Inqueria

Inqueria is a next-generation qualitative research platform built around AI-moderated interviews. Instead of asking a researcher to write a discussion guide and sit through every conversation personally, Inqueria lets a team describe a research objective in plain English and then runs the interviews itself, following adaptive conversational paths with each participant. It is designed for people who need to understand why users behave the way they do — why they churn, why they choose an alternative, or where onboarding breaks down — and who need that understanding at a scale a single moderator cannot reach. The platform's stated promise is direct: run 50 deep interviews overnight rather than spending the month it takes to schedule five. Traditional qualitative research is slow and manual by nature. A human researcher runs one interview at a time, so studies queue behind calendars, participants have to be recruited and scheduled one by one, and weeks or months can pass before a single insight surfaces. Analysis is just as heavy, because transcripts have to be coded by hand before themes emerge. Surveys sit at the opposite extreme: fast and broad, but unable to probe a thin answer, chase an unexpected thread, or capture the hesitation behind a response. Inqueria is positioned in that gap. It offers the conversational depth of an interview without a scheduling queue, and it replaces manual transcript coding with one-click synthesis, so teams can start learning instead of waiting for a study window to open. The site frames the shift simply as: stop manual transcript coding, start learning. The first step is research design, which happens in minutes rather than days. A team member describes the research objective in plain English — for example, "Understand why new users churn within their first week, and what would have made them stay" — sets the audience such as recently churned users, and picks a tone such as warm and professional. Inqueria then generates a complete question plan, including a system prompt, follow-up probes and an ideal conversational flow, ready to share in under five minutes. The site lists average setup time at five minutes with instant AI generation, and the product emphasises that there is no scripting and no guesswork involved. Rather than writing a guide from scratch, the researcher reviews and works from the plan Inqueria produced from a single sentence of intent, such as an eight-question plan built from one churn objective. The second step is adaptive interviewing. Participants join through a secure link and speak directly with Inqueria. The AI moderator listens to each answer, probes deeper when a response is thin, and follows threads the researcher did not anticipate — behaviour the company describes as interviewing users the way a trained researcher would. No human moderator is involved in the conversation itself. The product demo shows a participant being asked, on question 3 of 8, to walk through the specific moment they realised onboarding was not working for their team, inside an anonymous session where they can type or speak their response. Crucially, these sessions run concurrently rather than in sequence. Inqueria advertises unlimited parallel capacity, meaning as many interviews as your plan allows can be conducted at the same time, with no calendar to fill and response limits that apply by plan. The third step turns those conversations into a research library. One click surfaces cross-session themes, sentiment and verbatim quotes, and then goes further: cross-study patterns, theme saturation and response-level engagement signals. The synthesis view shown on the site, drawn from 148 sessions, displays top themes with prevalence figures, a net sentiment score, an engagement signal noting that 23 respondents described setup as "fine" but hesitated and backtracked while explaining it, a compounding indicator showing a theme also appeared in three past studies, and a saturation marker showing the point at which themes stopped changing. Because every study adds to the research library, patterns surface across all of a team's work rather than being trapped inside a single project file. Themes stay evidence-linked: Inqueria traces each one back to the exact quote it came from and checks the theme against every transcript, showing you the participants who disagree. Privacy is built into the pipeline rather than bolted on afterwards. Inqueria performs automated PII redaction in-house, detecting and stripping names, emails, phone numbers, IDs and addresses before any data leaves the platform — the site illustrates a raw input such as "My name is John and I work at Apple" becoming "My name is [REDACTED] and I work at [REDACTED]". Because redaction happens before any model sees a transcript, sensitive details never reach the AI layer, and data-retention policies are configurable. On the methodology side, Inqueria does not treat every study identically. Describe an objective and it recommends the best-fit method from a rigorous toolkit, then tells you why it chose it, and you can override the choice at any time. The named methods include Jobs-to-be-Done, Laddering, Critical Incident, Journey, Evaluative, Phenomenological and Semi-structured. The site describes this as methodological rigor: the right method, chosen for you. Overall, the approach is adaptive, concurrent and evidence-linked. A study begins with an objective rather than a rigid script, runs as many simultaneous conversations as the plan allows instead of one at a time, and finishes in a synthesis layer that ties every theme to the quotes underneath it. Inqueria summarises its own output as conversational, cross-study and evidence-linked qualitative research, where the research library compounds with each new study rather than sitting as a folder of stale transcripts. Interviews adapt to every answer, and the analysis is checked against every transcript, so the themes are not just generated but validated against the raw material and traced back to the participants who produced them. The practical benefits follow from that structure. Setup takes under five minutes on average, so a study can be designed and shared the same day the question is asked. Because interviews run concurrently, a team can reach volumes that would be impossible for a human moderator working one session at a time — the site's framing is running 50 interviews overnight versus skipping the month it takes to schedule five. One-click thematic synthesis removes the manual coding stage, and because each theme carries its verbatim quotes and its prevalence figure, findings arrive with the evidence attached. Engagement signals flag responses where wording and delivery diverge, and saturation markers show when additional interviews stop adding new themes, which helps teams judge when they have heard enough. Privacy redaction lets organisations collect candid feedback without exposing participant identities, and the accumulating library means each study makes the next one more valuable. Inqueria lists strategic use cases across several kinds of qualitative discovery: Customer Discovery, Churn & Retention, Market Validation, Concept & Packaging, Brand Perception, Employee Experience, Academic Research, Community & Policy, and "something else entirely" for work outside those categories. Customer Discovery is described in the most detail: uncovering the jobs, triggers and switches behind why people choose you, or don't, with an example question such as "When did you first realise the alternatives weren't solving your problem?" The Churn & Retention category maps directly to the churn example used throughout the site, where recently churned users are interviewed about the moment the product stopped working for them and what would have made them stay. These categories describe the kinds of studies the platform is presented as built for. For target users, the site says Inqueria is used by top product teams at fast-growing startups, and its pricing tiers point to three further audiences. Research is aimed at freelance researchers and UX teams. Consultant is built for consulting teams sharing findings with clients, and adds a client read-only insights share link plus the ability to remove Inqueria branding from the participant page. Scale is for larger corporate teams and includes five team seats, while Enterprise covers unlimited interviews, studies and seats with SSO, security review and dedicated support. Pricing is presented as being based on outcomes rather than features. Explore is free, with no card needed, one active study, three interviews per month, the qualitative AI agent and 10 insight refreshes monthly. Research is $55 USD per month for 30 interviews with unused allowance rolling into the next month, 5 active studies, thematic synthesis and sentiment, 15 insight refreshes, Excel spreadsheet export and a custom AI interview persona. Consultant is $109 USD per month for 75 interviews, 10 active studies, 30 insight refreshes, the client share link and branding removal. Scale is $169 USD per month with 5 team seats, 150 interviews per month, unlimited active studies, 50 insight refreshes and personalised email distributions. Enterprise is custom, and a one-off study pack of 20 interviews is available for $35 USD with no expiry. GST is added at checkout for Australian customers, with AUD billing. In summary, Inqueria replaces the slow, manual loop of scheduling interviews and hand-coding transcripts with an AI-moderated research process that designs the study, runs every conversation concurrently along adaptive paths, redacts PII before any model sees the data, and synthesises themes that stay linked to the exact quotes behind them. Its primary value proposition is depth at scale: fifty interviews overnight, evidence-linked findings checked against every transcript, and a research library that gets more useful with each study.