LLMO Prompt Intelligence | Ignited Nepal — Growth Engineering for Australian Businesses

Your Competitors Are Being Cited by AI. You Don't Know Which Prompts Are Doing It.

Australian businesses are operating in a market where AI-generated answers are becoming a primary discovery channel — and most brands have no systematic picture of which prompts their customers are using to ask ChatGPT, Gemini, Perplexity, or Claude about their category. They don't know which query patterns trigger citations, which formats earn recommendations, or where competitors are holding citation advantages that compound over time. LLMO Prompt Intelligence maps the full prompt landscape of your category across 100+ queries, scores every one by citation opportunity, and delivers a ranked, actionable prompt library so your team knows exactly where to direct content investment.

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This is for you if

Who This Is For

You are starting LLMO work and don't know which queries to target. Your leadership understands that AI search is reshaping how customers find products, services, and advice. Your team has read the think-pieces and sat through the briefings. But when it comes time to actually build a content programme, there's a gap: nobody has a systematic list of the prompts your customers use when asking AI about your category. LLMO Prompt Intelligence fills that gap before the content work begins.

You have existing AEO or GEO work and want to expand it into LLMO. Australian businesses that started early on Answer Engine Optimisation or Generative Engine Optimisation have built genuine capability in structured content and featured snippet targeting. That capability is a useful base. But LLM citation behaviour operates on different mechanics, and the prompts that matter in conversational AI are not simply a superset of your existing keyword targets. Expanding properly means building a separate prompt intelligence layer.

You run or lead a marketing team that needs AI-native prompt intelligence. Your team is sophisticated about search. You run keyword research, you track rankings, you understand content gap analysis. But keyword research was designed for a world where users type fragments into a search bar. LLM users ask full, specific, conversational questions — and the intelligence discipline you need to compete in that environment starts with mapping those questions at scale in your specific market and category.

You've noticed competitors being cited in AI responses and want to understand why it's happening. You've tested a few prompts yourself or had a client point it out. A competitor is being named in AI responses about your category. You don't know whether it's consistent, which prompts trigger it, how many LLMs are doing it, or what that competitor holds that's driving the citation. This service answers all of those questions with documented evidence rather than speculation.

What's broken

What's Broken

You are making LLMO decisions on a sample of one.

Most teams that have engaged with LLMO at all have done it by running a handful of test prompts and noting whether their brand or a competitor appears. That is a useful first step. It is not a strategy. The prompt landscape for any meaningful category contains dozens of distinct query types across different intent stages, different phrasings, different levels of specificity. Running five prompts and drawing conclusions from them is like auditing your SEO performance by checking three keyword rankings.

You are targeting prompts where citation is structurally unlikely for your brand.

Some prompts generate closed LLM responses — the model answers entirely from training data with no external source citation. Others generate responses that cite only high-authority reference sources your brand cannot reasonably compete with in the near term. If you are building content against these prompts, the effort is real but the citation return is close to zero. The opportunity scoring step of this service exists specifically to prevent that mistake.

You are missing the prompts where your Australian competitors are already being cited.

Competitor citation in AI responses is not random. It correlates with specific prompt patterns, specific content structures, specific authority signals, and specific platform presences. Your competitors are earning those citations in prompts you have probably never tested. The competitive gap is not visible — and what you can't see, you can't close.

There is no off-the-shelf tool for Australian LLMO prompt intelligence.

Global keyword research tools are not built for LLM prompt mapping. They were designed to surface search engine query data, not to identify the conversational question patterns that trigger AI citations in Australian market categories. The intelligence has to be built through direct LLM probing, structured category analysis, and systematic testing. That process is what this service delivers.

What we engineer

What We Do

Prompt Mining Report

100+ category prompts built from your seed terms, service or product descriptions, competitor names, and problem statements, using structured LLM probing and category analysis across the Australian market context

Prompt Categorisation Map

every prompt sorted by intent type: awareness queries, comparison queries, buying-intent queries, and brand-specific queries, with intent notes for each category

Citation Opportunity Scores

each prompt tested across five LLMs with full results documented: sources cited, citation frequency, response format, and an opportunity score assessing how achievable a citation is for your brand at its current authority level

Competitor Citation Map

for every high-opportunity prompt, a documented record of which competitors are being cited, how often, and what signals appear to be driving those citations

Ranked Prompt Library

all 100+ prompts ranked by citation opportunity tier with rationale for each ranking

Implementation Brief

per-tier action guidance explaining what to write, in what format, with what signals, to maximise citation probability across each tier of the ranked library

