--- title: "Quals.ai qualitative research platform competitive profile" source: internal-research created: "2026-05-06" status: active tags: [competitive, quals-ai, twentify, trellist, qualitative-research, ai-interviews, internal] --- # Competitive Profile: Quals.ai Date checked: 2026-05-06 ## Scope This note profiles **Quals.ai**, the Twentify-owned AI qualitative research platform, based on its current public website, supporting product pages, public data-protection material, LinkedIn company information, and the April 2026 Trellist / Twentify U.S. partnership announcement. Important naming note: - This profile is about **Quals.ai** / **QualsAI by Twentify** at `quals.ai`. - It is not about similarly named companies such as QualSights, Qual, Qualscale, or Quali. ## Executive summary - Quals.ai is best understood as a **fast-turn AI qualitative research platform** rather than a general insights operating system. - Its strongest public proposition is: - real human participants, not synthetic respondents - AI-led text and voice interviews - multilingual qualitative capture - automated transcripts, themes, quotes, sentiment, summaries, and reports - outputs in hours / as little as 24 hours - The product is commercially relevant because it attacks one of the most painful qualitative research bottlenecks: recruiting, moderating, transcribing, coding, and summarising small-to-medium qual studies quickly. - Quals.ai's differentiation is less about deep strategic memory or formal research governance, and more about making real-human qual feel as fast and scalable as a lightweight survey. - The April 21, 2026 Trellist / Twentify announcement materially strengthens the U.S. go-to-market story. Trellist is the exclusive U.S. partner and provides strategy, research design, implementation support, and an agency-partner route. - Public proof is directionally credible, especially the LC Waikiki loyalty-app embedded case signal, but the public product story is uneven across pages and leaves open questions around pricing, actual self-serve maturity, quality mechanics, and enterprise-grade defensibility. ## Company and platform snapshot Quals.ai presents itself as an AI qualitative research platform for: - market research teams - UX and product teams - CX and insight teams - market research agencies - brand and marketing teams Public company signals: - owned / created by **Twentify** - LinkedIn lists the company as: - headquarters: Newport, Wales - company size: `11-50` employees - founded: `2015` - privately held - public site shows offices / presence in: - UK - United States through Trellist - Turkey - Dubai - LinkedIn also lists a Canada location - public customer-logo signals on the homepage include Coca-Cola, Pepsi, Unilever, Shell, and Nestle The company also links Quals.ai to Twentify's broader research ecosystem, including **Bounty**, its mobile research / customer survey app. ## What the product appears to include ### 1. Study setup and guide generation Quals.ai helps users define the research objective, target audience, and interview approach. The AI research assistant page says the system can support: - study title generation - target demographic definition - interview structure generation - test interviews before launch - directives for deeper focus on specific sections This makes the product feel more like an AI-assisted qual project setup tool than a blank survey builder. ### 2. AI-led text interviews The FAQ says data is collected through text-based interviews with consumers who meet specific criteria. Typical projects are described as `20` real individuals with average interviews of around `10` minutes. Interviews can be conducted through: - the Bounty app - any web browser - messaging applications, including WhatsApp Participants can come from: - Bounty platform users - secondary panels - other databases if required - the client's own database or CRM ### 3. AI-led voice interviews Voice is a major product signal. Quals.ai says its voice interviewer can: - conduct guided, natural voice conversations - listen, respond, and adapt in real time - let participants interrupt or revisit previous answers - capture real voice responses for emotional nuance - automatically transcribe voice interviews - let researchers listen back from the insights dashboard Voice launch-language support is listed as English, Dutch, French, German, Italian, Japanese, Portuguese, and Spanish, with more languages planned. ### 4. SmartAsk follow-up intelligence SmartAsk appears to be Quals.ai's post-interview interrogation layer. Public pages describe it as a way to: - probe deeper into completed interviews - ask custom follow-up questions based on transcripts - explore themes and sentiment - refine understanding after initial outputs are available This is commercially useful, but the public story suggests it is mainly a project-level qualitative analysis tool, not yet a broad enterprise research-memory system. ### 5. Automated analysis and reporting Quals.ai publicly promises: - transcripts - themes - quotes - sentiment - summaries - executive / actionable reports - personas and journey maps in the embedded loyalty-app context The key commercial promise is not only capture, but reducing manual coding and report assembly. ### 6. Participant recruitment Quals.ai sells real participant access as a core asset, not an add-on. Public claims include: - recruitment