25+
technical & entity signals scored per audit
The Answer Engine Optimization platform
Measure how AI describes your brand — across every model that matters.
Stand Scope is the measurement layer for the post-search era. We probe every frontier model with the prompts your buyers actually run, capture the raw answers, and surface the technical and entity-layer fixes that change the output.
Plans from $79 — or try a $49 Spot Check first.
Trusted measurement
A reproducible methodology for tracking AI visibility — built on captured prompts, scored signals, and engine-by-engine evidence. No black boxes.
4
frontier engines probed every run
5
composite scores per brand, 0–100
∞
audit history retained per workspace
Platform
One platform for everything AI says about you.
Audits, monitoring, and reporting in a single workspace. Built so a marketing team and an engineering team can use the same numbers.
Workspace
Purpose-built, reproducible, engine-agnostic.
Every audit is a snapshot of how AI sees your brand at a moment in time. Stack them across weeks and you have a defensible curve. Run them across competitors and you have a category benchmark. The platform handles the orchestration — your team handles the strategy.
Audits
Probe ChatGPT, Claude, Perplexity, and Gemini with five prompt classes. Capture every answer. Score every signal.
Monitoring
Schedule re-scans. Get drift alerts when an engine flips on you.
Reporting
Branded HTML and JSON export, a print-ready view (Cmd+P → Save as PDF), and a public-share URL for stakeholders.
How a probe runs
A measurement pipeline, not a checklist.
Every score traces back to a captured artifact: a prompt, an answer, a header check, an entity record. You can audit the auditor.
01 / Capture
Site + entity ingest
Crawl the site. Pull headers, schema, sitemap, llms.txt, OpenGraph, robots policy. Resolve the brand against Wikidata and Wikipedia.
02 / Probe
Multi-model prompting
Run five prompt classes against ChatGPT, Claude, Perplexity, and Gemini. Persist every raw response with timestamps, model versions, and citations.
03 / Score
Composite scoring
Five composite scores (Overall · Clarity · Differentiation · AI citability · Entity authority) computed from the captured signals. Weights documented.
04 / Surface
Prioritised remediation
Top three fixes ranked by expected score impact. Each with current copy, rewrite, and the reason it moves the number.
05 / Track
Longitudinal comparison
Re-scan on demand or schedule. Diff every signal. Detect engine drift before it costs you pipeline.
06 / Export
Reportable output
Branded HTML for stakeholders, structured JSON for engineering, a print-ready view for Save-as-PDF (Cmd+P), and a public link for clients. Same data, four surfaces.
Live view
Watch a probe move through the pipeline.
Same six stages as above — comparing a static list against a live one.
live view · looping
>
crawling site, schema, sitemap, llms.txt…
Multi-model probe
Talking to every engine at once.
A second take on the same idea — this one fans a probe out to four models and counts what comes back.
>
signals captured: 0
Full lifecycle
One run, start to finish.
A third take — the complete trip, six stages, one continuous run. Nothing skipped.
stand scope · running
headers
schema
sitemap
llms.txt
chatgpt
claude
perplexity
gemini
0
overall score
#1 · +12
#2 · +8
#3 · +5
Δ +6 vs last scan
HTML
JSON
PDF
>
starting the run — resolving site + entity…
The front door
$49 one-time AI visibility probe.
One prompt, run live against Perplexity, with the full answer, cited sources, and a brand-mention score emailed to you in under a minute.
Run yours — $49
no login required · report in your inbox
Models
Every frontier model, side-by-side.
No vendor preference. No single-model bias. Buyers move between engines — your measurement should too.
OpenAI
ChatGPT
Largest consumer surface area. Recognition here predicts general-market awareness.
Anthropic
Claude
Preferred by technical and operator audiences. A higher bar — recognition signals real authority.
Perplexity
Perplexity
Citation-native. Surfaces the exact sources AI is pulling from when discussing your brand.
Google
Gemini
Tied to Google's index and AI Overviews. A leading indicator for Search Generative Experience visibility.
Trust
Built on verifiable evidence.
Every claim in a Stand Scope report is backed by a captured artifact. We retain the raw payloads. You can re-derive the score.
Auditable
Reproducible. Inspectable. Versioned.
Model versions, prompt text, and timestamps are captured on every probe. Site signals are stored as the raw response, not a summary. If a vendor disputes a score, you can re-derive it from the artifact.
Reproducible
Same prompt, same model, same week — same answer captured for both your team and ours.
Versioned
Every audit is timestamped and immutable. Comparison reports diff signal-by-signal.
Portable
Export to JSON. Pipe into your warehouse. Own your AEO data.
Who it's for
Teams that treat AEO as infrastructure.
Founders defining a category. Marketing teams owning AI visibility as a metric. Agencies running it at scale.
Founders & GTM
Define the category
Verify what AI says when a buyer asks for "the best X." Track the gap to incumbents. Close it deliberately.
In-house marketing
Own AEO as a metric
Five composite scores, monthly trend lines, evidence on every claim. Walk into the QBR with numbers, not narrative.
Agencies & consultancies
Operate at scale
Run audits across up to 10 monitored brands per workspace. Schedule re-scans. Branded HTML/JSON export with a print-ready view. Read-only audit history API on Agency tier.
Scope
What we don't do.
If a buyer asks for any of the below, we are not the right vendor. The platform is the product.
× Bespoke websites or web design
× Outbound / SDR / lead-gen builds
× CRM implementation, RevOps consulting, pipeline systems
× “AI strategy” decks
× SEO services — link building, content production, ranking work
Recent teardowns
Real brands, real model output.
This is the same probe-and-score pipeline every audit runs — applied publicly.
northlake.io
Overall 58 / 100
Named correctly by 3 of 4 models, but never cited as a source — no llms.txt, thin entity graph.
fernwood-analytics.com
Overall 71 / 100
Strong Wikidata presence carries entity authority; category description drifts across models.
brightloop.ai
Overall 44 / 100
Confused with a same-named competitor by Perplexity in 4 of 5 prompt runs.
Illustrative examples — fictitious brands, real scoring mechanism.
Get started
See exactly what AI says about your brand in under five minutes.
Run your first audit, get five composite scores, raw answers from four frontier engines (ChatGPT, Claude, Perplexity, Gemini), technical findings, and a prioritised remediation plan.