LLM optimization tools
The category is young enough that 'AI visibility platform', 'GEO tool', and 'LLMO software' are often the same product. Judge them on capabilities, not labels.
Updated: August 2026
The three kinds of tool
| Type | What it does | Good for | Limit |
|---|---|---|---|
| Monitors | Run prompts, report mention rate and alerts | Knowing you have a problem | No causal link to sources or actions |
| Citation analyzers | Track which domains models cite in your category | Building the target source list | Usually stop short of stage and sentiment |
| Optimization platforms | Measure, attribute to sources, prioritize actions, re-measure | Running an actual program | Only useful if the prompt set reflects real buyer language |
Five capabilities that actually differentiate
- Multi-model coverage. ChatGPT, Claude, Gemini, and Perplexity at minimum, sampled on the same schedule.
- Multi-run sampling. Several runs per prompt, averaged. Without it you are reporting noise.
- Buyer-stage classification. Discovery, evaluation, decision, retention — a blended score hides the gap that costs deals.
- Cited-source capture. Not just whether you were named, but what the model read to decide.
- Action attribution. A path from a specific gap to a specific fix, and evidence of whether the fix moved the answer.
Questions to ask on a demo
- How many times is each prompt run per cycle, and is the result averaged?
- Can I see the exact answer text and cited sources behind any data point?
- Can Share of Model be split by buyer stage and by competitor?
- What happens when a model releases a new version mid-quarter?
- Is sentiment tracked, or is a caveated mention counted as a win?
- What does the product tell me to do next, and why that action?
Where Monroya fits
Monroya is an LLM optimization platform: it runs your buyer questions against ChatGPT, Claude, Gemini, and Perplexity on a schedule with multi-run sampling, classifies every answer by buyer stage, source type, and sentiment, records the cited sources, and turns the gaps into prioritized actions you can re-measure.
Comparisons with other tools in the category: alternatives overview, vs Profound, vs Otterly, vs AthenaHQ.
Frequently asked questions
- What are LLM optimization tools?
- Tools that measure how AI models answer buyer questions about your category, and help you change those answers. At minimum they run a prompt set against several models on a schedule, record which brands are named and which sources are cited, and track the trend.
- What should an LLM optimization tool measure?
- Share of Model across a fixed prompt set and model set, broken out by buyer stage, competitor, and sentiment; the sources cited in each answer; and change over time with enough runs per prompt that variance is averaged out rather than reported as movement.
- How many models should a tool sample?
- At least ChatGPT, Claude, Gemini, and Perplexity. The same question returns materially different brand sets across them, so single-model tracking can look stable while your position on the model your buyers use is falling.
- Why does multi-run sampling matter?
- Model output is stochastic. The same prompt can name different vendors minutes apart. A tool that checks each prompt once reports variance as performance, which makes week-to-week comparisons meaningless.
- What separates measurement tools from optimization tools?
- Whether the product connects a gap to its cause and to an action. Reporting that a competitor is named more often is a metric; identifying the cited source that produced the mention and prioritizing what to do about it is optimization.
- Do you need a dedicated tool, or can this be done manually?
- Manual spot checks are fine for a first look and useless as a baseline: the run count required to average out variance across several models, several dozen prompts, and a weekly cadence is not practical by hand.
Related reading
- LLM Optimization (LLMO) platform— The category page: what LLMO is, what Share of Model measures, how to move it
- What is LLM optimization?— The plain definition, with examples of what it looks like in practice
- What is Share of Model?— The measurement: definition, formula, and a worked example
- LLM optimization vs SEO— Rankings vs recommendations — where the two disciplines diverge
- LLMO vs GEO— Parametric knowledge vs live retrieval, and why you need both
- How to improve Share of Model— Six steps, in order, from gap to measured change