AI visibility

A visibility project tracks one brand across the AI models. Create one under AI Visibility → New project, enter the brand name and website, and add the competitors you want to benchmark against. CrunchJunkie uses the brand and competitor names to detect mentions in AI responses, so spell them exactly as they appear in the market — including common variations if the brand is known by more than one name. Each project is independent, which makes it easy to run visibility for several clients side by side. Once the project exists, you add prompts and choose which models to scan, then CrunchJunkie takes over the daily monitoring.

Prompts are the questions CrunchJunkie asks the AI models on your behalf. Add them under the project's Prompts tab and tag each one by intent: Discovery prompts are unbranded category questions ("best marketing reporting tools for agencies"), Brand prompts name your client directly ("is Lumière any good?"), and Competitor prompts are comparisons or alternatives ("Lumière vs Velora"). A good starting set mixes all three categories — perhaps fifteen to thirty prompts that mirror how real buyers search. CrunchJunkie runs every prompt against every enabled model on each scan and records whether your brand was mentioned, where it ranked in the answer, how it was described, and which sources the model cited.

CrunchJunkie can track eight AI surfaces: ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, Google AI Mode, Grok, and DeepSeek. Enable the ones that matter for your client's audience under the project's Models settings — you don't have to run all eight. Each enabled model is scanned automatically every day. Each surface maps to a specific, current model version: ChatGPT is tracked on GPT-5.5 with web search, Gemini and Claude with web search, Perplexity on Sonar Pro, Grok with web search, and Google AI Overviews and AI Mode through a search-results provider. Seven of the eight are web-search-grounded; DeepSeek is the exception, since its API has no built-in web search. The exact model each surface calls is shown on the Models settings page and evolves as providers ship new versions. Some models require specific setup. Google AI Overviews, for example, is tracked through a search-results provider rather than a chat API. The Models settings page tells you exactly what each one needs and shows the current status of every connection at a glance.

Every scan's cost comes down to three dials: how many prompts run, how many engines each prompt hits, and how many passes (runs) per prompt. Project Settings gives you one-click scan modes so you don't have to tune them by hand. Lite runs two efficient engines at a single pass — a cheap regular pulse. Balanced (the recommended default, and what every new brand starts on) runs the four engines that matter most at two passes. Thorough runs all eight engines at three passes for the highest confidence. Custom is whatever you set yourself — pick any engines and passes, and the mode label switches to Custom automatically. Settings shows a live estimate as you change the dials — "each scan runs ≈ N checks" — worked out as prompts × passes × engines, with premium engines (ChatGPT, Claude, Perplexity, Grok) weighted 3× because they cost about three times as much to run. That estimate is the same whether you're on Managed AI (where it counts against your monthly allowance) or your own keys (where the provider bills you directly), so you always know the cost of a configuration before you save it. More passes and more engines mean more accurate, more stable numbers; fewer of each keep cost down. Start on Balanced, then raise or lower it once you see how the brand's numbers move.

Four headline metrics tell you how you're doing. Visibility % is how often your brand appears across all tracked prompts. Share of Voice is your slice of total brand mentions versus your tracked competitors — the clearest signal of who owns the conversation. Sentiment scores how positively each mention describes you, and Average Position records where you land when you do appear. Drill into any metric by model, by prompt category, or over time. The Responses view lets you filter to All, Mentioned, Not mentioned, or Mention gap (prompts where a competitor appears and you don't) — the fastest way to find concrete opportunities. The Sources view ranks the domains AI cites most, and the Insights view turns all of this into prioritised, plain-language actions.

A single overall visibility number hides where you actually win and lose. CrunchJunkie classifies every prompt on two axes so you can slice it. Buyer intent — Commercial (comparing/buying), Informational (learning) or Navigational (looking for a specific brand) — is auto-predicted for each prompt and stays editable. Branded vs non-branded is derived deterministically from your brand name and its aliases: a branded prompt names you ("is Lumière any good?"), a non-branded one is a pure category question ("best CRM for agencies?"). Both classifications are rule-based, not an opaque model guess, so they're instant, free and 100% reproducible. On the Prompts page these become one-click slice filters. Each slice chip shows that slice's visibility with its sample size (for example "Non-branded 34% · 340 runs"), so a slice built on three runs never reads like one built on three hundred — the number always carries its confidence. The distinction that matters most: high branded visibility just means AI repeats what you already told it, while non-branded visibility is the real prize — being recommended when the buyer hasn't named you yet. Slicing by Commercial intent shows exactly the purchase-moment queries where a recommendation converts.

