01 · Measure
See exactly how you show up
Every AI assistant, every query in your campaign. Where you appear in the answer, how you're described, and who gets cited instead of you.
The Strategy Engine
Dashboards tell you where you stand. The Strategy Engine changes it.
HeyOtis finds the actions that grow your AI recommendation share, confirms they went live, and proves whether they worked - a campaign-led loop that compounds.
Why now
AI search created a new channel most teams can't manage
Dashboards without direction don't drive results.
For years, discovery meant ranking on a page of links. AI assistants changed the shape of the journey - buyers now ask what to choose and act on a single recommendation.
Your brand can be strong in search, strong in retail and well known in market, yet still be missing, misrepresented or outranked when an AI assistant explains the category. Measuring that is table stakes. Closing the gap - and proving you did - is the hard part.
The inputs
Five signal streams. One picture of how AI sees you.
The engine is only as good as its evidence - so it reads the evidence directly. Your answers, your AI traffic, your analytics, your own pages and your competitors' wins all flow into one model of the gap.
AI answer sampling. How five AI assistants answer, cite and rank you across the prompts that matter.
AI traffic. Which pages AI assistants visit - GPTBot, ClaudeBot, PerplexityBot and the rest - and the visitors they send you.
Site analytics. Sessions, conversions and landing pages, so lift ties to business outcomes.
Your website. What's on your pages - structured data, how fresh it is, and what's changed.
Competitive signals. Who wins the answer when you don't, and why.
Every signal, one model
Answers, AI traffic, analytics and your own pages - together
The engine is only as good as its evidence, so all five signal streams flow into one model of the gap.
The loop
One campaign-led loop, end to end
Most tools stop at Measure. The Strategy Engine closes the loop - confirming the work went live and proving it changed how AI recommends you.
02 · Diagnose
Find the reason, with evidence
The engine reads your pages and the answers side by side, and every finding traces to a fact you can click: the answer, the citation, the page, the date.
03 · Prioritise
Get the short list that matters
Ranked by impact and effort, scoped to a campaign, sized to get done. If we can't verify it, we don't recommend it.
04 · Verifywhat others skip
We watch the work land
The engine re-checks your site until the change is live. Nobody has to tell us. If it slips later, you'll know that too.
05 · Provewhat others skip
Measure the lift
Visibility, referrals and conversions, tied back to the move that earned them.
How deep it goes
Four levels the engine operates at
From telling you what's happening to doing the work for you.
01
Diagnostic
Here's what's happening
Where you stand across every AI assistant, and the signals explaining why.
02
Prescriptive
Here's what to do about it
A ranked, evidence-backed action plan - the actions with the biggest return first.
03
Predictive
Here's what's about to happen
Emerging prompts, drift and competitive risk surfaced before they cost you the answer.
04
Autonomous
We've already done it for you
The engine makes the fix, confirms it went live, then proves it moved your recommendation share.
The action plan
From signals to the actions that matter
Findings become a focused, ranked plan - every opportunity scored by impact and effort, tied to the metric it moves, and backed by the data behind it.
- Opportunities ranked by impact × effort
- Each tied to the metric it's measured by
- Every action backed by the evidence beneath it
Strategy Engine
Your action plan
3 opportunities to focus on this month
Sample1Highest priority
Own the “best for everyday” recommendation
High impactMedium effortMeasured by ChatGPT recommendation share
- Why this
- You're named in the answer but rarely first, and never as the cited source - AI assistants lean on a retailer page instead of yours.
- What to do
- 1.Publish a comparison page targeting the “best everyday” buying question.
- 2.Add Product and FAQ structured data so AI assistants can ground on you.
- 3.Reclaim the citation with first-party proof points and reviews.
What you'll see: Move from mentioned to first-named on the everyday-use prompts.
Based on 4 signals
- 2
Turn recognition into top placement
Measured by Top-3 presence · 3 signals
High - 3
Win back the retailer citation layer
Measured by Owned citation share · 2 signals
Medium
Composed by the strategy engine from the measured data above. The wording is generated; the evidence is not.
Real attribution
Three layers of proof
“It worked” shouldn't be a guess. HeyOtis reads your traffic and AI activity directly, so you can watch AI revisit the pages you changed, see the visitors it sends, and track your share moving - with the evidence attached.
The revisit
AI comes back to the page.
Within days of a change going live, AI assistants revisit the pages you updated - visible directly in your traffic data, because HeyOtis reads it.
The visit
AI sends the humans.
Referral sessions arrive from chatgpt.com and perplexity.ai onto the pages the move touched - real buyers, from real answers.
Referrals · 30-day window
AI referral sessions
Sample- chatgpt.com
- +128%
- perplexity.ai
- +64%
- gemini.google.com
- +41%
Sessions arriving from AI assistants onto the pages a change touched.
The lift
The metric moves.
Recommendation share is re-measured on the same prompts, before and after, with the evidence trail attached.
Attribution · 30-day window
ChatGPT recommendation share
SampleBefore1.4%
After4.9%
30-day window across tracked prompts, evidence trail attached
The compounding advantage
Every campaign makes the next one sharper
Proof isn't the end of the loop - it's the input to the next one. Every outcome, proven or disproven, reweights what the engine recommends next.
- Proven actions raise the weighting of similar ones
- Disproven ones get deprioritised - honestly
- Every cycle starts smarter than the last
The compounding loop
Recommendation share, by campaign cycle
- Cycle 1: 1.2% recommendation share - Baseline measured
- Cycle 2: 2.1% recommendation share - Comparison page proven
- Cycle 3: 3.4% recommendation share - Citation moves reweighted
- Cycle 4: 4.9% recommendation share - Schema moves prioritised
Built on evidence
It won't recommend what it can't prove
The cheapest thing to write is a confident-sounding recommendation. The most expensive mistake is a confident-sounding wrong one. So the engine is built to refuse it.
Capabilities check
It only recommends an action it can verify got done. If a signal source isn't connected, it surfaces the gap instead of guessing.
Maturity gating
New signals are tested privately first, and only shown to you once the data proves they're reliable.
Validator-gated reporting
Every number traces back to source. The engine refuses to invent a statistic to make a point.
Platform + strategists
The engine finds the opportunities. Our strategists help you get them done.
HeyOtis pairs the Strategy Engine with hands-on GEO strategy. The platform does the analysis, the recommendations and the proof; our team helps you turn them into work that lands.
Monitored across
ChatGPT
Claude
Gemini
Perplexity
Google AI Overviews
Where this is heading
The operating system for brands in the age of AI search
The loop is the start. As AI assistants become how brands are discovered, served and transacted with, HeyOtis is building toward the full stack - see, serve, test and act on how AI represents you.
Get started
See the Strategy Engine
on your brand.
See the exact actions the Strategy Engine would prioritise for your brand - and how the loop proves the lift. 20 minutes.