What Is Conveo?
Conveo is a Belgian Y Combinator-backed AI video interview platform built around multimodal signal extraction, with a recent $5.3M raise behind the bet. The company’s defining commitment is an ESOMAR-informed methodology applied across async AI-moderated video sessions, where signal flows out of every interview through voice, video, tone, facial expression, emotional nuance, and contextual objects on camera. Eight integrated panel partners — Respondent, User Interviews, Norstat, Bilendi, Sago, Rakuten, Forsta, and Rally — plus BYOC paths via CSV upload, external panels, QR codes, and WhatsApp invites span the audience side. Fifty-plus languages are supported on the moderation layer, with multi-market global benchmarking framed as a core use case.
Inside the broader AI-led qualitative category, Conveo occupies a methodological lane that’s distinct from its peers. Chat-first platforms cluster frequency patterns from text responses. Audio-first adaptive platforms reach motivational architecture through systematic laddering on every conversation. Deterministic platforms produce standardized comparable artifacts by running every participant down the same predetermined question track. Conveo’s lane is none of those: the conversation architecture is async video, but the analytical surface area is intentionally broader than any single-modality peer. The platform is built to read across what participants say, how they say it, what their faces do while they say it, and what sits next to them on camera.
The multimodal signal extraction layer. What this methodology surfaces is what shows up across all signal types when verbal response and revealed reaction diverge — the micro-expression that contradicts a positive statement, the tonal flatness that undercuts an enthusiastic word, the visual cue that signals lived context the participant didn’t verbalize. The optimization target is concept testing, creative validation, and multi-market benchmarking — research where reaction is the artifact and where multimodal evidence answers the question. The trade-off is motivational depth: identity-level driver discovery from a single research instrument is a different methodological problem, and signal extraction across modalities is not the same instrument as systematic adaptive laddering inside one modality. Beyond the multimodal layer, Conveo carries the most formal research methodology in the cohort — survey-grounded structure with MaxDiff analysis that stack-ranks coded drivers by importance, which makes it attachable to rigorous, quant-style research designs in a way most adaptive-interview platforms are not. The rest of this review evaluates Conveo across five buyer-care dimensions (speed, cost, depth, scale, insights), then how User Intuition approaches the same dimensions, then security diligence and a decision framework.
How Fast Does Conveo Deliver Results?
Conveo’s delivery clock is shaped by two forces pulling in opposite directions. The async video format compresses the participant side dramatically — there are no live calendars to align, no moderator schedules to chase, no time-zone juggling across geographies. Participants record when it suits them, and recordings flow into the multimodal analysis layer as they land. The countervailing force is the procurement side: panel-partner access and Enterprise contracting set the runway before any interview gets fielded.
End-to-end question-to-answer time falls into three regimes depending on where a buyer stands when the research question arrives.
Established Conveo Enterprise account with panel-partner access in place: roughly 1-2 weeks from kickoff to themed deliverable. Study setup runs in parallel with recruitment through whichever integrated partner fits the audience profile, async video sessions field over several days, and the multimodal synthesis layer produces extracted themes against the recorded corpus. The credit pool is already sized and the panel relationships are settled, so the bottleneck is fielding velocity rather than setup overhead.
Pay-as-you-go agency engagement: variable depending on panel-partner negotiation and scope. PAYG is project-based and sales-led — no annual contract amortizes the panel-access conversation, so each engagement re-opens that scoping. Agencies running recurring PAYG cycles against a stable audience footprint compress this; one-off projects against unfamiliar geographies extend it.
New Enterprise buyer, no contract: 4-8 weeks from first sales conversation to first themed deliverable. Sales cycle, scoping, procurement, panel access setup, and credit-pool sizing all stack ahead of the first study, with the first interview waiting on the last of those to clear.
When the speed model fits. Established Enterprise accounts running continuous multi-market concept testing or benchmarking research where panel partner relationships are settled and the credit pool is sized to the team’s actual cadence. The format’s compression of the participant clock translates into real velocity once the procurement clock has already been spent.
What Does Conveo Cost?
