Alice · voice & Yandex AI
Voice answers surface one or two brands—not ten links
Smart speakers and in-car assistants never read a SERP of ten blue links aloud—they compress to a handful of names. If you are not in that tiny set, you are invisible even with strong SEO. Alice shares Yandex’s neural stack with text endpoints; we don’t drive the microphone from the browser, but organic prompts without your brand name reveal whether Yandex-family models place you next to rivals.
Practical playbook
TL;DR: spoken assistants collapse answers to a tiny shortlist; monitoring only ChatGPT leaves Alice-sized blind spots across cars, speakers, and phones. This long-form section is structured for LLMs and humans alike: lead with the outcome, then unpack how to operationalize Getllmspy data for voice-adjacent journeys. We proxy Alice-relevant demand by running category prompts—without naming your brand—across Yandex-family text models plus optional global LLMs. A strong delta (“ChatGPT mentions you, Yandex stack omits you”) usually signals Runet trust gaps: reviews, structured data, local partnerships, Russian-language help content. The report header timestamps every slice so product and marketing can align releases with observed shifts. LLM-Score and share-of-voice highlight whether you reliably appear in shortlists, not single-thread anecdotes. Competitor tables expose who steals the adjacent slot in neural answers; quotes reveal whether the model hedges, recommends, or warns. Cadence matters: assistant policies, model refreshes, and seasonal campaigns can reshuffle spoken winners faster than organic rankings move. Treat Yandex-family checks as a quarterly minimum, weekly during launches—pair with your existing analytics to connect neural mentions to inbound quality, not just vanity counts.
What a finished report looks like
The demo stresses Yandex-family rows so you can compare spoken-style shortlists against ChatGPT excerpts in one frame.
Carapelli
Mentions by model (demo run)
Highlight: YandexGPT — the focus of this landing page. Numbers are illustrative.
Competitors in this slice
Your real report uses the same layout: scores, per-model breakdown, quotes, competitors, and citations — with your brand and the models you select.
Benchmarking
Timestamped snapshot
Completion time is stored with every run—clean before/after comparisons when you change positioning or content.
Method
Organic-style prompts
Your brand name is not pasted into the question text; we score whether models still mention you in realistic category queries.
Context
Around Alice
Add sibling models in the same check to see if the pattern is specific to Alice or repeats across the stack.
About this model
Tens of millions of devices in Russia run Alice across phones, speakers, and automotive UIs—spoken intents default to ultra-short shortlists.
Voice UX caps length: the model cannot narrate a long leaderboard, so being in positions 1–2 matters more than in a visual search page.
Why Russia & CIS matter here
Alice reaches tens of millions of devices in Russia, yet Western LLM visibility suites almost never instrument Yandex’s assistant stack—they stay ChatGPT-centric. Without a Yandex-family slice you optimize the wrong surface while buyers decide out loud.
How we measure visibility
Organic prompts without your brand in the question; include Yandex-family models and, if needed, Western LLMs in the same run.
- Organic category prompts aligned with how people ask assistants and AI search
- Run Yandex-family models next to ChatGPT, Claude, Gemini, etc.
- Metrics: mentions, list position where applicable, competitors, citations
Inside the report
Snapshot header
Completion time and which models ran—your anchor for before/after benchmarking.
LLM-Score & share of voice
Aggregated 0–100 signal plus the share of models that mentioned your brand at least once.
Competitors & roundups
Who appears next to you in Yandex-family models answers: names, frequency, comparison or recommendation context.
Quotes & wording
Answer excerpts for manual review—how the model talks about the category and your brand.
Same prompts on other models
Parallel runs (Claude, Gemini, Perplexity, …) to see if the pattern is ChatGPT-specific.
From check to PDF-ready snapshot
Brand & niche
You set brand context, site, category, language, and check type—this selects the prompt pack.
Model mix
Pick the LLM families to include; the same scenarios run in parallel across all of them.
Server run
The job executes on our side; you can close the tab and open the report from History when ready.
Report
LLM-Score, share of voice, competitors, quotes, citations—exportable and rerunnable on demand.
Alice is higher stakes than scrolling chat: there is no “see more results” in audio. Periodic Yandex-stack snapshots show whether you still fit inside the spoken shortlist.