Polco wins on Gemini. Not Zencity.
And on Claude. And by 4 citations overall.
20 buyer-intent queries across ChatGPT, Claude & Gemini. Zencity is cited on 31 of 60 responses (52%). Polco is cited on 35. Zencity dominates the listening and public safety trust lanes but loses every representative-survey, benchmarking, and council-defensibility question to Polco NCS/NLES.
The findings below come from Xtrusio, an AI visibility audit system built specifically for B2B buyer-intent testing. Every citation was verified by running 20 real prospect queries across three generative AI platforms.
Findings reflect how ChatGPT, Claude, and Gemini position Zencity against direct competitors in the community engagement and public safety trust category.
Zencity is treated as a two-lane vendor by AI: real-time listening and police trust. Every other lane belongs to Polco.
Across 60 responses, Polco is cited 35 times to Zencity’s 31 — and Polco wins outright on the seven questions where methodological credentials matter: representative surveys, cross-department satisfaction tracking, benchmarking against peer cities, defensible budget recommendations, and council confidence in policy decisions. These are the exact use cases Zencity’s Ask module and Elucd-acquired technology are supposed to serve. AI systems don’t know it.
Platform Scorecard
Zencity citation rate across AI platforms — and how it stacks against Polco
AI Visibility Leaderboard
Who owns the community-engagement conversation — total citations across all platforms
AI Positioning Audit
20 buyer-intent queries — click any row to see the exact question asked
Each query was written from the perspective of a real decision-maker researching community engagement, resident sentiment, and public safety trust tools for a US local government. These three personas represent the buyers whose AI search results determine whether Zencity gets discovered.
| # | Query Topic | Cluster | Claude | ChatGPT | Gemini |
|---|---|---|---|---|---|
| 1 | Monitor residents across social/news/forums | Listen | ✗ | ✓ | ✓ |
Exact question asked across all AI platforms: “How can a city communications director monitor what residents are actually saying about the city across social media, local news, and neighborhood forums without hiring a full media monitoring team?” | |||||
| 2 | Reach underrepresented voices | Communicate | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What tools help municipal communications teams measure whether their messages are reaching underrepresented parts of the community and not just the loudest voices?” | |||||
| 3 | 24/7 sentiment for small PIO | Listen | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “Which platforms let a small city PIO office track resident sentiment 24/7 across dozens of channels without manually checking each one?” | |||||
| 4 | Prove outreach shifted sentiment | Communicate | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “What software helps a city communications team prove to the mayor that their outreach campaigns are actually changing how residents feel about a policy?” | |||||
| 5 | Pre-crisis detection | Listen | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “How can a city communications director identify community concerns before they become public relations crises on social media?” | |||||
| 6 | AI analysis of 311/comments | Listen | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “What AI tools are local governments using to analyze thousands of resident comments, tweets, and 311 complaints and turn them into insights the city manager can act on?” | |||||
| 7 | Close trust gap through data | Communicate | ✗ | ✓ | ✓ |
Exact question asked across all AI platforms: “Which platforms help city communications teams close the trust gap between city hall and residents through data?” | |||||
| 8 | Defensible budget recommendations | Ask | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What tools help assistant city managers use resident feedback data to make defensible budget recommendations to city council?” | |||||
| 9 | Pre-budget resident priorities | Ask | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “How can a city measure resident priorities before drafting the annual budget so that spending actually reflects what the community wants?” | |||||
| 10 | Statistically representative surveys | Ask | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “Which platforms give local governments statistically representative resident survey results instead of just self-selected online feedback?” | |||||
| 11 | Cross-department satisfaction tracking | Ask | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “What software helps city performance management teams track resident satisfaction across every department in city hall?” | |||||
| 12 | Benchmarking trust vs peer cities | Ask | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “How can a mid-sized US city benchmark its community trust and satisfaction scores against comparable cities?” | |||||
| 13 | Council confidence vs vocal minority | Ask | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Which tools help city councils and county commissioners feel confident that their policy decisions reflect real community needs and not just vocal advocacy groups?” | |||||
| 14 | Integrated 311+survey+social dashboard | Listen | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “What platforms integrate 311 service request data, resident surveys, and social media sentiment into one performance dashboard for local government leadership?” | |||||
| 15 | Police community trust over time | Public Safety | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “How can a police department measure community trust and sentiment about its officers over time using data instead of anecdotes?” | |||||
| 16 | Police social listening | Listen | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “What tools help police chiefs listen to what residents are actually saying about public safety across social media and neighborhood forums?” | |||||
| 17 | Post-incident trust surveys | Public Safety | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “Which platforms are local law enforcement agencies using to run community trust surveys and measure changes after critical incidents?” | |||||
| 18 | Small PD transparency dashboards | Public Safety | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “How can a small city police department demonstrate transparency and accountability to residents using published community sentiment data?” | |||||
| 19 | City + PD joint engagement | Public Safety | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “What community engagement software is used by cities and police departments together to align public safety strategy with resident priorities?” | |||||
| 20 | Controversial topic sentiment | Listen | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “Which AI platforms help local governments analyze resident sentiment about controversial topics like data centers, housing, or homelessness?” | |||||
| TOTAL | 10/20 (50%) | 16/20 (80%) | 5/20 (25%) | ||
The Gemini Blackout
Where Zencity loses 55 percentage points vs ChatGPT — and where Polco steps in
Zencity is cited on 80% of ChatGPT queries and just 25% on Gemini. That’s a 55-point collapse on a single platform. Fifteen of the twenty buyer questions surface Zencity on ChatGPT but not on Gemini. The pattern is remarkably clean: Gemini treats Zencity as a specialist for real-time listening (Q1, Q14, Q20) and outreach-impact measurement (Q4, Q7) — and effectively invisible everywhere else, including the entire police vertical Zencity has spent years and an acquisition (Elucd) building.