Conveo does not publish self-serve pricing on its website, and there is no free trial. The pricing references that exist come from buyer-reported sources — G2 reviews, Capterra, GetApp, TrustRadius, Software Advice, and 2025-2026 RFP analyses — and they consistently describe a dual-tier structure.
The pay-as-you-go option is positioned for agencies and project-based work. Rates vary by scope; activation is still sales-led rather than a self-serve checkout. The Enterprise plan starts at approximately $45,000 per year, structured around prepaid credits priced against total interview minutes, with lower per-minute rates available at higher annual volumes. The credit pool is sized at contract signature against the team’s expected cadence, and renewals re-scope based on actual consumption. Targeting skews mid-market and enterprise, with 200+ employee organizations the typical buyer profile.
For the full cost-by-frequency math at 1, 5, 10, 20, and 50 studies per year — including the PAYG-versus-Enterprise crossover and what the credit-pool architecture does to effective per-study cost at different cadences — see the Conveo pricing breakdown.
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How Deep Does Conveo Go in Each Interview?
Depth on Conveo is not a runtime decision. The platform’s multimodal-signal-extraction architecture sets the depth profile upfront — what gets captured, what gets analyzed, and what shape the resulting evidence takes are all consequences of the methodological commitment.
Moderator behavior. The async AI moderator runs the discussion guide and prompts participants through structured questions; participants record video responses on their own time. Inside each response, the signal extraction layer captures voice, video, tone, and facial cues. The probing question inside an async video format is whether the moderator follows up adaptively when a participant says something unexpected, or moves the guide forward to keep the recording on time. Conveo’s methodological emphasis sits on the extraction layer downstream rather than on within-session adaptive probing — the platform is built around reading the captured signal closely, not around restructuring the conversation in flight based on what a participant just revealed.
Signal extraction. Multimodal analysis surfaces what shows up across voice, video, tone, and facial expression. The reading runs over the full recorded corpus, with theme synthesis combining the signal types. For concept testing where revealed emotional reaction diverges from stated preference, this layer earns its keep — micro-expressions and tonal confidence shifts genuinely reveal what verbal response can hide, and a discomfort signal in the face during an otherwise positive answer is a finding the platform was purpose-built to catch. For motivational research where the question is the identity-level driver beneath a behavior, signal extraction surfaces the surface — what appeared across modalities at the moment of response — while systematic laddering reaches what’s beneath that surface across multiple structured exchanges within the same conversation.
Synthesis behavior. Themes combine the multi-signal evidence into output that pairs extracted theme statements with the multimodal video clips that support them. The output shape suits concept-validation deliverables and stakeholder communication: a stakeholder watching a packaging-reaction clip with the platform’s tonal-shift annotation can see and hear the evidence at once, which lands well in presentation contexts and brand-side decision meetings. One gap worth probing in the demo: the report does not expose a direct path back to the source transcript, so verifying a synthesized claim against exactly what a participant said can take extra steps — a real consideration when the deliverable has to withstand scrutiny.
When depth is Conveo’s strength. Concept testing, creative validation, and multi-market benchmarking where multimodal signal extraction surfaces what verbal response hides. The decision space is “does this resonate emotionally” — does the ad land, does the packaging variant trigger the intended response, does the positioning feel right across faces in three markets — and the platform’s signal layer is purpose-built for that question.
How Does Conveo Scale to Your Research Volume?
Conveo’s scaling shape is determined by three architectural pieces fitting together: the 8-partner panel integration, BYOC recruitment, and the Enterprise credit-bundle commercial model.
Audience scaling. The 8-partner integration is a distinctive approach in the category. Respondent, User Interviews, Norstat, Bilendi, Sago, Rakuten, Forsta, and Rally collectively span global B2B audiences, consumer geographies, and specialized panels — no single one of those panels covers what the eight together cover. BYOC supplements the integrated stack with CSV upload, external panel feeds, QR-code distribution, and WhatsApp invites. For consumer benchmarking that needs reach across France, Germany, the UK, and Japan in the same study, the partner stack is genuinely broad. The integration economics deserve a scoping question — partner access is sometimes bundled into the Enterprise contract and sometimes priced per partner depending on the audience profile, and the answer materially changes the all-in cost of a multi-market study.
