Xtrusio AEO/GEO Audit

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.

August 2026
20 Queries • 3 Platforms
Neonode
65%
ChatGPT
13 of 20 queries
2× #1 RANKINGS
65%
Claude
13 of 20 queries
5× #1 RANKINGS
15%
Gemini
3 of 20 queries
⚠ 50-POINT DROP
The Gemini Blackout

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).

Section 2

Platform Scorecard

Neonode citation rate across AI platforms

Neonode Citation Rate by Platform
ChatGPT
65%
Claude
65%
Gemini
15%
Competitor Comparison — Combined Citation Rates (60 responses)
Smart Eye
83%
Seeing Machines
73%
Neonode
48%
Cipia
47%
emotion3D
37%
ChatGPT & Claude Parity
Neonode holds identical 65% citation rates on both ChatGPT and Claude — strong positioning among the top 3 DMS vendors on these platforms. Claude awards 5 #1 rankings, the highest of any platform.
Gemini: 50-Point Drop
Gemini cites Neonode on only 3 of 20 queries (15%) while giving Smart Eye 15 of 20. The same buyer asking the same question on Gemini gets a completely different vendor landscape — one where Neonode barely exists.
Section 3

AI Visibility Leaderboard

Who owns the AI conversation — total citations across all platforms

Platform-by-Platform Breakdown
ChatGPT
13/20
Neonode cited
Claude
13/20
Neonode cited
Gemini
3/20
Neonode cited
Smart Eye
18
17
15
50
Seeing Machines
17
18
9
44
Neonode
13
13
3
29
Cipia
10
14
4
28
emotion3D
14
8
22
ChatGPT
Claude
Gemini
Citation Leaderboard
Smart Eye: 50 citations (83%) Neonode: 29 citations (48%) Seeing Machines: 44 citations (73%)
48%
Neonode
Smart Eye50
Seeing Machines44
Neonode29
Citation Intensity Heatmap
ChatGPT
Claude
Gemini
Total
Smart Eye
18
17
15
50
Seeing Machines
17
18
9
44
Neonode
13
13
3
29
Cipia
10
14
4
28
emotion3D
14
8
0
22
Neonode Ranks #3 Overall
At 29 total citations, Neonode sits third in the DMS leaderboard — ahead of Cipia (28) and emotion3D (22). A strong mid-tier position, but 21 citations behind Smart Eye’s dominant 50.
Gemini Is the Weakest Link for Everyone
Every DMS vendor sees lower Gemini citations, but Neonode’s drop is the steepest: from 13 on ChatGPT/Claude to just 3 on Gemini. Smart Eye drops only from 18/17 to 15. The Gemini gap is uniquely severe for Neonode.
Section 4

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.

Target Buyer Sector VP/Director Engineering, ADAS & Safety leaders at Passenger Car OEMs, Commercial Vehicle Manufacturers & Automotive Tier 1 Suppliers
SM
Strategy & Global Product Mgmt Director
FORVIA • Tier 1 Automotive • Paris, France
7queries
Pain Points
Needs to evaluate DMS software partners for integration into FORVIA’s mirror-integrated camera modules. Must balance Euro NCAP 2026 compliance, compute efficiency, and feature roadmap speed across multiple OEM platforms.
“licensable DMS software for Tier 1”“synthetic data driver monitoring”
Q7, Q9, Q14, Q17, Q18, Q19, Q20
DC
Automotive Technical Leader
Stellantis • Global OEM • Detroit, USA
7queries
Pain Points
Evaluating cross-domain controller architectures and in-cabin sensing for Stellantis’ vehicle platforms. Needs DMS that supports OTA updates, minimal compute, and meets both EU GSR and IIHS requirements across global markets.
“DMS for Euro NCAP 2026 highest rating”“camera-based hands-on-wheel L2+”
Q1, Q2, Q5, Q6, Q10, Q11, Q13
HN
Product Mgmt Director, Interior Sensing
Magna International • Tier 1 • Seattle, USA
6queries
Pain Points
Building Magna’s interior sensing product line with scalable, modular architecture. Evaluates DMS software that works across camera types, supports sensor fusion, and can run on commercial vehicle and passenger car platforms alike.
“camera-agnostic DMS commercial trucks”“occupant monitoring child presence”
Q3, Q4, Q8, Q12, Q15, Q16
#Query TopicClusterChatGPTClaudeGemini
1GSR DMS Provider EvaluationRegulatory 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?”

2Camera-Based Hands-on-WheelHands-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?”

3Camera-Position Agnostic DMSCamera 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?”

4Low-Compute DMS ProvidersCompute 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?”

5Euro NCAP 2026 DMS StrategyEuro 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?”

6OTA-Capable DMS for SDVSDV/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?”

7Synthetic Data for DMS TrainingSynthetic 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?”

8Full In-Cabin Sensing PlatformOccupant 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?”

9Licensable DMS for Tier 1Licensing 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?”

10DMS to Full Interior MonitoringInterior 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?”

11DMS Providers in ProductionProduction 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?”

12IR Camera DMS RobustnessCamera 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?”

13Driver Readiness for L2+ TakeoverDriver 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?”

14Fastest DMS Feature DevelopmentFeature 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?”

