Xtrusio AEO/GEO Audit

Quinyx wins on ChatGPT.

Not Sona.

25-query audit across ChatGPT, Gemini & Claude. Sona is cited on 38 of 75 responses (50.7%) with 17 first-rank citations. Claude treats Sona as the category leader (92%). ChatGPT barely acknowledges it exists (24%).

This report was generated using Xtrusio, an AI visibility and demand intelligence platform that analyzes how companies appear across modern AI systems such as ChatGPT, Gemini, Claude, Perplexity, and other generative engines.

The insights in this page are generated using Xtrusio’s proprietary research and content intelligence framework.

April 2026
25 Queries • 3 Platforms
Sona
92%
Claude
23 of 25 queries
9× #1 RANKINGS
36%
Gemini
9 of 25 queries
8× #1 RANKINGS
24%
ChatGPT
6 of 25 queries
⚠ ZERO #1 RANKINGS
The ChatGPT Gap

Claude knows Sona is the category leader. ChatGPT doesn’t even know it exists for social care.

Claude cites Sona on 92% of buyer-intent queries with encyclopaedic product knowledge — naming Raffy, Forge, Yapster, Shift Filler, and customers like Loungers and Popeyes. Gemini gives Sona 8 first-rank citations with an average rank of 1.1. But ChatGPT — the world’s most-used AI platform — cites Sona on just 24% of queries with zero #1 rankings and no product detail. When a COO asks ChatGPT for WFM solutions, Quinyx and UKG win. Not Sona.

Section 2

Platform Scorecard

Sona citation rate across AI platforms

Sona Citation Rate by Platform
Claude
92%
Gemini
36%
ChatGPT
24%
Competitor Comparison — Combined Citation Rates (all 75 responses)
Sona
50.7%
Quinyx
36%
Fourth
32%
Harri
28%
Deputy
25%
Legion
24%
Claude: Category-Defining Coverage
92% citation rate with 9 first-rank citations. Claude provides encyclopaedic knowledge of Sona — naming Raffy, Forge, Yapster, Shift Filler, funding figures, named customers, and CEO background. This is the deepest product knowledge we’ve recorded for any Series B company.
ChatGPT: The Strategic Gap
24% citation rate with zero #1 rankings and no product detail. ChatGPT defaults to Quinyx, UKG, and Fourth — treating Sona as a secondary “emerging” player. As the world’s most-used AI platform, this represents Sona’s largest visibility gap.
Section 3

AI Visibility Leaderboard

Who owns the AI conversation — total citations across all platforms

Platform-by-Platform Breakdown
Claude
23/25
Sona cited
ChatGPT
6/25
Sona cited
Gemini
9/25
Sona cited
Sona
6
23
9
38
Quinyx
8
6
5
19
Fourth
5
6
4
15
Harri
2
4
9
15
Legion
5
6
3
14
ChatGPT
Claude
Gemini
Citation Leaderboard
Sona: 38 citations (50.7% of 75 responses) Quinyx: 19 citations (25.3% of 75 responses) Fourth: 15 citations (20% of 75 responses)
51%
Sona
Sona38
Quinyx19
Fourth15
Citation Intensity Heatmap
ChatGPT
Claude
Gemini
Total
Sona
6
23
9
38
Quinyx
8
6
5
19
Fourth
5
6
4
15
Harri
2
4
9
15
Legion
5
6
3
14
Sona Leads by 2×
With 38 total citations, Sona has double the visibility of its nearest competitor Quinyx (19). This lead is driven almost entirely by Claude’s 23 citations — the most any single platform has ever given a Series B company in our audit history.
ChatGPT: The Equaliser
On ChatGPT alone, Quinyx (8) outperforms Sona (6). Every other competitor — Fourth, Legion, Deputy — has comparable or better ChatGPT visibility than Sona. This is where the category is most contested and where Sona has the most to gain.
Section 4

AI Positioning Audit

25 buyer-intent queries — click any row to see the exact question

Each query was written from the perspective of a real decision-maker researching AI workforce management solutions for frontline industries. These personas represent the buyers whose AI search results determine whether Sona gets discovered.