Frequency scaling. The Enterprise tier is built on credit-bundle architecture sized at contract signature. The $45K+/yr floor amortizes well when research cadence is continuous and high — twenty-plus studies in a year against a credit pool sized for that volume produces favorable effective per-study economics. The same floor amortizes poorly at low cadence: a team running three studies in a year against the Enterprise floor pays a much higher effective per-study cost than the credit-pool math implies, since unused credits do not always carry forward and the floor is fixed regardless of consumption. PAYG exists as an option for agencies and project-based work that doesn’t want the annual commitment, but PAYG project rates are higher per-minute than Enterprise rates at any meaningful volume.
Team scaling. Procurement-driven, with Enterprise contracts and PAYG project scoping both routed through sales conversations rather than self-serve account creation. The dual-tier model creates a different entry point for agencies (PAYG) than for enterprises (Enterprise contract), but neither path looks like the distributed-self-serve model where product managers, marketing leads, and CX directors spin up their own studies inside an existing seat. The platform’s center of gravity is research-led procurement, not horizontal-distribution adoption across non-research functions.
When scale is Conveo’s strength. Enterprise multi-market benchmarking practices running continuous concept testing or creative validation research where the 8-partner footprint covers the audience reach and the credit pool amortizes across consistent annual volume.
How Useful Are Conveo’s Insights — and Do They Compound?
Conveo’s per-engagement output is a synthesized theme set paired with the multimodal video clips that support each theme. The question worth asking is whether that output compounds — whether the third engagement learns from the second and the first, or whether each engagement is a standalone deliverable that lives and dies inside its scoped output package.
Per-project insight quality. On what the format is built for, the output is strong. Multimodal evidence of how participants emotionally reacted to stimuli — the face during the ad reveal, the tone shift when pricing came up, the body-language change during the competitor comparison — pairs with theme synthesis to produce concept-validation deliverables that land well in agency presentations and enterprise consumer-insights cadences. The clips themselves are the evidence, and the theme synthesis is the argument the clips support. Brand-side stakeholders watching the output package see the participant’s reaction directly rather than reading a researcher’s gloss of it.
Insight compounding. Per-engagement is the default output unit. A cross-study queryable corpus that lets a buyer ask plain-language questions across all prior research is not the platform’s published architectural promise, and the asset shape treats each engagement as its own scoped deliverable. How cross-project querying works — whether prior themes are retrievable, whether the multimodal evidence can be searched against new questions, whether earlier studies index against later ones — is a verify-in-scoping question.
Concrete example: if January’s concept test surfaced positive facial reactions to a packaging variant, and March’s brand benchmark captured tonal confidence about the same brand, you cannot ask “do positively-reacting concept-test participants show different brand confidence in subsequent benchmark waves” against the existing corpus without re-opening both studies as discrete deliverables and reading across them manually. The asset model is built for the engagement, not for the longitudinal question.
When the insight model works. Per-engagement deliverables for concept-validation and benchmarking audiences where the multimodal-evidence package is the artifact the stakeholder consumes. The output earns its keep inside the scope of the study it was commissioned for.
How Does User Intuition Approach the Same Dimensions?
User Intuition runs an adjacent lane of the AI-moderated qualitative category — adjacent in that both platforms run AI-moderated research at category-defining quality, but distinct in the methodological commitment that shapes every other design decision. Conveo’s commitment is multimodal signal extraction across each response. User Intuition’s commitment is systematic adaptive laddering on every interview — 5-7 levels deep, every conversation, from concrete behaviors through functional benefits to emotional drivers and identity markers. The research-question pivot between the two is why (UI’s target) versus what shows up across signal types (Conveo’s target). Both questions matter. They lead to different platforms.
Speed
Where Conveo’s end-to-end runway is gated by Enterprise procurement, panel-partner setup, and credit-pool sizing — a new Enterprise buyer waits 4-8 weeks to first themed deliverable — User Intuition operates on a self-serve clock that starts at signup. Three free interviews on signup, no credit card, no procurement conversation. Study setup runs in about five minutes through guided study design. Recruitment fires immediately against the included 4M+ vetted panel or against your own customers via CRM integration. The 24-hour end-to-end window covers signup through themed results on most studies, and a 200-300 interview study against the included panel typically lands inside that envelope.
