Know if AI engines recommend your brand — and whether the change is real.
Buyers now start their research inside AI assistants. Every other tool reports a single-run number that is statistically often noise. Orem samples each prompt many times, reports a 95% confidence interval, and tells you plainly whether your visibility really moved.
Illustrative — representative brand types, shown as design placeholders, not endorsements.
Repeat AI runs return the exact same brand recommendations
SparkToro
Of cited domains churn within six months — monitoring must be continuous
Profound
Share-of-voice gaps below this are statistically a tie, not a lead
arXiv:2603.08924
Of search traffic expected to shift to generative AI by 2028
Gartner
AI search is a black box — and single-run dashboards make it worse.
Ask an AI engine the same question twice and you get different brands and different sources. Tools that report one run as a score turn that randomness into false alarms: a “drop” that was noise, a “lead” that was a coin flip. Trust erodes, and teams churn once the novelty fades.
Orem is built on the opposite premise — that these metrics are random variables, so the only honest answer is a distribution and a confidence interval.
If it isn’t outside the interval, it isn’t real.
The full AI-visibility stack
Answer engine optimization
FAQs & standard questions — are you the answer engines give?
AI engine optimization
Brand mentions & citations inside AI answers
Generative engine optimization
Share of voice across generative results vs. competitors
A four-stage pipeline, built for rigor
The sampling orchestrator and statistics engine are the IP — everything else routes through them, so every number on every screen carries a confidence interval.
Sampling engine
Every tracked prompt is run N times per engine per refresh — not once. Independent samples are the only way to see the underlying distribution instead of one random draw.
Citation parser
Each answer is parsed for brand and competitor mentions — cited and uncited — plus every source domain and a sentiment read.
Statistics engine
Bootstrap resampling turns those runs into a 95% confidence interval and a Real gain / Real drop / Within noise verdict. The IP that makes the number trustworthy.
Recommender
Measured source-gaps become engine-specific off-page actions, joined to the proven on-page playbook — every action mapped to a gap and re-verified against the noise floor.
Everything a team needs to win AI search
Real-vs-noise overview
One weekly answer to “am I winning or losing, and is it real?” — a hero verdict, CI trend band, and a digest of only the prompt–engine cells that actually moved.
Prompt tracking
Every buyer question you track, grouped into AEO / AIEO / GEO, with per-engine mention & citation rates, confidence intervals and week-over-week sparklines.
Live sampling
Run the full pipeline on demand against any engine — watch each sample land, then get CI-backed results and AI-generated actions in seconds.
Share of voice
Your share of tracked-brand mentions vs. competitors, with overlap shading so a “lead” that is really a statistical tie is shown as such — never as a fake win.
Citations & sourcing
Which domains AI answers actually cite for your prompts, each engine’s sourcing bias, and exactly where competitors are cited while you’re absent.
Action playbook
Off-page moves generated from this week’s source gaps plus the proven on-page tactics — citations, quotations, statistics, question-shaped sections, freshness.
Usage & margin
Live COGS and gross-margin meters per account, with tier guardrails that keep every workspace inside its unit-economics envelope.
Multi-client workspaces + API
One workspace per brand for agencies managing many clients, plus a Bearer-key REST API to pull CI metrics into your own reporting.
Build hundreds of AI-citable pages from one template.
Point a keyword pattern at your modifier lists and Orem generates the whole matrix — the same head-term × modifiers method top brands use to own long-tail search. Every page is templated with the proven answer-engine tactics (a direct answer up top, question-shaped H2s, statistics, cited sources), so it’s built to be quoted by ChatGPT, Perplexity and Google AI Overviews — not just ranked on Google.
- Pattern + modifiers → a page matrix (5 services × 20 cities = 100 pages).
- AI-drafts a GEO-optimized template you can edit, with your own data columns.
- Export every page as CSV, ship them, then measure the AI-visibility lift in the same platform.
one template · one dataset · hundreds of answer-engine pages
Built for the teams feeling the shift
Marketing agencies
Manage every client brand in its own workspace, prove AI-search wins with statistics instead of vanity scores, and pull metrics into client reports via the API.
In-house SEO & growth
Get a defensible weekly read on whether your brand shows up in AI answers, and a prioritized playbook of what to ship next — no noise to chase.
PR & communications
Track how AI engines describe your brand, catch sentiment shifts that clear the noise floor, and target the community and publisher sources engines actually cite.
Rigor is the differentiator
Monitoring AI visibility is becoming table stakes. Being able to trust the number is not.
| Capability | Orem | Typical single-run tools |
|---|---|---|
| Multi-run sampling (N per prompt) | Typically single-run | |
| 95% confidence interval on every metric | ||
| Real-change-vs-noise verdict | ||
| Statistical tie detection on share of voice | ||
| Engine-specific off-page source gaps | Partial | |
| On-page playbook from published GEO research | Partial | |
| Per-account unit-economics guardrails |
Comparison reflects the single-run point-estimate approach documented across the category. Feature sets change — verify against current vendor docs.
How teams turn AI visibility into growth
Buyers are moving from search boxes to AI answers — the next wave of demand is decided inside ChatGPT, Perplexity and Google AI Overviews. Here’s how different teams put that shift to work.
Marketing agencies
Turn AI-search visibility into a billable service.
Report share of voice with 95% confidence intervals clients can trust, prove real wins vs. noise, and deliver defensible KPIs across every client workspace.