across `150+` countries - audience segmentation by profession, demographics, behaviours, and other criteria - healthcare panels with over `2 million` practitioners across `130+` therapy areas - B2B professionals, C-level decision makers, product leads, IT buyers, and general consumers - identity checks, role verification, pre-screening, fraud detection, and a one-strike policy There is some inconsistency: the AI research assistant page says automatic participant / screener selection is "coming soon", while other pages describe recruitment and participant targeting as a current strength. Treat the recruitment capability as real, but the degree of automation as an open question. ### 7. Embedded loyalty-app research Quals.ai Embedded is one of the more strategically interesting parts of the public surface. It positions Quals.ai as an AI interview engine inside loyalty apps, panels, and communities. Publicly described capabilities include: - AI-moderated interviews inside a brand-owned app - no separate recruitment when using existing loyalty users - transcripts, sentiment, themes, quotes, motivations, and barriers - automatically generated personas and journey maps - white-label dashboard - API / SDK integration - reward integration with loyalty points, coupons, vouchers, cashback The page names LC Waikiki as a live retail example and claims: - integration completed in `48 hours` - hundreds of AI interviews completed - no recruitment needed - insights ready in less than `24 hours` - actions delivered to CRM, product, and marketing teams This is a useful wedge because it reframes Quals.ai from project-by-project qual into always-on customer understanding inside owned channels. ## How they sell the product Quals.ai's commercial story rests on six messages: 1. **Real humans beat synthetic respondents.** The site repeatedly argues that synthetic data lacks lived context, emotion, contradiction, and defensibility. 2. **Qual can now move at survey speed.** The core promise is insights in hours or as little as 24 hours, without the scheduling and analysis drag of traditional qual. 3. **AI should moderate, transcribe, code, and summarise.** Quals.ai sells the operational compression of the qualitative workflow. 4. **Text and voice should live in one platform.** The ability to choose text or voice per study is an important accessibility and methodology message. 5. **Global and multilingual qual should be easier.** Public materials make broad language and country coverage central, although the exact language counts vary by source. 6. **Brands can turn existing audiences into qual panels.** The embedded loyalty-app offer is a stronger strategic wedge than the generic AI-qual platform story. ## Pricing and packaging signals No current public pricing page was confirmed in this pass. Search results still surface an older Quals.ai pricing page with self-service and full-service package examples, but that page now redirects to the homepage. Current public packaging signals point toward: - demo / contact-led sales - project-based or package-based commercial logic - self-service plus assisted / full-service modes - add-ons by feature tier, since voice is described as available on Pro, Team, and Enterprise plans, with Starter able to upgrade - services-backed U.S. distribution through Trellist The Trellist partnership is especially important. In the United States, Quals.ai is being sold with strategy, research design, implementation support, and agency enablement, not just as a pure software subscription. ## Where they look strongest - **Real-human positioning** Quals.ai has a clear wedge against synthetic research tools and AI persona products. That message will land with researchers who are wary of simulated respondents. - **Fast qual operations** The promise of AI-led interviews plus automated analysis directly attacks the cost, scheduling, transcription, and coding burden of traditional qual. - **Text plus voice** Voice creates a richer qualitative feel than text-only chat and gives Quals.ai a more human capture story. - **Participant access** The Bounty platform, secondary panels, and claimed global recruitment reach make the offer more complete than AI-interview software alone. - **Embedded research angle** The loyalty-app product could be a strong differentiator for retail, CPG, and customer-app environments where brands already have known user bases. - **Agency channel** Trellist's U.S. partnership and agency program could help Quals.ai reach buyers who prefer a services-supported path into AI qual. - **Public data-protection posture** The AI Data Usage & Protection Policy is more explicit than many competitor sites about consent, anonymity, transcripts, AI role disclosure, encryption, retention, GDPR, and restrictions on training AI models with client data. ## Where they look weaker or less clear - **Public product polish and consistency** The site contains uneven copy and at least one visible Lorem Ipsum block on the recruitment page. That does not invalidate the product, but it slightly weakens the enterprise-trust surface. - **Language and coverage claims vary** The homepage says `50+` languages, the Trellist announcement says `100+` languages, and the voice page lists a smaller launch set for voice. These may all be true for different modes, but the public distinction is not crisp. - **Pricing is opaque** Without current public