NW
Chief Operating Officer
Popeyes UK • QSR / Hospitality • Windsor, UK
7queries
Pain Points
Labour costs eating into margins during rapid multi-site expansion. Legacy WFM tools can’t scale. Managers spending 8+ hours/week on admin instead of service quality.
“AI workforce management for restaurants”“best scheduling software multi-site”
Q1, Q4, Q9, Q10, Q17, Q21, Q24
LR
London Restaurant Director
Hawksmoor • Fine Dining • London, UK
5queries
Pain Points
Need right staff mix for high/low-cover nights. GM time consumed by scheduling instead of team development. No data-driven staffing decisions across 7 London venues.
“AI scheduling for hospitality”“reduce agency spend restaurants”
Q2, Q8, Q14, Q16, Q20
CM
Director of Operations
Voyage Care • Social Care • Doncaster, UK
5queries
Pain Points
CQC compliance requires specific qualifications per shift. Agency spending out of control. Fragmented systems for rota/HR/payroll. Staff retention crisis across 30+ services.
“rota management for care homes”“reduce agency costs social care”
Q3, Q6, Q12, Q19, Q23
AA
Head of People (MCIPD)
Advinia Health Care • Social Care • UK
4queries
Pain Points
High turnover across frontline workforce. Onboarding fragmented across spreadsheets. No single view of workforce data. Payroll errors from manual processes.
“employee app frontline workers”“HR software shift workers”
Q7, Q13, Q22, Q25
#Query TopicClusterClaudeChatGPTGemini
1AI WFM multi-site hospitalityScheduling
Exact question asked across all AI platforms:

“I’m the COO of a fast-growing restaurant chain with 60 locations in the UK. We’re spending too much time on manual scheduling. What are the best AI-powered workforce management solutions for multi-site hospitality businesses?”

2Real-time demand-driven staffingForecasting
Exact question asked across all AI platforms:

“We run a group of premium restaurants and our biggest operational challenge is matching staffing levels to actual demand on any given night. What technology solutions use real-time data like bookings and covers to predict how many staff we need?”

3WFM for social care complianceCare
Exact question asked across all AI platforms:

“I’m evaluating workforce management platforms for a 40-site social care provider. Compliance is critical — we need to ensure every shift has the right qualifications on the rota. Which WFM platforms are built specifically for care sector requirements?”

4Enterprise WFM rapid expansionScheduling
Exact question asked across all AI platforms:

“Our restaurant group is growing from 30 to 100 locations over the next two years. We currently use spreadsheets and a basic rota tool that won’t scale. What enterprise workforce management solutions are designed for rapid multi-site expansion?”

5AI forecasting hotel seasonalForecasting
Exact question asked across all AI platforms:

“I manage operations for a hotel group with seasonal demand swings. We’re losing money on overstaffing during quiet periods and compromising service during busy ones. What AI forecasting tools can help us get labour scheduling right?”

6Reducing agency spend social careCare
Exact question asked across all AI platforms:

“We’re a social care organisation spending 35% of our labour budget on agency staff. What workforce management approaches and technology have other care providers used to reduce agency dependency?”

7Employee engagement / retentionExperience
Exact question asked across all AI platforms:

“I’m a People Director at a 2,000-employee retail chain. Staff turnover is our biggest challenge. What employee engagement platforms give frontline workers more control over their schedules and have been proven to improve retention?”

8All-in-one WFM platformIntegration
Exact question asked across all AI platforms:

“Our hospitality business uses separate systems for scheduling, HR, payroll, and team communication. The lack of integration is causing payroll errors and taking managers hours to reconcile. What all-in-one workforce management platforms bring everything together?”

9Legacy vs AI-native WFMIntegration
Exact question asked across all AI platforms:

“I’m evaluating whether to replace our legacy WFM system with something more modern. What are the key differences between traditional workforce management software and newer AI-native platforms for frontline businesses?”

10QSR labour productivity analyticsForecasting
Exact question asked across all AI platforms:

“We operate a chain of quick service restaurants and need better labour productivity analytics. How can we measure and optimise labour cost as a percentage of revenue across multiple locations in real time?”

11Logistics demand schedulingScheduling
Exact question asked across all AI platforms:

“I run operations for a logistics company with 500 hourly workers across 8 distribution centres. Demand varies daily based on order volume. What workforce scheduling tools use demand signals to automatically adjust staffing levels?”