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What changes

What Changes

Before
After
Before Your content investment stops going to low-return prompts.
After Without opportunity scoring, content teams default to prompts that feel strategically significant. Many of those prompts are genuinely important — but not all of them represent achievable citation opportunities at your current level of authority. The ranked library tells you which prompts represent genuine near-term opportunities and which require longer-term signal building first, so budget and time go where they can produce results now.
Before Your LLMO programme has a clear starting point and a logical sequence.
After One of the most common failure modes in LLMO programmes is paralysis at the point of prioritisation. There are too many possible prompts, too many content formats, too many LLMs to consider. The ranked library eliminates that paralysis. High-opportunity prompts, with implementation briefs, give your team a clear first sprint. Medium-opportunity prompts give you the second. The sequencing is done.
Before You can explain competitor citation advantages in specific, actionable terms.
After When a competitor is being cited in AI responses, the question that matters is not just "why them and not us" — it is "which specific signals do they hold that we don't, and which of those can we build." The competitor citation map in this service answers that question directly, converting a competitive threat into a list of discrete gaps.
Before Your marketing, SEO, and content teams align around the same prompt targets.
After The prompt library becomes a shared operational document. Instead of SEO pursuing one set of keyword targets, content pursuing another set of topics, and PR pitching stories without LLMO framing, all three functions can orient around the same ranked set of prompts. That alignment has compounding value over time.
Common questions

FAQ

What is LLMO prompt intelligence?

LLMO prompt intelligence is the systematic discovery, categorisation, and opportunity scoring of the conversational queries people use to ask large language models about a category, a competitor, or a specific problem — with the goal of identifying which of those queries represent achievable citation opportunities for a specific brand. It is the research foundation of any LLMO programme and the intelligence layer that separates targeted LLMO content investment from guesswork.

How is this different from keyword research?

Keyword research maps fragmented, index-optimised terms that users type into search engines. LLMO prompt intelligence maps the full, conversational questions that users ask LLMs — typically longer, more specific, and structured as requests for recommendation, comparison, or explanation rather than as index queries. The signals that earn citations in LLM responses are also structurally different from the signals that earn search rankings. Content depth, citation format, topical authority, and structured data all play different roles. The two types of intelligence are complementary, but they are not the same input.

What makes a prompt high-opportunity for an Australian business?

A prompt scores as high-opportunity when the LLM consistently draws on external sources when answering it, when the existing citation set is not completely dominated by global reference sources with authority levels your brand cannot match in the near term, and when the signal patterns of cited sources are ones your brand can realistically replicate or improve on. For Australian businesses, this often means prompts where local context, local expertise, or locally-relevant comparisons are part of the response — prompts where a well-positioned Australian source has genuine competitive relevance against global generalist content.

What is the turnaround time?

The standard engagement runs two to three weeks from briefing to delivery. The first week covers category prompt mining and categorisation. Weeks two and three cover citation opportunity scoring and competitor citation mapping across all 100+ prompts. Categories with higher complexity or broader scope may require a slightly extended timeline, which is discussed at briefing.

What does this cost?

Australian engagements are priced from AUD 4,500 for a single-category prompt library covering 100+ prompts across five LLMs with full scoring, competitor citation mapping, and implementation briefs. Multi-category engagements and ongoing monitoring programmes are scoped separately based on category breadth and competitive density. Pricing is confirmed after a discovery call.

How does the output get used in practice?

The ranked prompt library becomes the operational centre of your LLMO content programme. Content teams use the high-opportunity tier as the brief for their next production sprint — each prompt has an implementation note explaining what format and signals are most likely to earn a citation. SEO and PR teams use the competitor citation map to identify specific signal gaps to close. Marketing leadership uses the prompt categorisation to understand how AI is framing the category and where the brand currently sits. Most clients revisit the library each quarter as LLM citation behaviour continues to evolve.

Our team

The people behind the work

Not a black box. Real specialists you can call, with their names on the work.

Niraj Raut

Niraj Raut

Founder — Ecommerce SEO
Keshab Joshi

Keshab Joshi

PPC Expert
Hawrry Bhattarai

Hawrry Bhattarai

Google Ads Expert
Arogya Rijal

Arogya Rijal

SaaS SEO Expert
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Build Your LLMO Programme on Intelligence, Not Assumptions

The Australian businesses that will earn consistent AI citations over the next two years are the ones that started with a clear map of their prompt landscape — not the ones that guessed at prompts and hoped the content would land. Get the ranked library. Know your highest-opportunity targets. Build from there.

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