Not every AI answer is built the same way. On the Responses view you can filter by the features an engine actually used to produce an answer. "Web search" marks answers where the model retrieved live web sources before replying (grounded) rather than answering from its training memory — and the ungrounded answers are exactly where hallucinated or outdated brand claims tend to hide, so filtering to them is a fast way to audit risk. "Shopping" marks answers where the engine returned product recommendations. These flags are derived only from signal the provider genuinely exposes — the sources a model retrieved, and the products it recommended — never inferred or faked. We deliberately don't claim features our provider APIs don't report (such as ads or maps blocks): honest, verifiable coverage matters more than a longer feature list. Each response also shows its feature tags inline, so you can see at a glance how a given answer was assembled.

When an AI model answers a prompt it often cites the pages it drew on, and CrunchJunkie records every one. The Sources view ranks the domains cited most across all your prompts and models — the sites that are actually shaping what AI says about your category. The URLs view goes a level deeper, listing the individual pages cited and how often each appears, so you can see exactly which articles, listicles or product pages the models lean on. Gap analysis turns this into a to-do list. It surfaces the domains where AI cites a competitor but never you — your clearest content gaps. Each gap is a concrete opportunity: earn a mention on that source (a review site, a directory, an industry roundup) and you start appearing where buyers are already being pointed. Because all three views are built from real scan citations, they update every time a scan runs. Both the Sources and URLs tables carry a brand-mention filter with Any / All / None logic, so you can slice the citation set precisely. "Any of" shows sources cited in runs that mentioned at least one selected brand; "All of" shows only sources cited in runs that named several brands together — the fastest way to find a page that talks about you and a competitor at once; and "None" shows sources cited where no tracked brand was mentioned — untapped places not yet talking about anyone you follow.

Before an AI can mention a brand, its crawler has to be allowed to read the site. The Crawlability tool checks a domain's robots.txt against every major AI crawler — GPTBot and OAI-SearchBot (ChatGPT), ClaudeBot and Claude-SearchBot, PerplexityBot, Google-Extended and Googlebot, and more — and tells you whether each is Blocked, Partial or Allowed. Expand any crawler to see exactly why (which user-agent rule matched) and how to change it. The tool distinguishes training crawlers (blocking them keeps your content out of model datasets, often intentional) from search and answer crawlers (blocking these directly hurts your AI visibility), so you can make the right call per bot. The domain pre-fills from the client's saved website. Crawl insights is the companion view: once you forward your server logs, it shows how often each AI crawler actually visits the site — turning "we allow GPTBot" into "GPTBot fetched 40 pages last week". Together, Crawlability tells you who *can* read the site and Crawl insights tells you who *is*.

Larger projects benefit from a little organisation. The Brands view manages the exact names CrunchJunkie matches as mentions — your client plus its competitors — including spelling variations and aliases, so a mention is never missed or miscounted. Tags let you label prompts (by theme, funnel stage, or client priority) and then filter every metric by tag, which is the fastest way to answer questions like "how visible are we on bottom-of-funnel comparison prompts?". Project Settings is where you control the brand name and website, the competitor list, which models run, runs-per-prompt, and the scan schedule. Scheduling is off, daily, or weekly — and you pick the exact time (and, for weekly, the day) it runs, in your own timezone, so scans land when you want them. Changes here apply from the next scan onward.

Already tracking AI visibility in Peec AI? You can import a Peec project straight into CrunchJunkie instead of running native scans for that client. Under Data → Integrations, open Connect on the Peec AI card, choose the client, and paste a project-scoped Peec API key (created in Peec under Account → API Keys). CrunchJunkie verifies the key, then imports the project's visibility, share of voice, sentiment, average position, prompts and cited sources. Once imported, the data populates the exact same AI Visibility screens and report widgets as native scans — Overview, Prompts, Sources, Gap analysis and the report builder all work unchanged. The project is marked as Peec-sourced so the origin of the numbers is always clear, and re-syncing pulls the latest figures. Note that Peec's REST API is currently part of their Enterprise/beta tier, so the key needs API access enabled on their side.