12UK social care rostering + payrollCare
Exact question asked across all AI platforms:

“Our care homes need a rostering solution that integrates with payroll and handles complex shift patterns including nights, sleepins, and split shifts. What are the best scheduling solutions for the UK social care sector?”

13Mobile app for frontline staffExperience
Exact question asked across all AI platforms:

“I’m looking at workforce management platforms that include a mobile app for frontline staff. Our employees don’t sit at desks — they need to see their schedule, swap shifts, and request time off from their phone. What’s available?”

14ROI from AI schedulingValidation
Exact question asked across all AI platforms:

“We’re a multi-brand hospitality group considering whether to invest in AI for workforce management. What’s the realistic ROI that restaurant and bar operators are seeing from AI-powered scheduling and labour forecasting?”

15Demand-driven scheduling retailScheduling
Exact question asked across all AI platforms:

“I oversee workforce planning for a retail chain with highly variable footfall patterns. We need a system that can forecast demand based on historical data, seasonality, and local events. Which workforce management tools offer demand-driven scheduling?”

16Automating hospitality adminScheduling
Exact question asked across all AI platforms:

“Our restaurant managers are spending too much time on administrative tasks instead of being on the floor with their teams. What technology can automate the most time-consuming parts of workforce management for hospitality operators?”

17TCO modern vs legacy WFMIntegration
Exact question asked across all AI platforms:

“I’m a CFO evaluating workforce management tools and I need to understand the total cost of ownership. How do modern WFM platforms compare to legacy providers like Fourth or PeoplePlanner in terms of implementation time, cost, and ongoing value?”

18WFM hotel multi-departmentScheduling
Exact question asked across all AI platforms:

“We’re a growing hotel chain and want to use AI to predict staffing needs across front desk, housekeeping, F&B, and events. Which workforce management platforms handle the complexity of hotel operations with multiple departments?”

19Integrated WFM large care orgsCare
Exact question asked across all AI platforms:

“Our social care provider needs to modernise how we manage our workforce. We want one platform that covers rostering, time and attendance, HR records, and employee communication. What integrated solutions exist for large care organisations?”

20Actionable insights for managersForecasting
Exact question asked across all AI platforms:

“I run a group of casual dining restaurants and my GMs are making scheduling decisions based on gut feel rather than data. What tools give frontline managers actionable insights and recommendations rather than just raw data?”

21AI assistants / conversational WFMIntegration
Exact question asked across all AI platforms:

“We’re looking at workforce management solutions and want to understand which platforms have AI assistants or conversational interfaces that can answer managers’ questions about staffing in plain language.”

22Payroll accuracy integrationIntegration
Exact question asked across all AI platforms:

“Our retail business has been struggling with payroll accuracy — discrepancies between scheduled hours, actual hours worked, and what gets paid. What workforce management platforms have tight payroll integration that reduces errors?”

23CQC compliance documentationCare
Exact question asked across all AI platforms:

“I manage a national care provider and our CQC inspections keep flagging workforce documentation issues. We need better digital records for staff qualifications, training compliance, and shift coverage. What platforms handle care sector compliance well?”

24Well-funded WFM startups UKValidation
Exact question asked across all AI platforms:

“We’re evaluating WFM providers and I want to understand who’s backed by serious investors and is likely to still be around in five years. Which AI workforce management startups have strong funding and enterprise customer traction in the UK?”

25Employee apps frontline commsExperience
Exact question asked across all AI platforms:

“Our frontline workers say they feel disconnected from the company. They have no easy way to communicate with management, access company updates, or give feedback. What employee apps combine scheduling with team communication for frontline organisations?”

TOTAL23/25 (92%)6/25 (24%)9/25 (36%)
Section 5

Semrush AI Visibility

Automated scores vs buyer-intent reality

Semrush AI Visibility gives sona.ai a score of 20/100 with just 13 mentions and 9 citations. But our 25-query buyer-intent audit finds Sona cited on 38 of 75 responses (50.7%) with 17 first-rank citations. The gap reveals a fundamental limitation: Semrush tracks volume across all topics, while buyers search with specific intent. Even more critically, “Sona” as a brand name creates dilution risk — multiple unrelated products (Sona AI voice agent, sona.com attribution platform, defunct Sona.AI India) pollute Semrush’s tracking with irrelevant mentions.