The speed difference is not marginal — it’s structural. The Conveo Enterprise-procurement clock and the User Intuition self-serve clock are different shapes. A buyer evaluating both this week can compare procurement-call schedules against a working set of three interviews already returning themes. For multi-market benchmarking inside an established Enterprise relationship, Conveo’s compression of the participant clock is real and valuable; for the time-to-first-finding that most buyers are actually evaluating, the gap measured in weeks versus hours is decisive.
Cost
Where Conveo’s economic floor is ~$45,000/yr for Enterprise (or sales-led PAYG rates per project), User Intuition publishes per-study pricing that scales linearly with research cadence. Audio interviews run $25 on the Pro plan, video $50, chat $12.50, with studies starting at $150 for a 5-interview audio study. No annual base, no seat fees, no procurement floor, panel included.
The arithmetic at the Enterprise floor makes the operating-model difference vivid. The $45,000 that funds a Conveo Enterprise year covers approximately 2,250 audio interviews on User Intuition’s Pro plan — roughly 225 ten-interview studies, or somewhere on the order of a full year of weekly research across product, marketing, and CX combined. Whether that comparison matters depends entirely on what the research deliverable is. If multimodal signal extraction across eight panel-partner geographies is the artifact, the credit pool is what funds it and the floor is the price of access. If motivational depth via adaptive laddering is the artifact, the same dollars buy radically more research and the floor is the price of an operating model that doesn’t match the research need.
Depth
Where Conveo’s depth runs through multimodal signal extraction — the what across voice, video, tone, facial, emotional, and contextual modalities at the moment of response — User Intuition’s depth runs through adaptive 5-7 level laddering — the why underneath the pattern, surfaced through systematic conversational architecture across each interview.
These are different optimization targets, not better-versus-worse outcomes. Conveo answers “did this resonate emotionally” by reading the reaction signal across modalities. User Intuition answers “why does this resonate emotionally” by laddering from the participant’s concrete behavior toward the functional benefit it produces, then toward the emotional consequence of that benefit, then toward the identity-level value the emotional consequence connects to, then back to verify the connection holds. The AI moderator adapts in flight — when an answer is shallow, it probes; when a participant stalls, it redirects; when a thread is more interesting than the next planned question, it follows the thread before returning to the guide.
Both methodologies are real and distinct. For concept testing where a face during stimulus reveals more than a verbal response after stimulus, multimodal extraction is the right shape. For motivational research where the question is the identity-level driver of a behavior — why customers churn, why positioning fails, what brand value drivers actually predict purchase — adaptive laddering across one signal type reaches what extraction across modalities at one point in time does not.
Scale
Where Conveo’s scale runs through eight integrated panel partners plus BYOC — broad multi-source recruitment with negotiated partner-by-partner access — User Intuition’s scale runs through a 4M+ vetted panel included with every account plus native CRM recruitment of your own customers in the same workflow.
Both architectures are real options for different audience profiles. The Conveo footprint reaches geographies and specialized audiences that no single panel covers, which earns its keep when the audience profile is multi-market consumer benchmarking or specialized B2B segments distributed across the partner stack. The User Intuition footprint includes the panel as table-stakes and adds your-own-customers recruitment via HubSpot integration (Salesforce and Pipedrive via Zapier) without a separate tool or partner conversation. Hybrid studies — panel plus your customers in the same research workflow — run natively. For audience profiles that map to “panel-reachable consumers or our own customer base,” the single included panel plus CRM-native recruitment is the simpler operating model. For audience profiles that require eight panel partners’ worth of geographic and segment reach, Conveo’s stack is the broader one.
Insights
Where Conveo’s insight output is per-engagement — multimodal video clips paired with extracted themes for the scope of the study commissioned — User Intuition’s insight output flows into the Customer Intelligence Hub, an ontology-indexed corpus queryable in plain language across every study the team has ever run.
