Frequently asked questions

Candor is a synthetic research platform for deciding what to build, ship, and price with evidence, not instinct. It builds synthetic users grounded in real evidence, combining your uploaded research (when you have it), web data, and validated behavioral models to create personas with realistic personality traits, cognitive biases, and decision-making patterns. Use it for problem discovery, concept testing, value-prop testing, price testing, and assumption validation, on new products and live ones. You get decision-grade signal the same day, where a panel study would take weeks.

Most AI persona tools generate fictional characters from a prompt. Candor starts with evidence (your documents, web research, validated behavioral data) and builds personas from the ground up. Every trait carries a provenance tag showing exactly where it came from: grounded in your research, inferred from behavioral patterns, calibrated from peer-reviewed distributions, or flagged as low confidence. A critic agent validates every interview response for consistency before you see it.

Candor is built for the teams that own research demand. UX research leaders use it to scale their research capacity when their team can run one study a week and the PMs need five. Customer and consumer insights leaders use it to pressure-test concepts and pricing without burning panel budget. Innovation teams use it when a stage gate is approaching and traditional research would take too long. Consultants and agencies drop it into client engagements as a fast-validation layer. Startups use it to test assumptions before committing engineering resources. You don't need to be a trained researcher to run a study.

Candor uses a multi-stage pipeline, and the order matters more than people expect. It reads your audience description and any uploaded documents, then proposes candidate segments along behavioral and psychographic dimensions rather than demographics alone. Only then does it search the web, gathering evidence about those specific segments and extracting signals as it goes. That is why a sharp audience description changes the whole result: it is the input to the first real decision the pipeline makes. From the settled segments it builds archetypes, samples individual personas with OCEAN traits and cognitive biases drawn from real population distributions, and writes narrative memory for each one.

Evidence-grounded means every persona attribute traces back to a source. When Candor says a persona has high status quo bias, you can see whether that came from your uploaded customer interviews, from web research about the target market, from peer-reviewed personality distributions, or from inference. Nothing is made up without acknowledgment. Attributes flagged as low confidence are explicitly marked so you know what to trust and what to probe.

Provenance tagging is Candor's system for tracking the origin of every persona trait. Each attribute carries one of five tags: grounded (cited to one specific source), inferred (synthesised across two or more sources), calibrated (derived from validated behavioral or population distributions), sampled (drawn from one of those distributions, which is how OCEAN values and bias intensities are set), or weak confidence (an explicit hypothesis with no evidence behind it). This gives you a complete audit trail from any finding back to its source.

Candor offers two interview modes. In live mode, you type questions and the persona responds in real time. It feels like a natural conversation. In auto-interview mode, you configure an interview guide with sections and learning goals, and Candor conducts the interview automatically, tracking coverage and adjusting questions as it goes. Both modes use the same persona inhabitation system, and a critic agent validates every response for consistency.

Candor synthesises every transcript into a report, automatically, as soon as the last interview finishes. The pipeline runs a different sequence depending on the interview type you chose, because a hypothesis verdict and a willingness-to-pay range are not built the same way. What you read is consistent: four sections, opening with what matters most and the learnings paired with the moves they imply, then the findings, then the next steps, then an evidence appendix holding the supporting detail and the full transcripts. Every claim links back to the quotes behind it.

Personas are built on Big Five (OCEAN) personality traits sampled from peer-reviewed population distributions, calibrated by region and occupation. Cognitive biases are assigned with research-backed intensity values. Each persona has narrative memory covering their identity, behaviors, beliefs, and communication style. They remember everything across interview sessions, maintain consistent views, and push back when they disagree. They don't drift into generic AI agreeableness.

Every persona response passes through a critic validation step before you see it. The critic checks for hard contradictions (the persona said something that directly conflicts with an established belief or prior statement) and soft tensions (a notable shift in enthusiasm without a clear reason). Hard contradictions trigger regeneration. Soft tensions are flagged in metadata but still delivered, because real people have variable moods too.

Candor uses the Big Five (OCEAN) model for personality: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Trait values are sampled from cross-cultural population distributions published in peer-reviewed research. Cognitive biases are modeled as first-class traits with intensity values from 0 to 1, drawing from established behavioral science. B2B and B2C personas use different attribute models reflecting distinct decision-making frameworks.

