← Back to Projects

Idea for Startup

An AI-driven system for qualitative research that handles interviews, automates analysis, and dynamically generates and refines surveys…

To put it concisely automate away qualitative research

Key Features

  • Reducing Cost and Increasing Scale: Many qualitative studies involve time-intensive interviews. Training a generative agent to ask context-relevant questions, probe deeper on interesting topics, and adapt its approach as it learns can cut costs compared to hiring multiple human interviewers.
  • Dynamic or Adaptive Surveys: Instead of a static, one-size-fits-all questionnaire, an AI system can tailor follow-up questions based on a participant's prior answers or background. This can increase engagement and reduce the dropout rate.
  • Cross-Lingual Capabilities: Allowing uploads in various languages would greatly expand global reach. Modern large language models (LLMs) show promise in multilingual understanding and could help transcribe, translate, and analyze transcripts from diverse populations.

Imagine sociologists working with refugees or migrants who may not speak the language it will cut the middleman to interview and also analyse for contents over foreign language

Imagine researchers used an automated agent to interview or reach far more people than they are otherwise able to…. Also save costs

AI Synthesis

Qualitative Analysis with LLMs

  • Automated Thematic Coding: Traditionally, researchers manually tag transcripts with codes or themes. An LLM could speed this up by suggesting potential themes or clustering responses. Researchers might still do a human review for accuracy, but the AI gets them 80% of the way there.
  • Chat with Your Data: One approach is to enable a "chat" system where the researcher can pose high-level queries about the transcripts—e.g., "What were the top recurring themes about housing dissatisfaction?"—and the system backs its answers with quotes or transcript excerpts. This ensures transparency (reducing hallucinations) and gives researchers a quick path to the underlying data.
  • Avoiding Hallucinations: If a user wants to drill down to the exact quotes that support a conclusion, the system should link directly to original transcripts. This approach—sometimes referred to as retrieval-augmented generation—helps address any issues with AI-generated inaccuracies.

Challenges

While I am not a lawyer ….. everything to do with Privacy, Encryption, and GDPR Compliance of the data

  • Data Protection and Security: Since qualitative interviews can contain sensitive personal information, strong encryption both in transit and at rest is essential. That includes ensuring that any collaborator accessing the transcripts or analyses has the right level of permission.
  • Minimizing Personal Data: For GDPR, I need to provide data handling transparency, give participants a path for data deletion, and store only what's strictly necessary. Tools like data masking or pseudonymization can help, especially if you are retaining transcripts long-term.
  • Ethics Reviews: If this platform is to be used by universities, they often have institutional review boards (IRBs). I will need to anticipate common IRB concerns (informed consent, data reuse, confidentiality) and bake relevant features into the platform—e.g., consent form generation, safe storage, and deletion on demand.

Business Model

I remember UNIBO paying for STATA and MATLAB just so I could use them for the courses……

Target customers could be:

  • Universities and Research Institutes: Offer subscription-based access, with specialized modules for interview generation, cross-lingual support, and analysis.
  • NGOs and Public Policy Organizations: Who often need to conduct large-scale field surveys (e.g., working with migrants or refugees) in diverse languages. Streamlining this with AI-based interviews can reduce overhead.
  • Corporate Market Research: Automated focus groups or feedback surveys for product launches, featuring an agent that interviews customers or employees.

Moat

I think the Domain-Specific Expertise could be the differentiating factor. Building domain knowledge (e.g., codifying best practices from academic literature) into the system is a powerful differentiator and just in general bootstrapping all the data….imagine feeding all the data from the social sciences department of UNIBO

Existing Alternatives

I am aware of Lumivero(NVivo). While they do provide some automation and advanced analytics (particularly through NVivo for qualitative data), they're not built around the kind of dynamic, AI-driven interview process or simulation features that I envisage.

NVivo is geared toward storing, coding, and analyzing transcripts or user-uploaded materials. It doesn't fundamentally change how interviews happen. Researchers still rely on manual or third-party tools for transcription, scheduling, and question logic.

I imagine an AI agent that can conduct real-time or asynchronous interviews with participants, adapt questions on the fly, handle multiple languages, and actively probe follow-up questions based on participant responses.

Some more features to that come to my mind suggest an interactive "chat with your data" approach, where the platform can retrieve relevant quotes, highlight emergent themes in near real-time, and let the user refine queries.

I have watched NVivo tutorials NVivo does have advanced querying and visualizations (word frequency charts, coding queries, etc.), but it's still largely a user-driven manual process. Much of the advanced categorization or theming is reliant on the researcher's coding framework. Any auto-coding or theme extraction in NVivo is more static, not an adaptive, conversation-based approach.

I could explicitly target universities, NGOs, and corporate research teams with a single platform that can do end-to-end handling, from interview design to final reporting whereas Lumivero (even if their products are well-known among academics, and many institutions do subscribe) lack the "take it from zero to launch" approach I envisage with AI-led data collection and dynamic, generative analysis.

Why doesn't bending spoons purchase Lumivero?

Future Possibilities

One big differentiation and value proposition could be if it is possible to bring to life Generative Agents and Simulations

Exhibit 1: Generative Agent Simulations of 1,000 People

Exhibit 2: AI for Social Science and Social Science of AI: A Survey

Essentially research show that LLM-based generative agents can sometimes predict human survey responses or even replicate known social-science experimental results….This can have two immediate consequences:

  • A future in which one might pilot a study on AI-modeled participants first, see whether certain instruments or question formats prompt confusion or bias, and refine accordingly before going live with real participants.
  • And overturning ethical considerations for social experimentation…. Think of the Stanford Prison Experiment