Comparison
llmeknow vs. AI visibility tools.
Every AI visibility tool measures how often your brand appears in AI answers. The difference is what they simulate: your real market asking real questions, or a keyword query with your name in it.
The methodology gap
Monitoring a keyword is not the same as simulating a market.
Most tools send queries that contain your brand name, then count how often and how prominently the models respond. This tells you something about recall. It does not tell you what AI says when your buyers ask questions they actually ask, without prompting with your name.
llmeknow models your market from the ground up. You define the audience segments that matter. The platform builds synthetic personas within each segment, generates the questions those personas would ask AI, and runs them at scale across every major model, so the AI answers as it would to that person. The result is visibility as your market actually experiences it, not a keyword check.
There is a second gap: honesty about the numbers. AI answers change between responses, so a rank from a single query is one sample dressed up as a constant.llmeknow runs each question repeatedly and reports rates across those draws, with the raw responses kept for inspection. Read the full methodology.
Feature comparison
| Capability | llmeknow | Peec.ai | Brand24 / Chatbeat | Otterly.ai | Menra |
|---|---|---|---|---|---|
| AI models tracked | 8 provider families | 7–11 (3 per plan) | 4–9 (tier-dependent) | 4 base + paid add-ons | 9 (base 3 + add-ons) |
| Audience / market simulationAI receives queries as if from your target segment | ✓Synthetic personas: the AI receives the query in the context of that segment | – | – | – | ◐Prompt-prefix framing only ("as a startup CTO…") |
| Market generation engineBuild audience segments down to the finest level of detail | ✓Generate a full market with AI in one step, or craft every segment and persona yourself | – | – | – | – |
| Wave schedulingScheduled repeat runs that show whether your interventions worked | ✓Daily, weekly, monthly, quarterly | –Continuous daily monitoring only | –Continuous monitoring | –Continuous monitoring | –Continuous monitoring |
| Composite visibility metric | ✓Proprietary composite score, with the underlying metrics and raw responses inspectable | ◐Visibility score (frequency-based) | ◐Brand Score (0–100) + Median Position | ◐Share of Voice + position | ◐Unified visibility score |
| Search IntelligenceCaptures the web searches AI runs while answering | ✓Captures actual search queries from Gemini, Claude, and Grok in citation mode | ◐Query Fanouts: searches AI engines initiate before answering (different scope) | – | ◐Agent Analytics tracks AI bot crawls of your site, not the searches models run | – |
| Topic modellingFinds the recurring themes across thousands of AI answers | ✓Themes tracked wave over wave | – | – | – | – |
| Citation QCValidates whether cited sources support the claim | ✓Background service checks cited URLs actually support AI claims | –Tracks which pages are cited; does not validate claim support | –Identifies cited sources; no claim validation | – | – |
| Raw response explorerBrowse full AI-generated text per run | ✓ | – | – | – | – |
| Deep-dive reportsAI investigates a specific finding using web search | ✓ | – | – | – | – |
| In-dashboard AI assistant | In devLemme, our own agent: it creates markets and campaigns and runs waves from inside the dashboard | – | – | – | – |
| Custom tracking categoriesBrands, attributes, pain points, services, or anything you define | ✓ | ◐Brands + source domains | ◐Brands + mentions (social listening origin) | ◐Brands and keywords | ◐Brands + citations |
| PDF / branded export | ✓PDF and slide export with custom org branding | – | – | ✓PDF reports (launched June 2026) | – |
| Content and site recommendationsTurns findings into work your team can act on | ✓Search Intelligence reports produce content prompts for your content team and technical tickets for site improvements | – | – | ◐GEO recommendations and URL audits | ◐AEO Content Scoring audits existing pages |
| MCP serverQuery visibility data from Claude, Cursor, etc. | In dev | ✓All paid plans, read-only | – | ✓Standard and above | – |
✓ Yes · ◐ Partial · In dev · – No. Based on publicly available documentation as of July 2026. Competitor features may have changed.
The segment dimension
AI answers differently depending on who is asking. We measure that.
The same subject can be described differently to different people.
Ask ChatGPT about a CRM for a budget-conscious buyer, and it gives you one answer. Ask about the same CRM for an enterprise procurement lead, and the framing, the caveats, and the competitors it names may all differ. Neither answer is wrong. They are the different stories AI tells different parts of your market.
Synthetic personas, not prompt prefixes.
llmeknow simulates the market asking questions to AI, not the AI role-playing as a persona. Each query reaches the model in the context of the synthetic persona, so the model responds as it would to that specific type of person. Other tools that offer persona framing prepend a label to the query; the AI reads it as editorial context, not as who is asking. The outputs differ in ways that matter to research quality.
Segment deltas alongside the baseline.
Every wave produces a baseline (no persona) and a result per segment. You see where AI's account of your subject diverges by audience: which segments over-index, which under-index, and how the characterisation shifts between them. Bring your existing segmentation framework and the platform builds around it.
Search Intelligence
See the web searches AI runs before it answers.
When Gemini, Claude, and Grok answer with cited sources, they search the web first. Those searches are not always about your subject directly. A model may query a competitor, a category term, or a specific claim it is trying to verify. llmeknow captures those queries, tied to the prompt that triggered them, so you see what information each model seeks out when a member of your market asks about your space.
This is relevant to GEO strategy. If AI consistently searches for something your site does not address well, your content may never enter the citation pool regardless of how prominent you are in static training data. Search Intelligence makes that gap visible.
Search Intelligence works when models search the web at answer time (citation mode), currently for Gemini, Claude, and Grok. Coverage expands as more models expose their search steps.
Honest note
We don't write your content.
Some tools generate finished pages or FAQs for you. llmeknow stops one step short of that on purpose: Search Intelligence reports hand your team content prompts and technical site tickets, grounded in what AI actually searches for and fails to find. The writing stays with you, because a measurement tool should not grade its own homework.
Agent and MCP in development.
Peec.ai and Otterly.ai already expose MCP servers so you can query visibility data from Claude or Cursor. We are building both: Lemme, our own in-dashboard agent, and an MCP server. Neither has shipped yet, so the table marks them honestly as in development.
30-day trial, card required to start.
llmeknow is open for self-serve sign-up with a 30-day trial. We charge R1 to verify your card and credit it back to your wallet. Explore the product, top up wallet credits when you want to run waves, then subscribe to Basic or Pro to keep access after the trial.
Common questions
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