Credits Calculator

AI‑conducted interviews allow you to scale qualitative research without requiring a researcher’s time in every conversation. The Cardus interviewer follows a structured yet adaptive flow, focusing on real work experiences rather than generic survey answers.

Beyond saving time in data collection, AI interviews make it viable to involve dozens or even hundreds of participants with consistent quality, enabling research scopes that would otherwise be impractical.

Calculator assumptions: 1h saved per AI interview (replacing human interview time) + ~15 narratives per AI interview for cost estimation; automated extraction + clustering replace ~1 additional hour of manual work per AI interview.

In some contexts, engaging stakeholders through face‑to‑face interviews is essential. Cardus.AI transforms this material into structured narratives ready for analysis and seamless integration with AI‑conducted interviews.

It also lets you reuse existing material, such as focus groups, or historical conversations, this avoids manual coding, saving time and allowing teams to incorporate different collection methods into the same project.

Calculator assumptions: ~20 narratives per imported interview for cost estimation; automated extraction + clustering replace ~1 hour of manual work per imported interview.

Anonymization and rewriting protect people, teams, and organizations by removing sensitive information while preserving the original meaning of each narrative.

If you plan to share detailed research content — such as individual narratives — with broader audiences or leadership teams, this step becomes essential and significantly reduces the manual effort and risk involved in reviewing sensitive qualitative data.

Calculator assumptions: Manual anonymization or rewriting takes ~30s per narrative and is applied uniformly to all narratives when enabled.

Cardus.AI generates qualitative metrics from narratives, such as emotional tone and level of abstraction. These metrics support decision making by complementing human interpretation.

One typical use case is prioritizing intervention focus on themes or clusters that have strong negative emotions and have been mentioned by more people.

They also enable comparisons across clusters, time periods, or projects, helping researchers move beyond anecdotal evidence toward more structured qualitative insights.

Calculator assumptions: Manual metric generation takes ~30s per narrative and is generated for all narratives when enabled.

Narrative classification adds an analytical layer to each story, enabling you to assess narratives using a chosen research framework, model, or set of dimensions defined by the researcher.

Over time, this structured classification enables benchmarking across research cycles, teams, or organizational units, making it possible to compare results months later and observe meaningful change instead of isolated insights.

You may not always need classification: Cardus automatically clusters all narratives in a project to reveal emerging themes, and interviews can be categorized without consuming any credits.

Calculator assumptions: Manual classification takes ~30s per narrative and is applied to all narratives when enabled.

Readers have view‑only access to the project Dashboard and analytical chat. They can explore findings, ask questions, and request comparisons without changing any data.

This reduces the time spent preparing presentations, responding to follow‑up questions, and explaining results repeatedly, while giving stakeholders autonomy to explore insights at their own pace.

Since a Reader does not need a paid account, you can share results with stakeholders and organizational leaders without additional setup or licensing overhead.

Calculator assumptions: Preparing dashboards, sharing results, and answering follow-up questions takes ~2h per Reader;