LLM Optimization (LLMO) Platform for AI Visibility & Share of Model

LLM Optimization (LLMO) is the practice of improving how often, how accurately, and how favorably your company appears in answers generated by AI models such as ChatGPT, Gemini, Claude, and Perplexity.

It has three distinct objectives, and confusing them is the most common reason LLMO programs stall:

• Frequency — does the model name you at all when a buyer asks a question your product answers? A brand that appears in 8% of evaluation answers has a frequency problem, not a messaging problem.
• Accuracy — when the model does name you, is the description correct? Wrong category, stale pricing, a discontinued product, or a competitor's feature attributed to you are all LLMO defects.
• Favorability — is the mention a recommendation, a caveat, or a footnote? "X is an option, though most teams choose Y" counts as a mention and loses the deal.

In practice, LLMO work is source work. Models generate answers from what other people have written about you — analyst notes, editorial coverage, review platforms, community threads, documentation — plus whatever the model already learned during training. Publishing another page on your own site is the weakest lever available; in our observed set, vendor-owned pages account for well under 1% of the sources models cite.

Read the full definition with examples →

Share of Model measures how frequently your brand is recommended, mentioned, or positioned by AI models across the questions your buyers ask. It is the LLMO equivalent of share of voice — but scored against generated answers instead of impressions.

Worked example. You track 40 buyer questions across 4 models, sampling each question 3 times per week: 40 × 4 × 3 = 480 answers. Your brand is named in 62 of them. Share of Model = 62 ÷ 480 = 12.9%. A competitor named in 178 of the same 480 answers sits at 37.1%. The gap is not a branding opinion — it is 116 answers where a buyer heard their name and not yours.

The metric is only meaningful when broken out. Split it by buyer stage, by model, and by competitor, because a single blended number routinely hides a brand that is well known in category questions and invisible in comparison questions.

Share of Model, in full →

SEO and LLMO share tactics but not objectives. SEO wins a position in a list of links a person then chooses from. LLMO wins inclusion in a recommendation the model has already made on the buyer's behalf.

The practical consequence: you can hold the top organic result for a query and still be missing from the AI answer to the same question, because the model summarized three review-site comparisons and an analyst post instead of your page.

The full SEO vs LLMO breakdown →

A defensible measurement has five parts. Skip any one and the number drifts.

1. Fix the prompt set. Real buyer questions in buyer language, tagged by stage — not keywords with a question mark appended. 30–60 prompts is enough for most B2B categories.
2. Fix the model set. Sample the same models every cycle. Different models return materially different brand sets for the same question, so a change in coverage looks exactly like a change in performance.
3. Sample repeatedly. Model output is stochastic. Single-run checks are noise: across our scans, mention rates for the same brand vary by tens of points between runs of the same prompt. Run each prompt several times and average.
4. Classify each answer. Named or not; if named, recommended, compared, or caveated; and which sources the model cited. Frequency without favorability is half a metric.
5. Hold the window constant. Compare week to week with the same prompt set, or your trend line is measuring your own changes to the instrument.

Distribution of observed AI citations across buyer stages in Monroya's own scan data. Evaluation-stage questions generate the largest share of cited sources — which is also where most B2B brands are weakest.

Models do not have an opinion about your company. They reproduce the consensus of the text they can read, weighted toward sources that look independent and repeated. Four patterns show up consistently in the answers we capture:

• Third-party sources dominate. Analyst content, editorial coverage, and review platforms carry the majority of citations. Vendor-owned pages are cited rarely, and usually only for pricing or documentation specifics.
• Consistency beats volume. A brand described with the same category language across many independent sources is easier for a model to place than one with more coverage but conflicting descriptions.
• Comparison formats get lifted. Content that already compares options — tables, "X vs Y", ranked shortlists — is disproportionately reused, because it matches the shape of the answer the model needs to produce.
• Community text is a tiebreaker. Forum and discussion threads act as evidence of real usage, and they surface most often in decision-stage answers where a model is hedging between two credible options.

Source-type mix across all captured citations. Read it as a budget: this is where the answer actually comes from.

Most-cited domains

1. 01medium.com5,454
2. 02gartner.com4,113
3. 03linkedin.com3,646
4. 04g2.com3,036
5. 05forrester.com2,200
6. 06capterra.com1,474
7. 07youtube.com793
8. 08trustpilot.com749
9. 09reddit.com730
10. 10clutch.co459

Buyers do not ask one question. They ask a sequence, and the model answers each one from a different pool of sources. Optimizing for the first question and ignoring the rest is the most expensive mistake in LLMO, because the later questions are the ones attached to budget.

Top source types cited at each stage, from Monroya's observation set. The mix shifts as the buyer moves — which is why one content plan cannot serve all three.

A retention layer sits after the sale: churn-risk questions ("alternatives to X", "how to migrate off X") are answered by models too, and they are usually unmonitored.

They are layers, not rivals. AEO makes a page extractable, GEO gets it retrieved, LLMO decides whether the model would name you even if nothing were retrieved at all.

LLMO vs GEO in detail →

1. Identify recommendation gaps. Find the questions where competitors are named and you are not. Rank them by stage and deal proximity, not by volume.
2. Identify competitor advantages. For each gap, read why the competitor is there — which source the model cited, and what claim it supported.
3. Find influential sources. Build the list of domains the models actually cite in your category. That list, not your content calendar, is the target.
4. Fix narrative gaps. Correct wrong category placement, stale specs, and missing differentiators wherever the model is reading them — review profiles, directories, reference entries, your own docs.
5. Build supporting content. Comparison-shaped, evidence-backed material that an independent source can cite, and that a model can lift without hedging.
6. Monitor changes. Re-run the same prompt set on a fixed cadence and attribute movement to the work. Without a stable baseline you cannot tell a win from run-to-run variance.

The step-by-step version →

Monroya runs your buyer questions against ChatGPT, Claude, Gemini, and Perplexity on a schedule, samples each prompt multiple times, and records what the models said and which sources they cited. Every answer is classified by buyer stage, source type, and sentiment, so Share of Model can be read by stage and by competitor rather than as one blended number.

• Share of Model by stage, model, and competitor, tracked over time.
• Per-prompt answer history, so you can see exactly when an answer changed and which source changed with it.
• Cited-source analysis: which domains decide your category, and which of them never mention you.
• Prioritized recommendations tied to the specific gap and source that produced them.