The compounding question separates the two models. Each User Intuition interview gets parsed against the ontology at completion, indexed across the same conceptual map every prior interview indexed against, and made available to plain-language queries that read across the full corpus. A question asked in October — “what are the most common reasons customers in the $50K-$100K segment churn within the first 90 days?” — runs against every study the team has run since their first interview, not just the most recent engagement. Findings from January’s churn study compound into June’s positioning study compound into September’s competitive research. The asset is the corpus, not any single deliverable.
The output shape difference matches the methodological-commitment difference. Conveo’s evidence package is the engagement; User Intuition’s evidence package is the corpus.
Side-by-side at a glance
| Dimension | Conveo | User Intuition |
|---|---|---|
| Speed | 1-2 wks established; 4-8 wks new Enterprise | 24 hrs end-to-end from signup |
| Cost | ~$45K/yr Enterprise (PAYG project rates) | $25/audio interview, $250/10-interview study |
| Depth | Multimodal signal extraction (the what) | Adaptive 5-7 level laddering (the why) |
| Scale (audience) | 8 panel partners + BYOC | 4M+ included panel + CRM-native |
| Scale (frequency) | Credit pool against annual floor | Linear per-study, no floor |
| Scale (team) | Procurement-routed | Self-serve plus enterprise procurement |
| Insights (quality) | Multimodal video clips + extracted themes | Themed transcripts with identity-level depth |
| Insights (persistence) | Per-engagement deliverable | Ontology-indexed Customer Intelligence Hub |
| Public ratings | Limited public review presence | 5/5 G2, 5/5 Capterra |
| Free trial | None published; sales-led both tiers | 3 free interviews on signup, no card |
How Do Conveo and User Intuition Compare on Security and Compliance Posture?
Security has two distinct surfaces, and procurement teams that conflate them end up with gaps. Certification posture is the cert checklist — SOC 2, ISO 27001, HIPAA, GDPR, ESOMAR — and it answers whether the vendor has been independently audited against published frameworks. Data risk posture is where customer data actually flows — recruitment human touchpoints, export footprint, retention defaults, AI-training policy — and it answers what happens to participant audio and transcripts once the study completes.
| Surface | Conveo | User Intuition |
|---|---|---|
| Certification posture | Specific SOC 2 / ISO 27001 / GDPR status best verified in scoping demo. Belgian company and ESOMAR-informed methodology suggest EU privacy emphasis. | Mid-audit with engaged external auditors. ISO 27001-aligned posture today, SOC 2 Type 1 attestation in progress (H2 2026 target), GDPR and HIPAA alignment in place. |
| Sub-processor disclosure | Specific list verify-in-demo. | Covered in the security overview. |
| Participant PII surface | Eight panel partners means participant PII flows through multiple partner relationships, each with its own posture. BYOC paths (CSV, WhatsApp) add additional surfaces. | Included 4M+ panel runs against one published privacy posture. CRM-native recruitment of your own customers stays within your existing data perimeter. |
| Customer data export | Standard corporate sharing formats (PDF, PowerPoint, Slack) plus the multimodal video clip package as the native export shape. | Themed transcripts, verbatim quotes, ontology-indexed corpus query exports; data accessible inside the security overview. |
| AI training + retention | Retention defaults and AI-training policy verify-in-scoping. | AI-training-precluded policy on customer data, published retention defaults. |
The procurement profiles are different and not interchangeable. Conveo’s EU base and ESOMAR-informed methodology suggest a privacy-emphasis posture that maps well to regulated European research procurement, but the specific certification status — SOC 2 attestation, ISO 27001, sub-processor disclosure — is a verify-in-demo conversation rather than a published surface. User Intuition’s data-risk posture (included panel, public security overview, retention defaults, AI-training-precluded policy on customer data) is published and assessable in public docs before a procurement conversation begins. Different operating models for different procurement gates.
How to Choose Between Conveo and User Intuition
The choice between Conveo and User Intuition is a choice between two research methodologies — multimodal signal extraction or systematic adaptive laddering. Three lenses to orient.