“How can a police department measure community trust and sentiment about its officers over time using data instead of anecdotes?”
“Which platforms are local law enforcement agencies using to run community trust surveys and measure changes after critical incidents?”
“What AI tools are local governments using to analyze thousands of resident comments, tweets, and 311 complaints and turn them into insights the city manager can act on?”
Zencity’s content exists. Its co-branded Public Safety Confidence Index with the National Policing Institute exists. Its Elucd acquisition and representative-survey capability exists. ChatGPT knows all of it. Gemini knows almost none of it. The visibility gap here is not an awareness problem — Zencity is a 600-government, $94M-funded company that AI systems clearly know exists. It is a category-framing and training-data-exposure problem, and it is fixable.
AI Topic Authority Map
Query heatmap — product line × platform
| Topic | AI Leader | Zencity Status |
|---|---|---|
| Real-time community listening (multi-channel) | Zencity | 3 of 3 platforms |
| Pre-crisis sentiment detection | Zencity | 2 of 3 platforms |
| Police community trust measurement | Zencity | 2 of 3 (Gemini absent) |
| Post-incident police trust surveys | Zencity | 2 of 3 (Gemini absent) |
| Statistically representative surveys | Polco (NCS) | Claude only (1/3) |
| Benchmarking against peer cities | Polco (NCS) | INVISIBLE (0/3) |
| Defensible budget recommendations | Polco / Balancing Act | INVISIBLE (0/3) |
| Council confidence in policy decisions | Polco / PublicInput | INVISIBLE (0/3) |
| Reaching underrepresented voices | PublicInput | INVISIBLE (0/3) |
| Integrated 311+survey+sentiment dashboard | Zencity | 3 of 3 platforms |
7 queries
7 queries
3 queries
3 queries
▹ Zencity for Police is fully visible on ChatGPT and Claude but completely absent from Gemini — the entire law enforcement vertical needs a Gemini-specific content play.
Methodology
How we conducted this Xtrusio AEO/GEO Audit
This research is based on Xtrusio’s proprietary AI visibility analysis framework.
Recommendations
Prioritized actions to close the Gemini blackout and contest the Polco lead
- Publish a public-facing methodology page explaining the Elucd sampling frame, weighting logic, and margin-of-error math — the exact rigor content that anchors Polco NCS in AI training data
- Name the specific statistical certifications and academic partnerships behind Zencity Ask in customer case studies (currently these are buried below the product-marketing layer)
- Add a Zencity Ask vs Polco NCS comparison page to zencity.io — addresses the head-to-head lookup pattern Gemini and Claude are answering with Polco by default
- Publish 8–10 police customer stories on high-authority external domains (Route Fifty, Police1, Governing) explicitly naming Zencity Public Safety Confidence Index — this is what closes ChatGPT / Claude data gaps in Gemini
- Co-author 2–3 pieces with the National Policing Institute referencing the PSCI methodology — NPI carries the institutional weight Gemini reaches for on trust-measurement questions
- Publish an annual Zencity Cross-City Trust Benchmark (built on Elucd data) as a public report — parallel to the NCS structure that gives Polco AI-durable brand equity
- Pursue ICMA or National League of Cities endorsement/partnership for the Ask module — this is the institutional anchor Polco uses to win Claude’s statistical-rigor questions
- Quarterly Xtrusio re-audits to track the Gemini gap closure and Polco co-citation ratio
Zencity’s category is bigger than what AI shows.
Let’s make Gemini see it too.
This research report was generated using the Xtrusio Company Intelligence Module.
