LLMO Prompt Intelligence | Ignited Nepal — Growth Engineering for US Markets

Every Day, AI Is Recommending Your Competitors. You Don't Know Which Prompts Are Doing It.

American businesses in competitive categories are operating in a market where AI-generated answers have become a primary discovery channel — and most of them have no systematic picture of which prompts their customers are using when they ask ChatGPT, Gemini, Perplexity, or Claude about their space. They don't know which query patterns trigger citations. They don't know which formats earn recommendations. They don't know which competitors are accumulating citation advantages that will compound over the next two years. LLMO Prompt Intelligence mines 100+ category queries, tests every one across five major LLMs, scores each by citation opportunity, and delivers a ranked prompt library your team can use immediately to direct content investment where it will actually produce AI citations.

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/ This is for you if

Who This Is For

You are beginning LLMO work and you don't have a prompt map for your category. Your leadership is aware that AI search is changing customer acquisition. The percentage of your target audience that starts research with a conversational AI query is growing. But when it comes to building an actual programme — which prompts to target, which LLMs to prioritise, which content to produce — the honest gap is that nobody has mapped the prompt landscape for your category. LLMO Prompt Intelligence is the map.

You have existing AEO or GEO work and want to layer in LLMO properly. American businesses that started early on Answer Engine Optimisation or Generative Engine Optimisation built real capability. Featured snippet targeting, structured Q&A content, People Also Ask coverage — that work has value. But LLM citation mechanics are different from snippet selection, and the prompts that drive citations in conversational AI are not a simple extension of your existing keyword targets. Expanding into LLMO properly means building a separate prompt intelligence layer, not repurposing your existing keyword list.

You lead or run a marketing team that needs AI-native prompt intelligence as a planning input. You've been running keyword research for years. Your team understands content gap analysis, SERP structure, and search intent. But LLM users phrase their questions differently from search users, and the intelligence discipline you need to compete in an LLM-mediated discovery environment starts with mapping those question patterns at scale. The ranked prompt library from this service is the LLMO equivalent of your keyword master list — the foundational input everything else builds from.

You've seen competitors cited in AI responses and want to understand the mechanism precisely. You've run the prompts yourself. A competitor — maybe several — is appearing in AI recommendations about your space. You don't know if it's consistent across LLMs. You don't know which specific prompt types trigger it. You don't know what signals that competitor holds that you currently don't. This service answers those questions with tested, documented evidence.

/ What's broken

What's Broken

Your LLMO efforts are built on a sample that's too small to be strategic.

American marketing teams that have engaged with LLMO have typically done it by running a set of test prompts — often ten to twenty — and drawing conclusions from the results. That's a starting point. But any given category has dozens of distinct prompt types across different intent stages, different phrasings, different levels of specificity. A prompt list of twenty is not a strategy. It's a sample from which almost any conclusion can be rationalized. Real strategic decisions require a complete picture of the prompt landscape, not a snapshot.

You are building content against prompts where citation is structurally out of reach at your current authority level.

Some prompts generate closed LLM responses where no external source is ever cited. Others generate citations that go exclusively to major US publications with decades of domain authority — sources your brand cannot realistically compete with in any reasonable timeframe. If your content investment is going to these prompts, the effort is real and the return is near zero. Citation opportunity scoring exists to prevent this mistake by identifying which prompts represent genuine near-term opportunities versus longer-term aspirational targets.

You are losing citation ground in prompts you have never tested.

AI citation is a compounding advantage. The brands that are being cited consistently in your category today are building citation signals — referenced by AI responses, mentioned in AI-generated content, associated with authoritative answers — that will make them harder to displace over time. The prompts where this is happening are specific and identifiable. You are not testing them. The gap is growing.

The US market's competitive density makes prompt prioritisation more critical, not less.

In a smaller market, spreading LLMO effort across a broad set of prompts might be survivable. In the US market, with the density of content investment, the number of well-resourced competitors, and the pace of LLM capability evolution, broad and unfocused LLMO work produces weak results. The businesses that earn consistent AI citations in competitive US categories are the ones that identify the highest-opportunity prompts and concentrate their signals there. That concentration is impossible without a ranked prompt library.

/ What we engineer

What We Do

Prompt Mining Report

100+ category prompts generated from your seed terms, product or service descriptions, competitor names, and problem statements, using structured LLM probing and category analysis calibrated to US market query behaviour

Prompt Categorisation Map

all prompts organised by intent type: awareness queries, comparison queries, buying-intent queries, and brand-specific queries, with notes on the intent dynamics specific to your US market category

Citation Opportunity Scores

each prompt tested across five major LLMs with full results documented: sources cited, citation frequency, response format, citation type (recommendation, comparison, reference), and an opportunity score assessing how achievable a citation is for your brand at its current authority level in the US market

Competitor Citation Map

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

Ranked Prompt Library

all 100+ prompts ranked by citation opportunity tier (high, medium, lower) with documented rationale for every ranking

Implementation Brief

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

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/ What changes

What Changes

Before

After

BeforeEvery content decision in your LLMO programme is tied to a tested citation opportunity.

