Engine 1 of 6
ChatGPT
- Position
- 4th of 7
- Share of voice
- 14%
Named, never recommended.
What AI says when you're not in the room.
Vidrys asks six answer engines what your buyers ask, and measures whether you were recommended.
Tracked prompt
Which warehouse robotics vendor should we shortlist?
Engines probed
- ChatGPT
- Perplexity
- Gemini
- Copilot
- AI Overviews
- Claude
ChatGPT · verbatim
For high-throughput fulfilment centres, most teams evaluate Locus Robotics and 6 River Systems first — both have large deployed fleets. Dexory is worth a look for inventory scanning. Smaller vendors such as Acme Robotics also operate in this space.
Named in
2 of 6
Position
4th
Endorsement
None
Illustrative: across six answer engines, Acme Robotics is named in 2 of 6 answers, placed fourth, and recommended by none.
The blind spot
Six assistants answer the same buyer's question. You see none of them.
Tracked prompt
Which warehouse robotics vendor should we shortlist?
Engines probed
- ChatGPT
- Perplexity
- Gemini
- Copilot
- AI Overviews
- Claude
ChatGPT · verbatim
For high-throughput fulfilment centres, most teams evaluate Locus Robotics and 6 River Systems first — both have large deployed fleets. Dexory is worth a look for inventory scanning. Smaller vendors such as Acme Robotics also operate in this space.
Named in
2 of 6
Position
4th
Endorsement
None
Illustrative: across six answer engines, Acme Robotics is named in 2 of 6 answers, placed fourth, and recommended by none.
Named in passing. Not on the shortlist.
Mentioned is not recommended. We read every answer the way a buyer does, and mark the sentence that cost you the shortlist.
How it works
Four jobs, one loop.
Generative Engine Optimization, run as a loop: measure, explain, fix, verify.
01 · Measure
Measure
Every prompt. Every engine you run. Every sweep.
Sweep · 6 engines · 5 prompts
every 2 days
ChPeGeCoAIClbest warehouse robotics for 3PLsnamednot namednot namednamednot namednamedAcme vs Northwind pricingnamednot namednamednot namednot namednamedSOC 2 compliant AMR vendorsnamednot namednamednamednot namednamedwarehouse robotics vendor shortlistnot namednot namednamednot namednot namednamedAMR fleet for cold storagenot namednot namednamednot namednot namednamed
Share of Voice
47.2%
MarAprMayJunJulAug
02 · Explain
Explain
Why you lost the answer, sentence by sentence.
Acme RoboticsNamed, then argued against
Acme Robotics is capable, though I couldn't verify their compliance posture and pricing isn't published, so it's hard to recommend for a regulated buyer without a sales call.
- Share of Voice
- 47%
- Endorsement
- 0.00
- Position
- 3.0
Reasoning cardSCORE_VERSION 2.1
Why the endorsement was zero
Named third, then advised against on two grounds. Under multiplicative gating the position and prominence terms never apply — this answer contributes nothing, exactly as if you had not appeared.
When the evidence is too thin to support a reason, the card says insufficient evidence instead of inventing one.
03 · Fix
Fix
A drafted page, pushed to your CMS.
Content queue
1 of 3 gaps
Trust page: SOC 2 report date and subprocessor list
- Gap detectedAug 04done
- Draft writtenAug 05done
- Published to WordPressAug 06done
- Re-probedAug 08done
- VerifiedAug 08active
Publishes to
- WordPress
- Webflow
- Shopify
- Ghost
04 · Verify
Verify
Re-probed. Version-locked. Provable.
After the fix
vs. previous 30 days
Share of Voice
47.2%+5.2
Endorsement
0.71+0.06
Avg. position
2.6-0.4
Citation share
18.9%-1.1
Who gets recommended
Aug- Northwind62%
- Acme Robotics47%
- Kelvin Labs38%
- Orbit CRM24%
- Fernpath17%
Coverage
One brand, six answers.
Second on Perplexity and absent from AI Overviews are not one number averaged. They are two jobs.
Illustrative. Figures are from a fictional account.
Clients
Who we measure for.
Every name here is a customer. No logo walls.
Methodology
Most tools in this category are dead reckoning. We take bearings.
A brand named first in an answer that then advises against it scores zero — the same as one never named at all.
Endorsement · R
SCORE_VERSION 2.1
- Strong1.0
- Recommended0.8
- Neutral0.5
- Reference only0.2
- Discouraged0.0
- Strong
- Named as the one to choose.
- Recommended
- Named among the options worth considering.
- Neutral
- Listed. No view expressed either way.