Peec-imported data and CrunchJunkie's own (native) scans live side by side on the same client without clobbering each other. Every data point is tagged with its origin — "peec" or "native" — and the two are stored under different model names (Peec uses friendly labels like "ChatGPT" and "Perplexity"; native scans use the specific model versions they call, e.g. ChatGPT on GPT-5.5 with web search, or Perplexity Sonar Pro). Because of that they never overwrite one another: running a native scan adds native data alongside the imported Peec data rather than replacing it, and a Peec re-sync only refreshes its own imported rows. When a client has both, the AI Visibility overview shows a source switch — All / Native scans / Peec AI (imported) — so you can view either source on its own or together. One thing to keep in mind: in the combined "All" view the same assistant can appear twice (once from Peec, once from a native scan), so for a single client it's best to treat one source as your source of truth and use the switch when you want to compare. A Peec-imported project won't start native scanning on its own — it has no native prompts and its scan schedule stays off until you add prompts and choose to run them.

You can run scans two ways. Bring-your-own-keys lets you connect your own API keys for each model; CrunchJunkie runs the scans through them and you pay only the providers' usage rates, with no markup. Keys are encrypted at rest and used solely for your scans. This is the most cost-effective option if you already have provider accounts. Managed AI is the no-setup alternative: CrunchJunkie runs the scans on our own infrastructure, with no keys to manage. It is metered by check (one check = one prompt run). Model choice is free — you pick a monthly plan with an included check allowance, from €25/mo (Lite, 550 checks) up to €399/mo (Scale, 11,000 checks), and pay €0.05/check beyond it (all excl. VAT). Efficient models count 1× per check; premium models (ChatGPT, Claude, Perplexity, Grok) count 3×. You can mix Managed and your own keys, turn individual models on or off at any time, switch per model, and set a spend cap. See the pricing page for current Managed AI details.

Before setting up a full project, anyone can try a quick version at /ai-visibility-check on the marketing site — no account, no signup. Enter a brand (and, optionally, up to three of your own prompts and an email for the fuller write-up), and CrunchJunkie runs a small, real scan on the spot: it queries a single web-grounded AI model a handful of times and reports how often the brand was mentioned as a Visibility %, with the exact sample size ("n = … runs") and a margin of error (± standard error), plus any real sources cited. It's deliberately a small sample on one model, and the page says so — it's an honest snapshot, not a fabricated score, and if a run or two fails the number is recalculated on what actually completed. It's a good way to show a prospect what AI is saying about them in under a minute; the full product tracks continuously across all eight models with many more prompts, competitors, sources and trends over time. Use it as a lead-in, then create a project to go deep.

If you open ChatGPT (or Gemini, Perplexity) on your phone, run one of your prompts, and compare it to a CrunchJunkie scan, the answers usually won't match exactly — and that's expected. AI answers are non-deterministic and personalised, so a single manual check and a controlled scan measure different things. Five reasons they diverge: • Different model. The consumer ChatGPT/Gemini/Perplexity apps use whatever default model and built-in web search your account gets. CrunchJunkie scans a defined list of specific model versions via their APIs (e.g. ChatGPT on GPT-5.5 with web search, Perplexity Sonar, Gemini, Google AI Overview). Different model, different answer. • Personalisation. Your app carries memory, custom instructions, account history and your location — all of which bias the answer (e.g. toward agencies near you). Scans run clean and neutral, with no personal memory, against the market you configure, so results are comparable across clients and over time. • Live browsing. When the app browses the web, it pulls fresh results that change minute to minute. A scan captures a controlled snapshot. • Sampling. The same prompt returns different answers on repeat runs, because models sample. That's why CrunchJunkie runs each prompt several times and aggregates — one manual run is a single noisy sample. • Time. Models and the web index change constantly; a scan from last week and a check today will differ. The takeaway: a manual spot-check is one personalised, noisy data point. CrunchJunkie's value is the controlled, repeatable, multi-run measurement that averages out that noise — so you can track the trend and compare brands fairly, rather than reacting to a single screenshot.