Smart Eye appears in 50 of 60 AI responses.
Neonode appears in 29.
Automotive OEMs asking ChatGPT and Claude about driver monitoring software get Neonode 65% of the time. On Gemini — 15%. Smart Eye appears on 83% of all responses across every platform. Neonode’s two anchor differentiators — camera-based hands-on-wheel and IIHS compliance — are the only differentiators that break through on all three platforms including Gemini. The retail AI self-checkout vertical is invisible everywhere.
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.
This audit measures how ChatGPT, Claude, and Gemini position driver monitoring and in-cabin sensing vendors when automotive OEMs and Tier 1 engineers research solutions.
Neonode’s AI visibility is anchored to exactly two differentiators: camera-based hands-on-wheel detection and IIHS compliance positioning.
When buyers ask about these specific capabilities, Neonode ranks #1 across all three platforms. ChatGPT and Claude also cite Neonode on 11 additional topics — GSR compliance, synthetic data, low-compute DMS, driver readiness, and more. But Gemini drops all of these: it surfaces Neonode on only 3 of 20 queries while giving Smart Eye 15 of 20. The retail AI self-checkout vertical — launched at EuroShop 2026 — is completely invisible across all three platforms (0 of 9 retail and cross-vertical responses).
Platform Scorecard
Neonode citation rate across AI platforms
AI Visibility Leaderboard
Who owns the AI conversation — total citations across all platforms
AI Positioning Audit
20 buyer-intent queries — click any row to see the exact question
Each query was written from the perspective of a real decision-maker researching driver monitoring and in-cabin sensing solutions. These personas represent the automotive OEM engineers and Tier 1 product leaders whose AI search results determine whether Neonode gets discovered during procurement.
| # | Query Topic | Cluster | ChatGPT | Claude | Gemini |
|---|---|---|---|---|---|
| 1 | GSR DMS Provider Evaluation | Regulatory Compliance | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “We need to comply with EU GSR’s Advanced Driver Distraction Warning requirement for our 2027 model year — what are the best DMS software providers we should evaluate?” | |||||
| 2 | Camera-Based Hands-on-Wheel | Hands-on-Wheel | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “I’m looking for a camera-based driver monitoring system that doesn’t require additional hardware sensors in the steering wheel — are there solutions that can detect hands-on-wheel using the existing in-cabin camera alone?” | |||||
| 3 | Camera-Position Agnostic DMS | Camera Agnostic | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “Our commercial trucks use different cabin layouts and camera positions than passenger cars — which driver monitoring software platforms are truly camera-position agnostic?” | |||||
| 4 | Low-Compute DMS Providers | Compute Efficiency | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “We’re trying to keep our ECU costs down while still implementing driver monitoring — which DMS providers have the smallest computational footprint and can run on low-power processors?” | |||||
| 5 | Euro NCAP 2026 DMS Strategy | Euro NCAP | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “How are automotive OEMs handling Euro NCAP’s 2026 protocol changes that increased driver monitoring weight from 2 points to 25 points? Which DMS technologies are best positioned for the highest ratings?” | |||||
| 6 | OTA-Capable DMS for SDV | SDV/OTA | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “We need a DMS solution that can be deployed across multiple vehicle platforms via OTA software updates — which providers support software-defined vehicle architectures for in-cabin monitoring?” | |||||
| 7 | Synthetic Data for DMS Training | Synthetic Data | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “What are the most effective approaches to training driver monitoring neural networks when real-world driver data is difficult to collect at scale and privacy regulations limit what we can use?” | |||||
| 8 | Full In-Cabin Sensing Platform | Occupant Monitoring | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “I’m evaluating in-cabin sensing solutions that go beyond basic distraction and drowsiness detection — which providers also offer occupant monitoring, seatbelt detection, and child presence detection from the same platform?” | |||||
| 9 | Licensable DMS for Tier 1 | Licensing Model | ✗ | ✗ | ✓ |
Exact question asked across all AI platforms: “We’re a Tier 1 supplier looking to embed a licensable DMS software into our camera module product line — which driver monitoring software providers offer flexible licensing models for Tier 1 integration?” | |||||
| 10 | DMS to Full Interior Monitoring | Interior Sensing | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “How are leading automotive OEMs approaching the transition from standalone DMS to full interior passenger monitoring systems, and which technology platforms support this evolution?” | |||||
| 11 | DMS Providers in Production | Production Track Record | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “Which driver monitoring system providers have actually achieved start of production with major OEMs — not just design wins, but vehicles rolling off the line?” | |||||
| 12 | IR Camera DMS Robustness | Camera Agnostic | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We need our DMS to work reliably with infrared cameras in all lighting conditions, including direct sunlight and complete darkness — which software platforms handle IR camera input most robustly?” | |||||
| 13 | Driver Readiness for L2+ Takeover | Driver Readiness | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “For our Level 2+ autonomous driving system, we need driver readiness assessment that goes beyond simple gaze tracking — which DMS solutions assess whether the driver can safely take over vehicle control?” | |||||