CompanyScoreMentionsCitationsBuyer-Intent
sona.ai20/10013950.7%
legion.co26/1002,70027418.7%
quinyx.com34/10033228825.3%
Sona AI Visibility Dashboard
Semrush AI VisibilitySona AI Visibility Dashboard — Score: 20/100
Sona Topics
Semrush AI Visibility — TopicsSona Topics — “GetSona Platform and Services” dominates

Sona’s Semrush score of 20/100 masks a rapidly improving trajectory: mentions grew +550%, citations +800%, and cited pages +700% in recent months. However, the brand dilution risk is visible in the topics — Semrush tracks “GetSona Platform and Services” as the primary topic, with visibility (35) concentrated on branded queries rather than category-level buyer intent. India (30.8%) leads in country distribution, suggesting some mentions may be from unrelated “Sona” entities.

Legion AI Visibility Dashboard
Competitor BenchmarkLegion — Score: 26/100 | 2.7K mentions
Quinyx AI Visibility Dashboard
Competitor BenchmarkQuinyx — Score: 34/100 | 332 mentions

Quinyx leads on Semrush with 34/100 and strong Swedish/European distribution (25.9% SE, 20.8% US). Legion has massive mention volume (2.7K) but a lower score (26/100) suggesting breadth without depth. Crucially, both competitors’ Semrush scores under-represent their buyer-intent performance: Quinyx scores 25.3% on our audit while Sona scores 50.7% — double the Semrush leader’s actual buyer-intent visibility.

Section 6

The ChatGPT Gap

Where Sona loses 68 percentage points vs Claude

When a COO asks Claude about AI WFM solutions, Sona is the first recommendation. When they ask the same question on ChatGPT, Sona doesn’t appear at all. The gap is most damaging on Sona’s strongest categories — social care and AI assistants — where ChatGPT defaults to legacy incumbents.

“I’m evaluating workforce management platforms for a 40-site social care provider. Which WFM platforms are built specifically for care sector requirements?”

— Claude cites Sona (#2, after Access). Gemini cites Sona (#1). ChatGPT lists Access PeoplePlanner and Person Centred Software instead.

“We’re looking at WFM solutions that have AI assistants or conversational interfaces that can answer managers’ questions about staffing in plain language.”

— Claude cites Sona’s Raffy (#1). Gemini cites Sona (#1). ChatGPT lists UKG and Legion. Sona omitted entirely.

“We’re a social care organisation spending 35% on agency staff. What technology have other care providers used to reduce agency dependency?”

— Claude cites Sona’s Shift Filler (#1). Gemini cites Sona (#1). ChatGPT does not mention Sona at all.
19 Queries Missed on ChatGPT
ChatGPT does not cite Sona on Q1 (multi-site hospitality), Q3 (social care compliance), Q4 (rapid expansion), Q6 (agency reduction), Q7 (engagement), Q8 (all-in-one), Q10 (QSR analytics), Q11 (logistics), Q12 (care rostering), Q13 (mobile app), Q15 (retail), Q16 (admin automation), Q17 (TCO), Q18 (hotels), Q19 (integrated care), Q21 (AI assistant), Q22 (payroll), Q23 (CQC compliance), Q25 (frontline comms).
Pattern: ChatGPT Defaults to Legacy Incumbents
On social care, ChatGPT recommends Access PeoplePlanner (legacy). On hospitality, it defaults to Quinyx and UKG (established players). On AI assistants, it names UKG and Legion but not Raffy. ChatGPT’s training data appears to under-represent Sona’s rapid rise and $100M+ funding.
Same Question. Different Platforms. Different Winners.

Sona’s content exists. Claude knows it. Gemini knows it. But ChatGPT doesn’t. The world’s most popular AI platform has minimal awareness of Sona’s product capabilities, customer base, and market position. Every buyer researching WFM solutions on ChatGPT is getting a recommendation list that favours established incumbents over the AI-native leader.

