'Which grocery app is cheapest/fastest in my city?' — AI answers this thousands of times a day.
CiteRank measures who AI engines recommend in grocery & quick commerce — across ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, Microsoft Copilot, xAI Grok and Meta AI, in both grounded and ungrounded modes — and fixes why it isn't you.
Vertical benches currently run 8 engines; full tracking covers all 8.
The grocery & quick commerce leaderboard, measured.
Top-5 brand mention rate, first-mention rate and Share of Voice — straight from the citerank-bench scoring tables, recomputed weekly.
Benchmark for Grocery & Quick Commerce runs with our first customer in this vertical. The leaderboard below is an illustrative sample built from the blueprint's brand registry — numbers are placeholders, not measurements.
Illustrative sample — benchmark not yet completed.
Real grocery & quick commerce prompts, eight engines, one question: did you get cited? Gartner predicts a 25% shift from search to AI agents by 2026 (Gartner).
Three failure modes specific to retail.
Marketplace capture: AI cites Amazon/Flipkart/Myntra instead of your D2C site.
'Is it safe to buy from {brand}.com?' — AI answers from forum scraps, not your trust signals.
Category-page SEO collapses inside AI Overviews; PLP traffic moves to the answer panel.
How a grocery & quick commerce benchmark gets built.
URL, competitors, prompt seed list. 5 minutes.
25 prompts × 8 engines × 2 modes, 10–20 replays each.
Schema, llms.txt, entity hygiene, content briefs — shipped.
Weekly re-runs, deltas with 95% confidence bands.
What a 90-day citation delta looks like.
Illustrative pattern based on our methodology — first customer results publish Q4 2026.
Cited in 1 of 25 high-intent grocery & quick commerce prompts across 8 engines (4%).
Cited in 9 of the same 25 prompts (36%) — schema, llms.txt and entity fixes shipped over 3 weekly re-runs.
Grocery & Quick Commerce Citation Intelligence
This section contains Verified Category Data observed during current Grocery & Quick Commerce monitoring runs. These patterns define how AI engines rank your competitors and why certain brands are cited over others.
Top Category Prompts
- "best grocery delivery app in Bengaluru"
- "Blinkit vs Zepto vs Instamart — which is cheaper?"
- "fastest grocery delivery in Whitefield right now"
- "no-membership grocery app with free delivery"
Engine Citation Skew
Dominant Source Domains
Grocery & Quick Commerce case study publishes after the first 90-day window.
We don't ship anonymous success stories. Every case study card on this page becomes a real logo, real prompts and real deltas after the customer's 90-day window closes.
Coming in the Grocery & Quick Commerce visibility report.
Fee hallucinations: what AI gets wrong about delivery pricing
Notify me when publishedWinning the 'cheapest' prompt without racing to the bottom
Notify me when publishedGrocery & Quick Commerce questions, answered.
How is CiteRank different from marketplace listings?+
Those surfaces optimise for their own funnel. CiteRank measures how often ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, Microsoft Copilot, xAI Grok and Meta AI actually name your brand for grocery & quick commerce buyer prompts, then fixes the schema, entity and content gaps so the engines have something to cite besides the aggregator.
Which segments / cities do you cover for this vertical?+
We start with your highest-intent buyer prompts (250 tracked prompts on Platform, 500 on Platform + GEO) — segmented by city, persona and price band as relevant — and expand the prompt graph weekly. Coverage scope is set during the 5-minute onboarding.
How fast to first new citation?+
First measurable citation lift is typically 3–5 weeks after the first fix pass (schema, llms.txt, entity, content briefs). Re-runs are weekly with 95% confidence bands so you can attribute deltas to specific fixes.
Is our data sent to LLMs?+
Only the public buyer prompts we run on your behalf reach the engines — exactly what a prospective customer would type. We do not send your CRM, customer data, or internal documents to any LLM.
Start with one snapshot. Scale into weekly monitoring.
25 prompts × 8 engines × 2 modes. Brand-mention report and gap analysis.
Weekly re-runs, deltas with confidence bands, alerting on rank/citation changes.
Monitor + shipped fixes: schema, llms.txt, entity, content briefs and re-measurement.