pricing, it is hard to benchmark true cost per project, per interview, or per package. - **Recruitment automation is ambiguous** Recruitment is sold as a core capability, but automatic participant / screener selection is also described as coming soon on one product page. - **Governance is mostly policy-level, not workflow-level** Quals.ai has privacy and ethics language, but public materials are lighter on formal research design QA, pre-launch methodological review, decision audit trails, and governed revision workflows. - **Limited evidence of cross-study memory** SmartAsk is useful, but public materials do not show a Listen Labs-style research library or a broader enterprise insight repository. - **Less direct quant / mixed-method operating-system overlap** Quals.ai is highly relevant for qual capture and fast analysis, but it is not yet publicly positioned as a full quant + qual + knowledge + trust centre platform. ## Relevance for EmpathyIQ Quals.ai matters because it competes with EmpathyIQ's qualitative research platform ambitions and parts of the AI-assisted research workflow story. ### Likely overlap with EmpathyIQ - AI-assisted study setup - AI-led qualitative interviews - multilingual text / voice research - automated qual analysis and reporting - real-human participant positioning - agency and insight-team workflows - faster path from brief to findings ### Where Quals.ai may feel ahead - More concrete public offer for AI-led qual interviews with real participants. - Voice interview positioning is easy to understand and commercially appealing. - Embedded loyalty-app angle could be compelling for retail / CPG brands with owned audiences. - U.S. services-backed distribution through Trellist may reduce adoption friction for buyers who want help rather than pure self-serve software. ### Where EmpathyIQ can differentiate - **Defensible research workflow** EmpathyIQ can be more explicit about quality, governance, and decision defensibility before, during, and after fieldwork. - **Research Guard and Trust Centre** Quals.ai's trust language is mostly privacy, ethics, and real-participant oriented. EmpathyIQ can go deeper on methodological review, questionnaire risk, respondent-quality review, and live fieldwork controls. - **Connected quant + qual + knowledge** Quals.ai looks strongest as a qual engine. EmpathyIQ can position around the broader research system: quant, qual, trust, reporting, and reusable evidence. - **Research memory** If EmpathyIQ develops a strong knowledge repository and cross-study retrieval layer, that can exceed SmartAsk-style project-level interrogation. - **Operational clarity** A cleaner public product architecture, explicit source-to-claim traceability, and clearer governance mechanics would contrast well against Quals.ai's somewhat uneven public site. ## Practical takeaways 1. **Treat Quals.ai as a real qual competitor, not a generic AI wrapper.** The combination of real participants, AI moderation, voice, and automated reports is commercially meaningful. 2. **Do not ignore the embedded loyalty-app wedge.** This is the most strategically interesting part of the offer because it turns existing customer bases into continuous qualitative infrastructure. 3. **Sharpen EmpathyIQ's qual story around trust, not only speed.** Quals.ai already owns a simple speed story. Our advantage should be stronger proof, governance, and connected evidence. 4. **Make the real-human stance explicit where relevant.** Quals.ai's anti-synthetic positioning is clear and likely resonant. EmpathyIQ should be precise about when it uses real respondents, when AI assists, and where evidence comes from. 5. **Watch the Trellist partnership.** The exclusive U.S. route could make Quals.ai more visible in agency and enterprise conversations during 2026. ## Bottom-line assessment As of **May 6, 2026**, Quals.ai looks like a credible, practical competitor in AI-assisted qualitative research. It is most threatening where buyers want: - fast qual studies - real participants - voice or text AI interviews - automated analysis - service support - brand / UX / CX / concept feedback It is less threatening, based on current public evidence, where buyers need: - formal research-governance workflows - transparent questionnaire or respondent-quality logic - quant + qual operating-system breadth - cross-study institutional memory - deep enterprise insight-repository capabilities The competitive response should not be to out-speed Quals.ai on qual alone. It should be to make EmpathyIQ's broader promise sharper: faster research that is also more reviewable, better governed, and easier to defend when the decision matters. ## Open questions to revisit later - What is current Quals.ai pricing by package, project, participant, and feature tier? - How automated is recruitment / screener selection in live customer usage? - How robust are fraud checks and respondent verification in practice? - Does SmartAsk expand into cross-project or enterprise-wide research memory? - How much U.S. traction does the Trellist partnership generate during 2026? - Does the embedded loyalty-app product become a major strategic wedge or remain a case-study niche? ## Source pointers - Web research note: [competitive/source-material/quals-ai/quals-ai-web-research-2026-05-06.md](../source-material/quals-ai/quals-ai-web-research-2026-05-06.md) - Core public URLs: - - - - - - - - - -