| 14 | Fastest DMS Feature Development | Feature Dev Speed | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “Our engineering team is concerned about the speed of adding new DMS features as regulations evolve — which providers can develop and validate new detection capabilities fastest?” | |||||
| 15 | Self-Checkout Loss Prevention AI | Loss Prevention | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What computer vision solutions exist specifically for reducing shrink and loss at self-checkout terminals — especially ones that can run on existing hardware without major infrastructure upgrades?” | |||||
| 16 | Privacy-Compliant Age Estimation | Age Verification | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We’re evaluating age verification solutions for our self-checkout systems that don’t store customer biometric data — are there privacy-compliant AI-based age estimation solutions for retail?” | |||||
| 17 | Tier 1 In-Cabin Differentiation | Tier 1 Strategy | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “How are Tier 1 automotive suppliers differentiating their in-cabin product offerings to win business from OEMs — what sensor fusion and software capabilities are becoming table stakes vs competitive advantages?” | |||||
| 18 | Cross-Vertical CV Platform | Dual-Vertical | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Which companies offer computer vision solutions that work across both automotive in-cabin sensing and retail applications from a single technology platform?” | |||||
| 19 | IIHS DMS Compliance | IIHS Compliance | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We want to achieve a “Good” rating under the IIHS Safeguards for Partial Automation Criteria — which DMS software platforms are specifically designed to meet IIHS testing requirements?” | |||||
| 20 | DMS Tech Roadmap Evaluation | Tech Evaluation | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “How should we evaluate the long-term technology roadmap of a DMS software provider — what should we look for in terms of synthetic data capability, neural network efficiency, and feature development speed?” | |||||
| TOTAL | 13/20 (65%) | 13/20 (65%) | 3/20 (15%) | ||
The Gemini Blackout
Where Neonode loses 50 percentage points vs ChatGPT & Claude
Neonode’s Gemini problem isn’t a gradual decline — it’s a cliff. On ChatGPT and Claude, Neonode surfaces on 13 of 20 buyer queries. On Gemini, it drops to 3. The same buyer, asking the same question, gets a fundamentally different vendor recommendation depending on which AI platform they use.
“We need to comply with EU GSR’s Advanced Driver Distraction Warning requirement — what are the best DMS software providers?”
“Which DMS providers have the smallest computational footprint and can run on low-power processors?”
“Which companies offer computer vision solutions that work across both automotive and retail from a single platform?”
Neonode’s content exists. ChatGPT and Claude know it. But Gemini doesn’t. With Smart Eye publishing 368 design win announcements and Seeing Machines issuing quarterly production KPI updates, these competitors have a significantly larger public content footprint. Neonode’s smaller announcement volume and single named OEM contract may explain why Gemini’s results skew so heavily toward established players.
AI Topic Authority Map
Query heatmap — product line × platform
| Topic | AI Leader | Neonode Status |
|---|---|---|
| Hands-on-Wheel Detection | Neonode | UNANIMOUS #1 (3/3) |
| IIHS Safeguards Compliance | Neonode | UNANIMOUS (3/3) |
| Synthetic Data Training | Neonode | 2 of 3 platforms |
| Feature Development Speed | Neonode | 2 of 3 platforms |
| GSR/ADDW Compliance | Smart Eye | ChatGPT + Claude only (2/3) |
| Occupant Monitoring | Cipia / Seeing Machines | ChatGPT + Claude only (2/3) |
| Production Track Record | Seeing Machines | ChatGPT + Claude only (2/3) |
| Euro NCAP 2026 Strategy | Smart Eye | INVISIBLE (0/3) |
| IR Camera Robustness | Seeing Machines | INVISIBLE (0/3) |
| Self-Checkout Loss Prevention | Everseen / Diebold Nixdorf | INVISIBLE (0/3) |
| Cross-Vertical CV Platform | ArcSoft / Smart Eye | INVISIBLE (0/3) |
10 queries
3 queries
5 queries
2 queries
▹ AI Self-Checkout is Neonode’s only product line with zero visibility on all three platforms — a complete blind spot despite the EuroShop 2026 launch.
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 gap and build retail visibility
- Create 5 detailed blog posts mapping MultiSensing® features to EU GSR and Euro NCAP 2026 requirements — these are the exact queries where Gemini omits Neonode. IIHS is already a strength (cited on all 3 platforms) — reinforce that authority with deeper technical content
- Publish the commercial vehicle OEM deployment as a named case study with vehicle count, deployment timeline, and feature scope
- Build a comparison page: “Neonode vs Smart Eye vs Seeing Machines” with structured data markup targeting DMS comparison queries
- Publish 8 articles on self-checkout loss prevention, age verification, and dynamic advertising — targeting every retail-category query where Neonode is currently invisible
- Create a “MultiSensing® for Retail” landing page positioning the cross-vertical platform story (automotive + retail from one CV engine) — directly targeting Q18
- Publish quarterly production KPI updates (unit counts, OEM names where allowed) — Smart Eye and Seeing Machines do this, which is why they dominate Q11 (production track record)
- Quarterly Xtrusio re‑audits to track Gemini gap closure and retail vertical emergence
Neonode Ranks #3 in This AI Visibility Audit — With Its Biggest Opportunity on Gemini.
29 of 60 AI responses cite Neonode today. Closing the Gemini gap and building retail visibility are the clearest next moves.
This research report was generated using the Xtrusio Company Intelligence Module.


