Submission declined on 27 May 2026 by Bonadea (talk). This draft's references do not show that the subject meets Wikipedia's criteria for inclusion. The draft requires multiple published secondary sources that: provide significant coverage: discuss the subject in detail, not just brief mentions or routine announcements; are reliable: from reputable outlets with editorial oversight; are independent: not connected to the subject, such as interviews, press releases, the subject's own website, or sponsored content. Please add references that meet all three of these criteria. If none exist, the subject is not yet suitable for Wikipedia. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by Bonadea 2 months ago. Last edited by Bonadea 2 months ago. Reviewer: Inform author. Resubmit Please note that if the issues are not fixed, the draft will be declined again.

Submission declined on 13 May 2026 by Rht bd (talk). This draft's references do not show that the subject meets Wikipedia's criteria for inclusion for organizations and companies. The draft requires multiple published secondary sources that: provide significant coverage: discuss the subject in detail, excluding routine coverage like product launches, staff appointments, or financial reports and listings in databases or listicles; are reliable: from reputable outlets with editorial oversight; are independent: not connected to the subject, such as press releases, the subject's own website, or sponsored content. Please add references that meet all three of these criteria. If none exist, the subject is not yet suitable for Wikipedia. This draft reads like an advertisement. Wikipedia is an encyclopedia, not a platform for promotion or marketing. Drafts that are exclusively promotional may be deleted without notice. Wikipedia articles must be written neutrally in a formal, impersonal, and dispassionate way. They should not read like a blog post, advertisement, or fan page. Rewrite the draft to remove: promotional language: see Words to watch; personal commentary: opinions or direct addresses to the reader; informal language. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. If you have a conflict of interest (e.g. you are the subject, an employee, or a relative) or are being paid to edit, you must disclose this to comply with Wikipedia's Terms of Use. Declined by Rht bd 3 months ago.

• Comment: Resubmitting. The draft has been completely rewritten to adhere to NPOV and WP:NOTADVERTISING. All promotional language, pricing details, and software feature lists have been removed. The focus is now on the technical architecture, supported by independent academic papers (Ivey 2026; Anugraha 2026) that analyze the underlying technology. Lanceyuu (talk) 12:16, 26 May 2026 (UTC)

• Comment: In accordance with the Wikimedia Foundation's Terms of Use, I disclose that I have been paid by my employer for my contributions to this article. Lanceyuu (talk) 18:56, 13 May 2026 (UTC)

Qualitati is an artificial intelligence-assisted qualitative data analysis software (QDAS) and data collection platform developed by the Generative AI for Research Initiative. It integrates large language models (LLMs) to automate qualitative research workflows, including semi-structured interviewing, thematic analysis, and synthetic data simulation.^[1]

The core conversational technology underlying Qualitati is based on the MimiTalk framework, a dual-agent constitutional AI system introduced in 2025.^[2] Unlike traditional survey tools, this architecture utilizes two distinct models operating in tandem: a "supervisor model" responsible for strategic oversight and ethical compliance, and a "conversational model" tasked with dynamic question generation during participant interactions.^[2] This framework allows the software to conduct naturalistic interviews while attempting to minimize interview anxiety and maintain conversational coherence.

Qualitati operates through a web-based interface and is structured around several computational modules used in social science and market research:

• Automated interviewing: The platform deploys autonomous AI agents to conduct structured and semi-structured interviews via text or voice. The system dynamically adapts follow-up questions based on real-time participant responses.
• Algorithmic thematic analysis: The software applies inductive coding algorithms to text corpora (such as interview transcripts), allowing researchers to visualize recurring patterns and construct codebooks without manual tagging.
• Synthetic simulation: Qualitati includes experimental tools for generating "digital twins" or synthetic personas derived from empirical data. These personas can be utilized in simulated focus groups to model potential discussions prior to fieldwork.^[3]

The integration of generative AI into qualitative data collection has prompted methodological evaluations within the social sciences. Independent research evaluating the quality of AI-generated interviews has cited the MimiTalk framework as a notable example of AI application in academic qualitative research.^[4] Furthermore, in comparative studies of adaptive semi-structured interviewing systems, the underlying models of Qualitati have been analyzed alongside other LLM-based interviewing frameworks for their ability to balance predefined topic coverage with the discovery of emergent themes.^[5]

While AI interviews are noted for eliciting candid views on sensitive topics and producing highly structured data, researchers emphasize that human interviewers remain superior in capturing cultural and emotional nuances.^[2]