LLMO Prompt Intelligence | Ignited Nepal

You Don't Know Which Questions AI Is Answering About Your Category — This Fixes That

Most businesses in Nepal operating in competitive categories have no idea which prompts people are using when they ask ChatGPT, Gemini, Perplexity, or Claude about their market. They don't know which queries trigger citations, which competitors are being named, or which question formats drive the most AI-generated recommendations. This service mines 100+ category-specific prompts, tests every one of them across five major LLMs, scores each by citation opportunity, and delivers a ranked prompt library you can act on immediately.

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This is for you if

WHO THIS IS FOR

You are beginning LLMO work and don't know which queries to target. Your team understands that AI search is changing how customers discover products and services. You've heard about LLMO, GEO, and AEO. But when someone asks "where do we start?" the honest answer is that you have no systematic picture of what people are actually asking AI in your category. You need the map before you can plan the route.

You already have AEO or GEO work running and want to expand into LLMO. Your team has been optimising structured content for featured snippets and Google's AI Overviews. That work has value. But LLM citation behaviour is different from snippet selection, and the prompt patterns that trigger citations in conversational AI are not the same as the queries you've been targeting. You need a separate, dedicated prompt intelligence layer.

You are a marketing team in Nepal that needs AI-native prompt intelligence as a strategic input. You've been using keyword research to plan content for years. That discipline still matters. But keyword research was built for a search paradigm where users type fragmented terms into a box. LLM users ask full, conversational questions — and the intelligence you need to compete starts with understanding those questions at scale.

You are a brand that has noticed competitors being cited in AI responses and wants to understand why. You've run a few test prompts yourself and seen a competitor named. You don't know whether that's happening consistently, which prompts trigger it, or what signals that competitor holds that you don't. This service gives you that picture with precision.

What's broken

WHAT'S BROKEN

You are guessing at AI query terms with no systematic method.

Someone on your team has probably run a few prompts and noted whether your brand appeared. That is not intelligence — that is a sample of one on a given day with a given LLM. The actual prompt landscape for your category contains hundreds of variations across awareness queries, comparison queries, buying-intent queries, and brand-specific queries. Without a method to surface and test all of them, you are making strategic content decisions on fragmentary evidence.

You are targeting the wrong prompts.

Even when teams try to be systematic about LLMO, they tend to prioritise prompts that feel important rather than prompts where citation opportunity actually exists. A prompt might seem central to your category but generate responses where no external source is ever cited. Another prompt that looks peripheral might be one where LLMs consistently pull in third-party sources — which means it is a real citation opportunity. Without scoring, you cannot tell the difference.

You are missing the prompts where your competitors are already winning citations.

Your competitors are being recommended in AI responses right now. Some of those recommendations are happening in prompts you have never tested. The gap is not just about which brands are cited — it is about which specific query patterns trigger those citations consistently. You cannot close a gap you have not measured.

You have no systematic way to discover and prioritise LLMO opportunities.

LLMO is a new enough discipline that most teams are improvising their approach. There is no equivalent of a keyword planner for LLM prompts — no tool that tells you the "volume" or "difficulty" of a given prompt in the way SEO tools do for search queries. The intelligence has to be built through direct LLM probing, category analysis, and structured testing. Most teams do not have the time or the method to do that at scale.

What we engineer

WHAT WE DO

Prompt Mining Report

100+ category prompts generated from your seed terms, product or service type, competitor names, and problem statements, using structured LLM probing and category analysis methodology

Prompt Categorisation Map

every prompt organised by type: awareness queries, comparison queries, buying-intent queries, and brand-specific queries, with notes on what user intent each category represents

Citation Opportunity Scores

each prompt tested across five LLMs with documented results: who is being cited, how often, with what frequency, and an opportunity score indicating how achievable a citation is for your brand given your current signals

Competitor Citation Map

for each high-opportunity prompt, a documented picture of which competitors are being cited and what signals (content type, domain authority, review presence, structured data, publication patterns) appear to be driving those citations

Ranked Prompt Library

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

Implementation Brief

for each tier of the ranked library, a brief explaining what action to take: which prompts to target first, what content format is most likely to earn a citation, and which signals to build or strengthen