Defensible client KPIsRead the playbookCMOs & marketing leaders
See what your outsourced team actually delivered.
An honest, real-vs-noise scorecard of how your agency or in-house team moved the needle in AI answers — accountability instead of vanity dashboards.
Vendor accountabilityRead the playbookSolar & clean energy
Win homeowners who ask AI for an installer.
Get recommended when buyers ask ChatGPT “best solar installer near me,” find the source gaps keeping you out of the answer, and close them.
More quote requestsHome services
Be the name AI suggests for the job.
HVAC, plumbing, roofing — track your presence per engine when local buyers ask AI for help, and turn those recommendations into booked jobs.
More booked jobsRead the playbookSoftware & SaaS
Land in the AI shortlist for “best tool.”
As buyers shift from Google to ChatGPT, track mention and citation rate per engine and scale AI-driven pipeline for your category’s highest-intent queries.
Scale AI-driven pipelineAutomotive & dealers
Own the answer when shoppers research.
Be the brand AI names for models, financing, and dealerships — and monitor how each engine describes you versus competitors, so you can steer the narrative.
Share of AI recommendationsIllustrative use cases showing how each team applies Orem — not client testimonials.
Advice that actually transfers to AI answers
Citations, quotations & statistics win
GEO optimization can lift generative-answer visibility up to 40% — the top tactics are adding citations, attributed quotations and concrete statistics.
KDD 2024 · arXiv:2311.09735Keyword stuffing does not transfer
Tactics that game classic search don’t move generative answers. Orem deliberately never recommends them.
GEO research consensusEngine sourcing biases are real
Wikipedia dominates ChatGPT’s citations; Reddit dominates Perplexity’s. The off-page playbook is mapped to each engine’s bias.
Profound · 680M-citation datasetThe metrics are random variables
AI answers are non-deterministic — the same prompt returns different brands and sources on repeat runs, so sampling and confidence intervals are mandatory.
arXiv:2603.08924, 2604.07585Start free, scale by rigor
Multi-run sampling is a real per-account cost, so tiers scale on prompts, engines and samples — with weekly refresh as the default and daily reserved for the top tier.
| Tier | Price | Prompts | Engines | Runs / prompt | Refresh |
|---|---|---|---|---|---|
| Free | $0 | 15 | 2 | N=5 | Weekly |
| Starter | $99/mo | 75 | 3 | N=8 | Weekly |
| GrowthPOPULAR | $199/mo | 150 | 3 | N=8 | Weekly |
| Pro | $399/mo | 350 | 3 | N=8 | Weekly |
| Business | $699/mo | 500 | 4 | N=8 | Weekly |
| Enterprise | Custom | 1,000+ | 8 | N=20 | Daily |
Questions, answered
What are AEO, AIEO and GEO?+
Three service pillars. AEO (Answer Engine Optimization) tracks whether you are the answer engines give to FAQ-style questions. AIEO (AI Engine Optimization) tracks brand mentions and citations inside AI answers. GEO (Generative Engine Optimization) tracks your share of voice across generative results versus competitors. Orem measures all three, each with a confidence interval.
Why a confidence interval instead of a single score?+
Because a single run is one random draw from a distribution. Identical brand recommendations appear in fewer than 1 in 100 repeat runs, and citation-share gaps under ~5–7 points are statistically indistinguishable from a tie. Orem samples each prompt many times and reports a 95% interval plus a real-vs-noise verdict, so you never chase a change that was just randomness.
Which AI engines does Orem track?+
ChatGPT, Perplexity and Google AI Overviews as the core three, with Gemini and Claude available too. A provider-abstraction layer routes each engine to its data source, so engines can be added or swapped without changing the measurement pipeline.
How many times is each prompt sampled?+
It depends on your tier (N=5 on Free up to N=20 on Enterprise). Precision improves with the square root of N, so Orem uses adaptive sampling — more runs only where the interval is still wide — to get tight intervals without runaway cost.
How is this different from Profound, AthenaHQ or Peec?+
Those tools ship a single-run point-estimate dashboard. Orem’s wedge is statistical honesty: multi-run sampling, a confidence interval on every metric, an explicit real-change-vs-noise verdict, and tie detection on share of voice — so you can tell a real win from random noise.
Where does the action playbook come from?+
Off-page actions are generated from your actual measured source gaps — domains where competitors are cited and you’re absent — mapped to each engine’s known sourcing bias. On-page actions come from the proven GEO feature set (citations, quotations, statistics, question-shaped sections, freshness). Every action’s effect is re-verified against the noise floor after you ship it.
Can agencies manage multiple clients?+
Yes. Each brand lives in its own workspace with its own competitors, prompts, region and engine set. Switch between clients from the workspace picker, and pull any client’s CI metrics into your own reporting through the public API.
Is there an API?+
Yes — create a Bearer key in settings and call /api/v1/workspaces and /api/v1/workspaces/:id/metrics to get weekly mention/citation rates with 95% CIs, verdicts and share of voice per engine.
See where your brand stands in AI search
Tell us about your brand and we’ll reach out with a walkthrough and a free visibility snapshot.
Prefer to talk? Call us at +1 (855) 302-8711 — our AI assistant answers 24/7 and can connect you to a specialist.
Stop guessing whether your AI-search wins are real.
Track your brand across every major answer engine with confidence intervals and honest verdicts — free to start, no card required.