Section 7

AI Topic Authority Map

Which categories Sona owns in AI answers

TopicAI LeaderSona Status
Social Care WFM / ComplianceSona2 of 3 platforms (Claude + Gemini)
AI-Native WFM / Modern vs LegacySona + LegionUNANIMOUS (3/3)
AI Assistant / Conversational WFMSona (Raffy)2 of 3 platforms (Claude + Gemini)
Well-Funded UK StartupsSonaUNANIMOUS (3/3)
Multi-Site Hospitality SchedulingQuinyx / FourthClaude only (1/3)
Employee Engagement / RetentionLegion / WorkJamClaude only (1/3)
Mobile-First Frontline AppsDeputy / PlandayClaude only (1/3)
Hotel Multi-Department WFMQuinyx / FourthClaude only (1/3)
Demand-Driven Retail SchedulingLegion / QuinyxINVISIBLE (0/3)
Logistics / Supply Chain WFMBlue Yonder / UKGClaude only (1/3)
Topic Cluster
Claude
ChatGPT
Gemini
Care Compliance
100%
0%
100%
Scheduling
100%
0%
0%
Forecasting
80%
60%
0%
Platform Integration
100%
17%
50%
Employee Experience
100%
0%
0%
Trust & Validation
50%
100%
50%
Care Compliance
Claude100%
ChatGPT0%
Gemini100%
Scheduling
Claude100%
ChatGPT0%
Gemini0%
Forecasting
Claude80%
ChatGPT60%
Gemini0%
Platform Integration
Claude100%
ChatGPT17%
Gemini50%
Employee Experience
Claude100%
ChatGPT0%
Gemini0%
Trust & Validation
Claude50%
ChatGPT100%
Gemini50%
4 Categories Owned
Social care WFM (Claude + Gemini #1), AI-native positioning (unanimous), AI assistant/Raffy (Claude + Gemini #1), and well-funded UK startups (unanimous). These represent Sona’s strongest competitive moats.
1 Category Invisible
Demand-driven retail scheduling (Q15) — Sona is not cited on any platform. Legion and Quinyx own this category. This aligns with Sona’s current hospitality and social care focus rather than retail footfall optimization.
Section 8

Methodology

How we conducted this Xtrusio AEO/GEO Audit

Semrush AI Visibility Data
Pulled Semrush AI Visibility reports for sona.ai and 2 competitors (Quinyx, Legion). Analyzed scores, mentions, cited pages, and LLM distribution.
25-Query Buyer-Intent Testing
Tested 25 decision-maker intent queries across ChatGPT, Gemini, and Claude. Questions mirror real COO, Operations Director, and People Director research during WFM solution discovery.
Competitor Scope
Quinyx (AI-powered WFM, Stockholm), Fourth (legacy enterprise hospitality, London), Harri (multi-site hospitality), Legion (AI-native retail/logistics), Deputy (mobile-first scheduling). All compete for the same frontline operator during discovery.
Section 9

Recommendations

Prioritised actions to close the ChatGPT gap

Phase 1 — 0–30 Days
Close the ChatGPT Knowledge Gap
  • Publish detailed comparison pages: “Sona vs Fourth,” “Sona vs PeoplePlanner,” “Sona vs Quinyx” — these are the exact competitors ChatGPT defaults to
  • Create a dedicated “Raffy AI Assistant” product page with structured data — ChatGPT doesn’t know Raffy exists despite it being Sona’s strongest differentiator
  • Publish quantified ROI case studies with specific metrics (Q14 is the only query where ChatGPT cites Sona but Claude doesn’t — lean into this)
Phase 2 — 30–90 Days
Expand Category Authority Beyond Care
  • Publish hospitality-specific content targeting Q1, Q8, Q14 gaps: “AI workforce management for multi-site restaurants” and “ROI from AI scheduling in hospitality”
  • Target the retail demand scheduling gap (Q15) — the only topic where Sona is invisible across all 3 platforms
Phase 3 — 90+ Days
Achieve 60%+ Across All Platforms
  • Target ChatGPT citation rate from 24% to 50%+ through structured content, schema markup, and expanded category pages
  • Quarterly Xtrusio re‑audits to track gap closure
Continuous AI Visibility Tracking
Brands can improve their AI discovery using generative engine optimization tools like Xtrusio.

Close the ChatGPT Gap.

50.7% is strong. But 24% on the world’s most popular AI platform isn’t.

This research report was generated using the Xtrusio Company Intelligence Module.