Yes. Every persona attribute carries a provenance tag. In the persona detail view, you can see which traits are grounded in your uploaded research, which were inferred from behavioral patterns, which were calibrated from validated distributions, and which carry low confidence. The synthesis report traces every finding back through themes to specific interview quotes to the personas and archetypes that generated them.

Four interview types, and concept testing splits into four kinds of its own. Problem discovery explores unknown problems in a market. Problem validation tests whether your hypothesized problems are real. Concept testing evaluates a product concept: what resonates, what needs rethinking, and who it's for. Price testing examines value perception gaps, anchoring effects, and willingness-to-pay ranges. Inside concept testing you pick a concept test type. A standard test shows whole concepts cold and compares them. A name test, a value proposition test, and a messaging test each expose one anchor concept first and then present the candidate names, benefits, or headlines inside that context, so nobody judges a name in a vacuum. Assumption validation layers per-assumption verdicts onto any study type. See the use cases page for the full set.

Yes. You can upload PDF, DOCX, CSV, or TXT files: customer interviews, survey data, market reports, or any research you have. Candor parses, chunks, and indexes these documents, then uses them as the primary evidence source for audience generation. Your documents are prioritized over web research, and signals extracted from them are tagged with full provenance so you can always trace a persona trait back to your original data.

About an hour or two, most of it in the background. Audience generation takes 25 to 35 minutes and is queued, so you can close the tab. Persona generation runs another 7 to 12 minutes after you approve segments. Interview guides generate in a couple of minutes. Auto-interviews run 25 to 35 minutes in parallel across your personas, and live interviews happen in real time. Synthesis adds 3 to 5 minutes once the last interview finishes. Your own attention adds up to well under an hour of that, spent reviewing segments and reading the report. You go from a research question to a structured report the same morning, instead of the six weeks a panel-driven concept test would take.

Yes, and it treats them as fundamentally different. A procurement lead evaluating enterprise software uses a different decision framework than a consumer making an impulse purchase. Candor models B2B and B2C personas with distinct attribute schemas, personality weightings, bias profiles, and buying triggers. You choose your audience type when creating a study, and the entire pipeline adapts accordingly.

Your data is stored in Supabase (hosted on AWS in the US) and is never shared with other customers. Uploaded documents are used only for your studies. Data processing happens through secure APIs with industry-standard encryption in transit and at rest. Candor's full list of third-party data processors is published on the subprocessors page, updated whenever vendors change.

No. Your research data, uploaded documents, and interview transcripts are not used to train any AI models. Candor uses commercial AI APIs for inference only. Your data is processed and returned, not retained for model training. This applies to all third-party AI providers in the stack.

After every persona response, a separate AI model reviews it against the persona's established beliefs, prior statements, seed decisions, and stance change history. It checks for hard contradictions (directly conflicting with something the persona said or believes) and soft tensions (a shift in tone or enthusiasm without a clear reason). Hard contradictions are rejected and regenerated. Soft tensions are flagged but delivered, because real people have variable moods.

Candor samples Big Five personality traits from peer-reviewed cross-cultural research, including Schmitt et al. T-score distributions across 56 nations and Anni et al. occupational personality profiles. Regional and occupational distributions are used when available. When occupational data doesn't exist for a specific role (about 57% of occupations), Candor falls back to regional norms. No traits are assigned randomly.

When you upload research documents, Candor parses, chunks, and indexes them. During audience generation, signals extracted from your documents are tagged as 'grounded' with a reference to the source file. This provenance carries through to persona attributes, interview behavior, and synthesis findings. You can trace any persona trait or report insight back to the specific document that informed it.

Synthetic research is not a substitute for talking to real people. It's a complement. Candor's evidence grounding, personality calibration, and consistency enforcement make its output more structured and traceable than prompting a generic AI. But synthetic personas can't replicate genuine emotional responses or surface truly unexpected insights the way a real conversation can. Use Candor to sharpen your hypotheses and identify where to invest real research budget.

Join the waitlist at runcandor.com. Candor is currently in development and will launch with early access for waitlist subscribers. When you get access, you'll create a study by describing your target audience, optionally uploading research documents, and choosing your study type. From there, Candor handles audience generation, persona creation, and interview facilitation.

Candor is in active development and not yet publicly available. Join the waitlist to be notified when it launches. Early access will be offered to waitlist subscribers first. Candor is built by Highline Beta, based in Toronto, Canada.