15Self-Checkout Loss Prevention AILoss 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?”

16Privacy-Compliant Age EstimationAge 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?”

17Tier 1 In-Cabin DifferentiationTier 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?”

18Cross-Vertical CV PlatformDual-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?”

19IIHS DMS ComplianceIIHS 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?”

20DMS Tech Roadmap EvaluationTech 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?”

TOTAL13/20 (65%)13/20 (65%)3/20 (15%)
Section 5

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?”

— ChatGPT and Claude both cite Neonode. Gemini lists Smart Eye, Seeing Machines, and ArcSoft instead.

“Which DMS providers have the smallest computational footprint and can run on low-power processors?”

— Claude ranks Neonode #2 for low-compute DMS. Gemini names ArcSoft, Smart Eye, and Eyeris — Neonode doesn’t appear.

“Which companies offer computer vision solutions that work across both automotive and retail from a single platform?”

— This is Neonode’s exact positioning (MultiSensing® for auto + self-checkout). All three platforms name other vendors. Zero citations for Neonode’s dual-vertical play.
17 Queries Missed on Gemini
Of the 20 buyer-intent queries, Gemini omits Neonode on 17. The 3 it catches are all narrow differentiator questions: hands-on-wheel (Q2), Tier 1 licensing (Q9), and IIHS compliance (Q19).
Pattern: Gemini Defaults to Smart Eye
Smart Eye appears on 15 of 20 Gemini responses — near-universal default positioning. This pattern may reflect stronger retrievable signals around Smart Eye, including its larger public content footprint, press release volume, and 368 OEM design win announcements.
Same Question. Different Platforms. Different Winners.

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.

Section 6

AI Topic Authority Map

Query heatmap — product line × platform

TopicAI LeaderNeonode Status
Hands-on-Wheel DetectionNeonodeUNANIMOUS #1 (3/3)
IIHS Safeguards ComplianceNeonodeUNANIMOUS (3/3)
Synthetic Data TrainingNeonode2 of 3 platforms
Feature Development SpeedNeonode2 of 3 platforms
GSR/ADDW ComplianceSmart EyeChatGPT + Claude only (2/3)
Occupant MonitoringCipia / Seeing MachinesChatGPT + Claude only (2/3)
Production Track RecordSeeing MachinesChatGPT + Claude only (2/3)
Euro NCAP 2026 StrategySmart EyeINVISIBLE (0/3)
IR Camera RobustnessSeeing MachinesINVISIBLE (0/3)
Self-Checkout Loss PreventionEverseen / Diebold NixdorfINVISIBLE (0/3)
Cross-Vertical CV PlatformArcSoft / Smart EyeINVISIBLE (0/3)
Product Line
ChatGPT
Claude
Gemini
Driver Monitoring
10 queries
80%
80%
20%
In-Cabin Sensing
3 queries
67%
67%
0%
MultiSensing Platform
5 queries
60%
60%
20%
AI Self-Checkout
2 queries
0%
0%
0%

▹ 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.

Driver Monitoring • 10 queries
ChatGPT80%
Claude80%
Gemini20%
In-Cabin Sensing • 3 queries
ChatGPT67%
Claude67%
Gemini0%
MultiSensing Platform • 5 queries
ChatGPT60%
Claude60%
Gemini20%
AI Self-Checkout • 2 queries
ChatGPT0%
Claude0%
Gemini0%
Driver Monitoring: 80% on ChatGPT & Claude
Neonode’s core DMS product line has strong AI visibility on two of three platforms. Hands-on-wheel detection and synthetic data training are the anchors that pull the entire line up.
AI Self-Checkout: 0% Everywhere
Despite launching at EuroShop February 2026, no AI platform associates Neonode with retail loss prevention, age verification, or self-checkout computer vision. Competitors like Everseen, Diebold Nixdorf, and Yoti own these queries entirely.
Section 7

Methodology

How we conducted this Xtrusio AEO/GEO Audit

20-Query Buyer-Intent Testing
Tested 20 decision-maker intent queries across ChatGPT, Claude, and Gemini. Questions mirror real automotive OEM and Tier 1 engineering research during DMS procurement and vendor evaluation.
Competitor Scope
Smart Eye (368 design wins, market leader), Seeing Machines (GM Super Cruise, Mercedes S-Class), Cipia/Harman (Mobileye integration), and emotion3D/indie Semiconductor. All compete for the same automotive OEM buyer during DMS procurement.
Dual-Vertical Coverage
Questions span both Neonode’s automotive DMS vertical (18 queries) and the emerging AI self-checkout vertical (2 queries) to test cross-platform awareness of the MultiSensing® platform’s dual-market positioning.
Section 8

Recommendations

Prioritized actions to close the Gemini gap and build retail visibility

Phase 1 — 0–30 Days
Publish Gemini-Optimized OEM Win Content
  • 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
Phase 2 — 30–90 Days
Build AI Self-Checkout Content Moat from Zero
  • 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
Phase 3 — 90+ Days
Scale Production Evidence & Measure Progress
  • 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
Continuous AI Visibility Tracking
Brands can improve their AI discovery using generative engine optimization tools like Xtrusio. Neonode’s 48% composite rate has clear upside — the Gemini gap alone represents 10 recoverable citations.

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.