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What changes

WHAT CHANGES

Before
After
Before You stop guessing and start targeting.
After Every piece of content your team produces for LLMO purposes is tied to a specific, tested prompt with a known citation opportunity score. You know why you are writing it, which LLMs are most likely to cite it, and which competitors you are competing against for that citation. The guesswork is replaced by a clear target.
Before Your content investment goes to the highest-opportunity prompts first.
After Not all prompts are equal. Some represent genuine, achievable citation opportunities for your brand at its current level of authority and content depth. Others are dominated by sources you cannot realistically compete with in the short term. The ranked library lets you sequence your work so that early effort goes to prompts where you can earn citations quickly, building momentum before you tackle harder targets.
Before You understand your competitors' citation advantages and can address them directly.
After The competitor citation map tells you not just that a competitor is being cited, but why — which signals they hold that you currently don't. That is actionable. It converts a vague competitive threat into a specific gap you can close through content, structured data, review acquisition, or publication.
Before Your marketing and content teams speak the same language as the AI landscape.
After The prompt library becomes a shared reference for your team. SEO, content, PR, and digital advertising teams can all orient their work around the same ranked set of prompts rather than pulling in different keyword directions. That alignment has value beyond LLMO — it creates coherence across the way your brand addresses its market.
Common questions

FAQ

What is LLMO prompt intelligence?

LLMO prompt intelligence is the systematic discovery and scoring of the questions people use to ask large language models about a category, a problem, or a set of competitors — then ranking those questions by how achievable a citation is for a specific brand. It is the research foundation of any LLMO programme. Without it, teams are making content decisions without knowing which prompts they are actually competing for, which LLMs are most active in their category, or where their competitors hold citation advantages.

How is this different from keyword research?

Keyword research maps the terms people type into search engines — typically short, fragmented phrases optimised for index matching. LLMO prompt intelligence maps the full, conversational questions people ask LLMs — longer, more specific, often phrased as requests for a recommendation, comparison, or explanation. The two sets of intelligence overlap but are not interchangeable. An LLM citation is earned through different signals than a search ranking, and the query patterns that trigger citations are structured differently from the keywords that drive organic traffic. You need both, but they are separate inputs.

What makes a prompt high-opportunity?

A prompt scores as high-opportunity when it meets several conditions simultaneously: the LLM consistently draws on external sources when answering it (rather than generating a closed, self-contained response), the existing set of cited sources is not completely dominated by sources your brand cannot realistically compete with at its current authority level, and there is a clear signal pattern among the cited sources that your brand can match through content or structural improvements. A prompt that generates citations but where all citations go to major international publications with decades of domain authority would score lower for a Nepal-based brand — not because the prompt is unimportant, but because the path to citation is longer.

How long does the engagement take?

The standard LLMO Prompt Intelligence 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. If your category is large or highly competitive, or if you want the scope extended to include multiple geographic prompt variants, timeline and scope are discussed at briefing.

What does it cost?

Pricing for Nepal-based engagements starts at NPR 95,000 for a standard single-category prompt library covering 100+ prompts across five LLMs with full scoring and competitor mapping. Multi-category engagements and ongoing prompt monitoring programmes are scoped separately. Exact pricing is confirmed after a brief discovery call where we assess category complexity and competitive density.

How do I use the output?

The ranked prompt library is the operational starting point for your LLMO content programme. Your content team uses the high-opportunity prompt tier to plan the next round of content production — each prompt in that tier has an implementation brief attached that explains what to write and how to structure it. Your SEO and PR teams use the competitor citation map to identify signal gaps to close. Your broader marketing team uses the prompt categorisation to understand how AI is framing your category and where your brand currently sits in that framing. The library is a living document — we recommend revisiting it every quarter as LLM citation behaviour evolves.

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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Know Exactly Which Prompts to Target — Before You Write a Single Word

Most LLMO programmes stall because teams don't have a clear picture of the prompt landscape they're operating in. Start with the intelligence. Get the full ranked library, understand where your competitors are winning citations and why, and build your LLMO content programme on a foundation that's mapped rather than guessed.

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