GigaChat · LLM monitoring
Sber stack, corporate Russia—GigaChat
A CFO asks an assistant inside a banking workflow—not a random browser tab. GigaChat is Sber’s model family embedded across SberBusiness, SberBank Online, and adjacent surfaces. Western “AI visibility” dashboards rarely instrument that path, so brands miss decisions made inside Russian fintech apps.
What a finished report looks like
The demo highlights GigaChat’s row with quotes from GigaChat answers in the same run as Western models.
Carapelli
Mentions by model (demo run)
Highlight: GigaChat — 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 GigaChat
Add sibling models in the same check to see if the pattern is specific to GigaChat or repeats across the stack.
About this model
GigaChat targets corporate Russia, finance, and large retail—exactly the cohort that already lives inside Sber’s product graph.
Tight integration with banking and business UIs means recommendations can appear without ever opening a standalone chat website.
Why Russia & CIS matter here
If your buyers are in Russia’s regulated industries—banking, insurance, healthcare, retail—GigaChat is part of the advice layer inside Sber’s ecosystem. Western AI visibility vendors typically do not monitor GigaChat at all, so ChatGPT-only dashboards miss a channel where millions of users actually ask for recommendations.
How we measure visibility
No brand name in prompt text; scoring aligns with other Getllmspy models.
- GigaChat + YandexGPT + ChatGPT + Claude and more
- Share of voice, competitors, sentiment, quotes
- Repeatable reports after content or PR updates
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 GigaChat 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 GigaChat-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.
Western LLM visibility suites usually skip GigaChat—without a dedicated slice you optimize ChatGPT while buyers get advice inside Russian super-apps.