Maya AI is more than just a smart research tool. It is built for qual-at-scale. In this comprehensive guide, we will answer all the questions pertaining to the creation and use of Maya AI, crafted by Merren. Whether you are exploring AI interviews for the first time or looking to run qualitative research at scale, this guide covers it all.

1. Who created Maya AI ? 

Maya AI is created by Monalisa Saxena and Sumit Saxena. Maya is an AI-powered qualitative interviewer. The founders curated a research tool that thinks like great researchers and moves at the speed of business using technology. Businesses are making decisions in days and traditional research takes weeks or months. To close the gap between getting research results and helping businesses take quicker actions, Maya was built.

2. What inspired the creation of Maya AI?

After 2 decades in research, Monalisa Saxena noticed a pattern in research. Research throws up great insight but it’s slow, expensive and very dependent on human bandwidth, especially qualitative research. They asked a question: what if you could clone your best qualitative moderator who can work 24-7. That’s how Maya AI was conceptualized and how AI-driven qualitative research was built at Merren.. 

3. How has Maya’s mission evolved since its inception?

Initially Maya was about speed to cover the initial need-gap.However, from 2026 onward, Maya is about democratizing depth that makes qualitative research accessible anytime anywhere. Maya enables continuous always-on insighting capabilities, beyond just project based research. 

4. For non-technical users, how would you describe Maya AI?

 Maya is an on-demand qualitative researcher. She will interview your customers, understand nuances and emotional metrics, she will probe deeply and will hand you the insights without clients having to schedule a single call. This process will happen in real time. 

5. What are the core capabilities of Maya as a customer research platform?

Maya conducts in-depth AI interviews via voice and text. It operates across multiple languages, probes intelligently just like a trained moderator. Maya adapts in real time, analyzes responses, and generates insight ready reports. It is a summation of fieldwork analysis and synthesis in one-flow all on one platform. 

6. Traditional market research can be slow, costly, and complex. How does Maya address those challenges?

Traditional research takes weeks but Maya takes hours. Traditional research scales with budget but Maya scales with demand making true qual-at-scale a reality. Traditional research analyzes post field-work but Maya analyses interviews as soon as it happens. Due to the wide contrast, there is faster turnaround, much lower operational complexity and also insights from decisions get made immediately and not after.  

7. What are the biggest misconceptions around the use of AI in research?

Misconceptions were common in the initial phase even among seasoned qualitative researchers. The initial fear was that it would make the process robotic. Qualitative research is about the element of human insights in research that captures the soul and essence of people’s experience and perspective. However, while designing and creating Maya, Monalisa noticed that when you design well, AI removes mechanical work and enhances depth. AI does not replace researchers but makes them more productive.  

8.Who is Maya designed for? 

It is designed for anyone who needs fast and reliable customer insights. Research teams who are looking to scale, marketing teams that need very fast validation, product teams that are testing concepts, CX leaders who are tracking sentiments in real time. Maya’s AI-driven qualitative research capabilities are for people who cannot afford to delay business decisions who delay insights. 

9. Could you share a time when Maya delivered measurable impact?

There are qualitative studies that take 4 weeks to complete being done in under 4 days. One of the early cases, the interviews revealed a hidden product barrier. It allowed the client to pivot the messaging before the launch itself that saved them media spend.  It helped with the brand equity overall. Speed is not just about efficiency but also about risk mitigation that was proven in this case.

10. How efficient is Maya as an AI agent?

Maya can conduct thousands of simultaneous interviews across markets, languages, formats (voice or text) which is the very definition of qualitative research at scale. There is a consistent quality, zero scheduling friction, there is no fatigue and bias. It is very efficient as an AI agent and much more than what human-researchers can do.

11. How does Maya’s conversational AI differ from traditional survey or chatbot tools?

Surveys ask but Maya listens and probes, beyond asking.That is the key difference. Chabots follow scripts whereas Maya follows the meaning. She will adapt her next question based on what respondents say just like a skilled human moderator. These are the vivid differences between Maya’s operation vis-a-vis a chatbot.

12. What kind of data analysis does Maya AI provide?

The process is very simple. You enter your research objective, you customize and upload your discussion guide. Maya will conduct the interview and you can watch the insights build in real time. Stakeholders can download the structured report which will have the basic themes, verbatims and the recommendations. It is very in-sync with how a typical qualitative research happens. The user journey is made in a way that is accessible to anyone irrespective of technical know-how.

13. Can users tailor the AI’s behavior for different research contexts?

Stakeholders can define the tone, depth and probing style. Maya can behave like a typical researcher, a friendly peer or a product tester. It can be changed with regards to language,  tonality and other factors that people usually decide when onboarding a human moderator for a particular target group.

14. What level of expertise is required to operate Maya ?

If you can articulate a business question, you can use Maya. You do not need to be a market researcher whereas non-researchers can get clarity without any sort of methodological training. IT is designed to be very simple so that professionals access industries can use it sans training.

15. How does Maya handle multilingual and cross-cultural research challenges? 

When Maya is interviewing in multiple languages, it’s not just a translation, it is native. It is trained to understand cultural nuance, context and local expression which is critical in qual-at-scale deployments. This is done to prevent a robotic voice from asking questions in qualitative research. 

In an interesting turn of events, a respondent began candidly interacting (and diverting) with Maya despite being told that she is an AI agent. Maya was using the local nomenclature during the interaction. Due to the conversational nature of Maya AI, the respondent got carried away in the interaction, a testament to how natural these AI interviews can feel.

16. How do you ensure privacy and ethical use of participant information?

Participants are informed prior and show up only if they agree to be a part of the interviews. There is data encryption and the whole workflow is consent first. We have anonymization protocols in place and strict data governance. The privacy and ethical use is absolutely spot on in Maya.

17. Between traditional research and Maya AI, what key benefits should a business expect?

A business should expect speed that is measured in hours not weeks. Cost efficiency without sacrificing depth is also a big benefit. A business will not be sacrificing the depth and quality of data that they would get from a typical qual research. The stakeholders will also receive continuous insights instead of one-off studies. This encourages the agility in decision making since they are getting all insights at speed of business. This gives them the power to execute the correct decisions on time.

18. What new features or improvements can users expect in Maya over the next 12–18 months?

The team at Maya is focused on continuous development with all of the learnings that are derived from all the case studies. The team is looking at richer behavioural analysis, better integration with CRM and product analytics tools. Maya is also looking to add more advanced persona capabilities so that clients can choose from various personas that will suit their moderation needs.

19. Do you see Maya extending beyond market research into other domains?

Maya has a role in places that thrive on insights from structural conversation. Example, employee listening, employee interviews, learning and development, community engagement program. When you need to speak to a large number of people to collect critical insights, Maya has an important role to play.

20. What should businesses or researchers think about when considering AI-powered research tools today?

The key question is not whether AI will enter research since AI is already a part of research now. The real question is, will you use it strategically or be disrupted by someone who does?

21. Any final advice for teams looking to get started with Maya?

Start small but think big. Pilot a project and compare timelines and depth. Once professionals experience real-time AI-driven qualitative research, it is very hard to go back to the traditional route.