GetHalalMalaysia helps overseas importers, food and cosmetic brands and contract manufacturers work through Malaysian Halal certification. The buyers are abroad and cautious, and they research the process in detail before they write to anyone.
The Challenge
- The site was producing no enquiries. Before the work started, the owner had no leads coming from the website at all.
- Halal is one of the harder industries to run paid ads in. The sales cycle is long and the buyer needs a lot of reassurance before closing.
- Overseas buyers now put the question to an AI assistant, "who can help me get Malaysian Halal certification", and act on the shortlist they get back. If the site is not on that shortlist, it does not exist for them.
The Strategy
1. Map the questions a foreign buyer actually asks
Keyword targeting alone does not cover this. An overseas buyer asks a chain of questions: what JAKIM requires, how long it takes, what it costs, how the Malaysian standard compares with other certification bodies. I mapped those chains and built each page to answer a full chain rather than a single phrase.
- Cost and timeline: "Cost and timeline for foreign companies to get JAKIM Halal certification"
- Comparison: "Malaysia Halal standard vs other international Halal certification bodies"
- Process: "JAKIM Halal application requirements for overseas food and cosmetic manufacturers"
Supporting vocabulary covered MS1500, halal supply chain terms and import compliance, so the pages read as a specialist source.
2. Build a clean, verifiable directory layer
- Consultants, logistics providers and manufacturers were gathered with Python scripts. Entries rated below 3.5 stars or that could not be verified were dropped and redirected with 301s to the core category pages.
- Page titles rotated across five sentence patterns, aimed at specific buyer regions such as Middle East importers, ASEAN partners, European cosmetics brands and US food distributors.
- Any missing field shows a dash instead of a broken string or filler text.
3. Make the entity easy for AI to verify
Every content page carries one author card tied to a named expert and consistent schema markup. AI systems cite sources they can attribute. An anonymous directory is easy to skip, a page with a named, checkable author is not.
The Result
| Metric | Before | After |
|---|---|---|
| Leads from the website | 0 | 9 |
| Leads who said they found the business through AI | None | All 9 |
| Leads from China | 0 | 4 |
| Leads from Malaysia | 0 | 5 |
The business owner sent a WhatsApp update, which was shared on Threads and then quoted in a post by the owner. Translated from the Malay, the message reads: of the 9 leads received, every one said they found the business through AI; 4 were from China and the rest from Malaysia; none had closed yet but many enquiries were coming in; and there had been no web leads at all before.
The AI answer screenshot shows the other half. Asked "Who is halal consultant to get halal cert from malaysia", the assistant first explains that JAKIM does not endorse consultants, then lists three firms. GetHalalMalaysia is the third, described as offering end-to-end guidance covering gap analysis, documentation support and submission readiness.
Why it worked
AI assistants assemble an answer from sources that state facts plainly and agree with each other. The pages gave a foreign buyer the regulatory detail they were already asking about, the data was clean, and the author was identifiable. That made the site safe for a model to cite.
How to apply this
- Write the buyer's whole question chain into the page, including cost, timeline and the regulator's role, not only the head keyword.
- Keep directory data verified and strip out weak entries so every listing can be defended.
- Attach a named author and schema to every page so the source can be attributed.
- Format answers as short definitions, lists and tables that a model can lift cleanly.