Kuala Lumpur" or "which audit firm handles cross-border tax for manufacturers in Penang." The scorecard records, prompt by prompt, whether your firm appears, where it ranks in the answer, and which source the model pulled the information from.

This matters because buyers are increasingly asking these questions to AI tools before they ever open Google or call a referral. A firm that has never appeared in this kind of side-by-side comparison has no way of knowing whether it is invisible to this channel entirely, or visible but losing ground to two specific competitors. Without the scorecard, firms are guessing. With it, the gap becomes a list of specific pages, mentions, and platforms to fix.

How Do ChatGPT and Perplexity Actually Decide Which Firms to Mention?

ChatGPT and Perplexity decide which firms to surface based on which sources they trust and how directly those sources answer the question being asked, not based on domain age or paid placement. Perplexity leans heavily on live web retrieval and tends to cite recent articles, directories, and forum discussions in real time. ChatGPT draws more on a blend of its training data and, when browsing is active, on pages that are structured clearly enough to be extracted and summarized.

The practical implication: a firm that has a well-written "About Us" page but no third-party mentions, no comparison content, and no forum presence will often lose to a smaller competitor that has been mentioned across LinkedIn posts, industry publications, or Reddit threads discussing the category. This is the core mechanism behind generative engine optimization. Classic SEO optimizes a page to rank in a list of ten blue links. Generative engine optimization optimizes a firm's entire footprint, on-site and off-site, so that when a model needs to answer a question, it has enough clearly structured, corroborated information about your firm to cite it with confidence.

How Do You Build a 3-Competitor Benchmark Step by Step?

Building the benchmark starts with picking the right competitors and the right prompts, not with picking the right tools. Follow this sequence:

1. Name three real competitors. Choose firms your sales team already loses deals to, not just the biggest names in the market. The comparison is only useful if it reflects actual buyer choice.

2. Draft 15-20 buyer-intent prompts. Mix direct comparison prompts ("X firm vs Y firm for [service]"), category prompts ("best [service] firm in [city]"), and problem-based prompts ("who can help with [specific compliance issue] in Malaysia").

3. Run each prompt in both ChatGPT and Perplexity. Log the full answer text, not just whether your firm appeared. Note position (first mentioned, buried in a list, absent entirely) and the source cited, if one is given.

4. Score each result. A simple three-column scorecard works well: mentioned (yes/no), sentiment (positive, neutral, negative), and source type (owned content, third-party media, forum, directory, none identifiable).

5. Repeat monthly. LLM answers shift as new content gets indexed and as models retrain or re-crawl the web, so a single snapshot understates or overstates the real trend.

Running this manually across two models and three competitors for 15-20 prompts is roughly 90-120 individual data points, which is why most firms eventually move to a repeatable process or an ai search visibility tool rather than redoing it by hand every month.

What Should You Do Once You See the Gap?

Once the scorecard shows exactly where a competitor is winning, the fix is almost always a content and distribution gap, not a product or service gap. Stepping back from the audit mechanics, the harder question is what to actually do with the data once it is in front of you. If Competitor A gets cited from a LinkedIn thought-leadership post and Competitor B gets cited from a press release picked up by a local business publication, the pattern tells you exactly which two channels to prioritize, rather than guessing across ten possible tactics.

This is where the discipline splits into two related but distinct tracks:

• Generative engine optimization focuses on conversational assistants like ChatGPT, Claude, and Perplexity. It rewards content that directly answers a question in the first few sentences, cites credible detail, and gets corroborated by third-party mentions elsewhere on the web [webthreeconsulting.com].

• Google AI Overview optimization focuses on Google's own AI summary box, which draws more heavily on structured on-site content and pages that already rank well organically. The two overlap, but a firm optimizing only for one will still be invisible in the other.

A useful analogy: think of your firm's AI visibility like a witness being asked about you in a courtroom. If only your own lawyer (your website) has ever spoken about you, the judge (the LLM) has one biased source and will hedge or stay silent. If three independent witnesses (press, LinkedIn, industry forums) have all said consistent, specific things about your work, the judge cites you with confidence. Content volume alone does not fix this; corroboration does.

Frequently Asked Questions

How many prompts do I need to run for a reliable benchmark?
Fifteen to twenty prompts per model is a reasonable minimum for professional services, covering direct comparisons, category searches, and problem-based questions. Fewer than ten tends to produce noisy, unreliable patterns.

Is ChatGPT or Perplexity more important to benchmark first?
Perplexity relies more on live retrieval, so it reacts faster to new content. ChatGPT's answers shift more slowly. Benchmarking both gives a short-term and a medium-term view of the same gap.

What are the top AI assistants firms should track beyond ChatGPT and Perplexity?
Current comparisons cite Claude, Google Gemini, and Microsoft Copilot as the most frequently referenced alternatives worth tracking alongside ChatGPT [blackstoneintelligence.com.my].

Do I need a dedicated ai search visibility tool or can I do this manually?
Manual benchmarking works for an initial gap check. Ongoing monthly tracking across multiple models and competitors is where a dedicated tool or managed service becomes worth the time saved.

Does Google AI Overview optimization require different content than generative engine optimization for ChatGPT?
They overlap substantially but are not identical. Google AI Overview draws more from pages that already rank organically, while ChatGPT and Perplexity weight third-party corroboration and clear, directly-answering content more heavily [webthreeconsulting.com].

How fast can a firm improve its AI visibility score?
Improvement depends on how much corroborating content exists across owned and third-party channels; firms that publish consistently formatted, LLM-extractable content alongside press and social mentions tend to see measurable shifts within a few months rather than years.

What is the single biggest mistake firms make with this kind of benchmark?
Running it once and stopping. LLM answers change as new content is indexed, so a scorecard is only useful as a repeated, tracked exercise, not a one-time report.

About Simaia

Simaia is the marketing team for firms that do not want to hire a marketing manager, content writer, PR contact, and SEO consultant separately to solve this problem. Simaia runs the audit (the brain: AI search audits and competitor gap analysis across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview, built on 50 prompts run across models) and then writes and places the content that closes the gap (the body: on-site blogs formatted for LLM extraction, press releases, LinkedIn posts, and Reddit replies matched to whichever sources each model cites most). For one client, a global textile manufacturer, this approach took AI search visibility from near-zero to a 3.5x year-over-year increase in AI bot visits and a 10x increase in monthly inbound leads within two months. For a healthcare SaaS client in Australia, visibility grew from 0% to 45% of the category's AI-referred traffic in 2.5 months.

If your firm has never seen how it actually compares to named competitors inside ChatGPT and Perplexity, that is the first gap worth closing. Get in touch with Simaia to run the audit and see exactly where the visibility gap sits.

References

1. How to Rank in ChatGPT, Gemini, and Perplexity in 2026: Patterns From 50 B2B Sites Across Industries | WebThree Consulting (webthreeconsulting.com)

2. Professional Services AI Case Study Malaysia - Agentic Model (aitraining2u.com)

3. chatgpt competitors: Practical Guide - Blackstone Consultancy (blackstoneintelligence.com.my)