- Reference only
- Cited as a source, not offered as a choice.
- Discouraged
- Named, and advised against.
Position and prominence multiply this figure. They never rescue it.
For teams
From ₹999/mo
Every plan gets the whole product. Tiers change how much you measure.
See pricingFor agencies
Seats, quoted.
One client per seat, from ten seats. Your clients’ logins are free.
For agenciesLatest
From the blog
About
Counsel, not commentary.
Vidrys — after Vidura, who saw the war coming and said so. From the root vid, to know: the same root as evidence. Pronounced VID-riss.
We build recommendation intelligence: whether, how strongly, and why AI systems recommend you — and the practice of changing that.
Talk to the teamWhat Vidrys does not measure
- The trust factor — how authoritative the citing sources are — is not built yet. Its weight is renormalised out, and the product says so.
- Private or logged-in assistant sessions. We probe what a new buyer sees.
- A causal link from a published fix to a score. We report the baseline, the current window, and whether the difference clears the interval.
- AI Overviews and Copilot have no public API. They are read from captured AI-search results, and every figure says so.
We name our own gaps first. It is cheaper to publish a limitation than to defend a number that quietly assumed it away.
FAQ
Questions worth asking.
What the product measures, what it does not, and how the numbers are produced.
Whether AI assistants recommend you, not whether they mention you. Every recorded answer is judged on a five-level endorsement scale — from named as the choice, down to named and advised against — and that judgment multiplies the score before position or prominence are considered. A brand named first in an answer that then argues against it scores zero, the same as one that was never named.
A mentions report counts appearances. Vidrys scores the quality of the appearance, asks each prompt again on every sweep so the number rests on a distribution, version-stamps every score so a formula change cannot rewrite your history, and carries each finding through to a drafted fix and a re-measurement. The difference shows up when a mention count rises and the shortlist has not changed.
That variability is the reason single-screenshot tools mislead. Every prompt is re-probed on each sweep, and scores, win rates and trends are computed across all the answers in the window you look at: “recommended in 8 of 12 answers” rather than one answer treated as a fact. Each score also carries a confidence interval, and a week-over-week move that does not clear it is reported as inconclusive rather than as a change.
The record attached to a single answer: which competitor was recommended, which sources the model actually leaned on, and the specific claim your pages failed to support. It is what the drafted fix is written against. When the evidence in an answer is too thin to support a reason, the card says so instead of inventing one.
ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews and Claude. How many run on each sweep is the main thing that separates the plans — one on Solo, two on Track, three on Grow, five on Scale and Custom — because every additional engine multiplies the number of answers analyzed, and an all-engines default would quietly multiply what you are billed for. From Grow up you can add engines your plan does not already run. The per-plan sets are listed in full on the pricing page.
6 more questionsFewer questions
The score has a trust factor — how authoritative the citing sources are — that is specified but not yet built. Rather than approximate it, the remaining weights are renormalised and the omission is stated in the product. We also do not measure private or logged-in assistant sessions, and we do not claim a causal link between a published fix and a score move: the product reports the baseline window, the current window, and whether the difference clears the confidence interval.
SEO optimises for a ranked list of links. The inputs that decide an AI recommendation are different: whether a crawler can read the page at all, whether a claim is stated somewhere a model can cite, entity clarity, and which third-party sources the model already trusts. Vidrys audits the first, detects the second, and reports the third from the citations in your own answers.
Yes, once you approve them. Drafts are written against your own crawled pages so they read like your site rather than generic filler, and they publish to WordPress, Webflow, Shopify or Ghost. Nothing is published without approval.
Yes, on Track and above, through the Vidrys MCP server. Claude Code, Codex and Cursor connect with an API key. claude.ai and ChatGPT connect by signing in to Vidrys, as soon as sign-in is enabled. It returns the same data the dashboard shows, nothing more. Every plan with MCP also includes safe writes: adding tracked prompts, acting on the content queue, and drafting from your monthly content credits.
We do not publish a timeline, because we do not have the cohort to support one and the honest answer depends on your cadence, your engines and what you ship. What the product does commit to is telling you when a move is real: each sweep re-probes the same prompts through the same scoring pipeline, and a difference that falls inside the confidence interval is reported as inconclusive rather than as progress.
Sign up, add your brand, your competitors and the prompts you want tracked. No card is required to start, and each new workspace is reviewed before we activate it. If you would rather see it against your own brand first, book a demo.
A position you can defend
Start with your own prompts.
Your first sweep comes back with the answers six engines gave, and the sentences that cost you the ones you lost.
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