AfterWhen your team sits down to plan the next quarter of LLMO content, instead of asking "what prompts should we target?" they are asking "which of our high-opportunity prompts do we tackle next?" Every piece of content has a prompt behind it, a citation opportunity score behind that, and a tested LLM behaviour pattern behind that. The strategic ambiguity is gone.

BeforeYour highest-concentration effort goes to the prompts where early results are most achievable.

AfterIn competitive US categories, LLMO programmes that try to compete for every prompt simultaneously generate weak signals across the board. The ranked library tells you which prompts represent the fastest path to real citations — the ones where your current signals are close enough to cited sources that focused content investment can close the gap within a quarter. You build from wins rather than from hope.

BeforeCompetitor citation advantages are documented specifically enough to act on.

AfterThe competitor citation map does not just tell you that a competitor is winning citations. It tells you which specific signals that competitor holds — content depth, structured data, review volume, publication patterns, topical authority — that your brand currently lacks. That level of specificity converts competitive intelligence from a diagnostic into an action plan.

BeforeYour LLMO programme has an operational document at its centre.

AfterThe ranked prompt library is not a one-time report. It is a living operational document that your content, SEO, and PR teams can work from quarter to quarter, updating as LLM behaviour evolves and as your brand's signals improve. Most clients refresh the scoring quarterly to track whether citation gaps are closing and to identify new prompts that have entered their category landscape.

/ Common questions

FAQ

Straight answers on scope, timelines and cost. Anything else, ask us directly.

Ask us directly

What is LLMO prompt intelligence?

LLMO prompt intelligence is the systematic discovery, testing, and opportunity scoring of the conversational queries people use to ask large language models about a category, a set of competitors, or a specific problem — with the output being a ranked set of citation opportunities for a specific brand. It is the foundational intelligence layer of any LLMO programme. Without it, teams produce content against guessed prompt targets and have no reliable way to know whether that content is reaching the citations it was designed to earn.

How is this different from keyword research?

Keyword research was designed to surface the fragmented, index-optimised terms users type into search engines. Those terms are typically short, intent-compressed, and built for matching against indexed documents. LLMO prompt intelligence maps the full, conversational questions users ask LLMs — longer, more specific, and structured as requests for recommendations, comparisons, or explanations. The citation signals that matter in LLM responses — content depth, topical authority, structured data, citation format, source reputation — are also materially different from the ranking signals that matter in search. The two types of intelligence are complementary inputs to an integrated content programme, but they are not interchangeable.

What makes a prompt high-opportunity for a US brand?

A prompt scores as high-opportunity when it meets three conditions: the LLM consistently draws on external sources when answering it (rather than generating a closed response from training data), the existing citation set is not completely dominated by sources the brand cannot realistically compete with, and the signal patterns of cited sources are ones the brand can match or exceed through focused content and authority building. In the US market, competitive density means that many prompts in established categories are already dominated by well-resourced sources — which makes accurate opportunity scoring especially critical, because the difference between a realistic citation target and an unrealistic one is not always obvious from prompt inspection alone.

What is the engagement timeline?

The standard engagement runs two to three weeks from briefing to delivery. Week one covers category prompt mining and categorisation. Weeks two and three cover citation opportunity scoring and competitor citation mapping across all 100+ prompts. For categories with large competitive sets or broad scope — enterprise software, financial services, healthcare, consumer goods — we discuss timeline adjustments at the briefing call.

What does this cost?

US engagements are priced from USD 4,200 for a single-category prompt library covering 100+ prompts across five LLMs with full scoring, competitor citation mapping, and implementation briefs. Multi-category engagements, ongoing quarterly monitoring programmes, and national-plus-local prompt libraries (for brands competing at both national and regional levels) are scoped separately. Pricing is confirmed after a discovery call where we assess category complexity and competitive density.

How does the output feed into an ongoing LLMO programme?

The ranked prompt library is the operational centre of an LLMO programme — the document that every content production sprint, every SEO signal-building initiative, and every PR placement strategy should reference. Content teams work from the high-opportunity tier as their near-term production brief. SEO teams use the competitor citation map to close specific signal gaps. PR teams use the prompt categorisation to understand which brand narratives are most relevant to AI-generated recommendations in their category. Most clients schedule a quarterly re-scoring session to track whether citation gaps are closing and to surface new prompts that have entered their category landscape as LLM behaviour continues to evolve.

/ Our team

The people behind the work.

Niraj Raut

SEO Expert

LinkedIn Website

Keshab Joshi

PPC Expert

LinkedIn Website

Hawrry Bhattarai

Google Ads Expert

Arogya Rijal

SaaS SEO Expert

Start here

The Brands That Win AI Citations in Competitive US Markets Will Be the Ones That Mapped Their Prompt Landscape First

AI citation advantages compound. The businesses earning consistent recommendations in your category today are building a lead that gets harder to close every quarter. Start with the intelligence. Get the full ranked prompt library for your US market category and build your LLMO programme on evidence rather than assumption.

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