Research question × methodology fit
| Research question | Best fit | Reason |
|---|---|---|
| Concept testing where emotional reaction is the artifact | Conveo | Multimodal extraction captures facial and tonal evidence verbal response hides |
| Creative validation across multi-market consumer segments | Conveo | 8-partner panel stack reaches the geographies; extraction layer reads reaction |
| Churn motivation, why-customers-leave research | User Intuition | Adaptive laddering reaches identity-level drivers beneath the behavior |
| Brand positioning and identity-driver discovery | User Intuition | Laddering surfaces the value architecture that predicts loyalty |
| Win-loss research with deep buyer reasoning | User Intuition | Systematic probing across each decision factor reaches the why behind the choice |
Research-frequency fit
| Annual cadence | Best fit | Reason |
|---|---|---|
| 1-3 studies/year, variable cadence | User Intuition | Per-study pricing with no annual floor; $45K Conveo floor amortizes poorly here |
| 5-15 studies/year, growing program | User Intuition | Linear scaling without contract re-scoping at every cadence change |
| 20+ studies/year, established continuous program | Either | Conveo Enterprise credit pool amortizes; UI per-study scales linearly — methodological fit decides |
Operating-model fit
| Operating model | Best fit | Reason |
|---|---|---|
| Self-serve evaluation before procurement | User Intuition | 3 free interviews on signup, no card, no sales call |
| Distributed adoption across product, marketing, CX | User Intuition | Per-study pricing and self-serve let non-research teams run studies |
| Enterprise-procurement-led consumer insights practice | Conveo | Enterprise contract maps to the operating context |
| Agency project-based work with variable scope | Conveo (PAYG) or User Intuition (per-study) | PAYG fits agency project rhythm; per-study fits flexible scoping |
Two-platform answer. Some orgs want both: UI for motivational research where customer-why drives strategic decisions; Conveo for concept testing and creative validation where multimodal reaction signal is the artifact. Most teams reading this review don’t need both — they need self-serve adaptive depth for the customer-why questions, not the multimodal-extraction layer.
Evaluation Questions for Your Conveo Demo
Use these questions in the scoping call before committing to an Enterprise contract or PAYG engagement.
Speed
- From signed Enterprise contract to first production study running, what’s the typical runway including panel access setup and credit-pool sizing?
- On PAYG, how does engagement-to-engagement scoping affect the front-end clock for an agency running quarterly cycles?
Cost
- How does the credit pool work — what counts against minutes (interview duration only, or includes processing and synthesis), what happens to unused credits at year-end, and how is overage priced relative to contract per-minute rates?
- Is the eight-panel-partner access bundled into the Enterprise contract, or priced per partner depending on which audiences the studies need?
Depth
- For research where the deliverable is motivational themes rather than multimodal video clips, how does Conveo’s analysis layer compare to native-AI peers built around systematic adaptive laddering on every conversation?
- Inside an async video session, when a participant says something unexpected, does the AI moderator follow up adaptively or move the guide forward? Show three example sessions where adaptive follow-up happened.
Scale
- For multi-market benchmarking across France, Germany, and the UK on the same protocol, which of the eight panel partners typically fields each market and what’s the cost variance across them?
- For BYOC recruitment via CSV or WhatsApp, how does signal-quality control compare to integrated-partner recruitment?
Insights
- How does cross-study querying work — can a buyer ask plain-language questions across all prior research, or are insights scoped to the engagement they were produced inside?
- Walk through three concrete examples where the multimodal signal extraction surfaced something a verbal-response reading would have missed.
Security
- What’s the current SOC 2 and ISO 27001 attestation status, and where can a procurement team see the most recent audit summary?
- What are the retention defaults on participant audio, transcripts, and multimodal extraction outputs, and what’s the AI-training policy on customer recordings?
- For European audiences sourced via integrated panel partners, what’s the GDPR data-flow architecture across the eight partner relationships?
Run these questions in parallel against three free User Intuition interviews. Comparative output is the cheapest way to know which model fits your team.
Three free interviews. No card. 5 minutes to launch. 5/5 on G2 and Capterra. Try User Intuition → · Compare Conveo vs User Intuition → · Conveo pricing reference → · 7 Conveo alternatives compared → · Migration guide →