The Next Evolution of AI-Driven Qualitative Insights

At inVibe, we’ve always believed that the true power of qualitative market research lies not just in collecting voices, but in connecting them. Today, we’re excited to announce a breakthrough feature that redefines how clinical and commercial teams generate insights across studies: 

Built for life science and pharma companies conducting clinical or commercial market research, this upgrade expands our AI chat capabilities beyond single-study analysis. Now, you can explore patterns, compare findings, and validate themes across your entire library of inVibe studies—all through a seamless conversational interface.

But this isn’t just another chatbot. Let’s dive into why.


How inVibe’s AI Chat Differs: Thematic Saturation Over Needle-in-a-Haystack Queries

Most AI tools treat data like a puzzle to solve: 

 But qualitative research isn’t about isolated facts—it’s about understanding 

How prevalent is a perception across participants?

Does a finding hold true across disease states or patient cohorts?

Are emerging themes consistent over time?

Traditional chatbots fail here. They prioritize speed over depth, often omitting critical context to fit token limits. inVibe’s AI, however, is engineered to 

 while preserving conversational flexibility.

Our single-project chat already solved this challenge

 (questions, verbatims, metadata) into the context window without sacrificing token space for dialogue.

Now, we’re scaling this rigor to multi-project analysis.


How Multi-Project Chat Works: Precision at Scale

“How have patient perceptions of treatment side effects evolved post-pandemic?”

, our system doesn’t guess—it reasons. Here’s the technical backbone:

Step 1: Intelligent Source Data Retrieval

Your query triggers our most advanced LLM to scan your entire inVibe study library.

It identifies every dataset related to your question—

This ensures the most critical verbatims, themes, and metadata are prioritized, even across dozens of studies.

The selected data is embedded into a fresh context window, and our AI analysis tool generates insights grounded in the aggregated source material.

Prefer hands-on curation? Use our manual selector to pick projects or questions to include to combine automated rigor with human expertise.

Why This Matters: Turning Data Libraries into Strategic Assets

For clinical and commercial teams, the implications are transformative:

: Track shifts in respondent sentiments and opinions over time without manual data stitching.

: Confirm if a theme exists across research engagements, i.e., does your psoriasis study align with rheumatoid arthritis insights.

: Forget hunting for “that one study from 2022.” The AI surfaces relevant data 

every new inVibe study enriches your insights ecosystem

. The more research you conduct, the richer the context for future queries—whether about a specific drug launch or broader market trends.

The Technical Breakthrough: Mastering Context Window Economics

Large context windows (like GPT-4’s 128k tokens) still have limits. Off-the-shelf tools waste tokens on redundant or irrelevant data. inVibe’s system optimizes them by:

: Balancing depth (verbatim coverage) and breadth (cross-study representation).

: Enhancing search accuracy through pre-embedded codes (e.g., “patient journey stage: adherence”).

: Decoupling data retrieval from analysis to avoid token spillover.

Build Your Library, Future-Proof Your Insights

Multi-Project Chat isn’t just a feature—it’s a paradigm shift. By centralizing and connecting qualitative data, life science teams can:

Turn archived studies into living assets.

The more you build your inVibe library, the smarter (and faster) your analysis becomes.

Current customers can access Multi-Project Chat in their dashboard today. New to inVibe? 

 to see how AI-driven analysis can transform your research.

Discover how our AI-powered chat evolves to help life science teams explore themes across studies—not just within them.

Unlocking Cross-Study Insights: Introducing inVibe’s Multi-Project AI Chat for Deeper Market Research Analysis

Cervical Cancer Prevention Week is an annual initiative that takes place the last week of January with the goal of raising awareness about cervical cancer, encouraging women to attend cervical screenings, and educating about the human papillomavirus (HPV) vaccine.

Cervical cancer is most often caused by an infection with high-risk types of HPV, leading abnormal cells in the cervix to form a tumor. While cervical cancer was once one of the most common causes of cancer death in American women, this has decreased by more than 50% since the 1970s due to the increased use of screenings and preventative methods, as well as advances in treatment.

Living with cervical cancer involves navigating a range of challenges, from managing the logistics of treatment and medical appointments, to coping with the emotional impacts of the disease.

Let’s listen to a 41- to 45-year-old woman with cervical cancer describe what it’s like for her to live with this condition and some of the challenges she faces.

Question 1 Response 159

Understanding the patient voice is at the heart of 

, which we approach holistically by analyzing what is said, how it’s said, and how it sounds.

Looking first at what she is saying and how, this patient conveys the two main elements of her disease experience. On the one hand, she describes the feeling of having cervical cancer in evocative terms: “terrifying” and “super scary.” On the other, she acknowledges the roles preventative care and management play in making the disease “manageable and controllable”. While it is “very treatable,” the logistical burden of doctor’s appointments adds an additional challenge to manage on top of the fear of relapsing or progressing.

, she describes the disease itself with strongly negative emotionality (‘valence’), which emphasizes the frustrations and fear of living with a life-threatening condition and her frustration with the burdens of treatment. As she describes the care process, however, this negative emotionality lessens, as well as the acoustic signals of stress (‘activation’), corresponding with the low-level frustration patients with chronic conditions often describe.

Taken together, this patient’s response underscores the importance of early detection of cervical cancer. Although the condition is scary, and treatment can be burdensome, routine screenings to detect abnormal cells help those living with cervical cancer to keep their condition under control.

Our Listening Platform makes it simple for patients to speak with us about their experiences in an open and authentic way and easy for you to engage with their perspectives directly.

If you’re interested in hearing from more patients like this one so that you can gain insight into their experiences, beliefs, behaviors, and desires, 

Learn about Cervical Cancer Prevention Week through a patient's perspective on diagnosis, treatment, and hope. Discover how early detection and proper care make this condition manageable.

In Observation of Cervical Cancer Prevention Week (January 20-26, 2025)

As we ring in the new year, we would like to call attention to two ongoing awareness campaigns in the world of metabolic health. The United Kingdom has designated this Obesity Awareness Week, and January is Thyroid Awareness Month, with both campaigns seeking to raise awareness of both prevention and treatment in their overlapping therapeutic areas.

Obesity is a common condition throughout the world, but it is often misperceived and stigmatized as solely arising from poor lifestyle choices. While diet and exercise can help prevent or reduce the risk of obesity for some patients, understanding it as a disease is important to diagnosing any comorbidities, learning the relevant risk factors, and determining treatments. For example, since thyroid irregularities can lead to changes in appetite and weight, obesity can co-occur with both hypothyroidism and hyperthyroidism

Obesity is often a chronic condition, and its negative physical impacts can compound over time, making movement and other daily activities more difficult. Aside from the direct effects of weighing more, obesity also increases the risk of cardiovascular and endocrine conditions (among others). Stigma can compound these issues, making it difficult understand obesity as a disease and seek treatment accordingly.

Let’s listen to a 51- to 55-year-old woman describe her experience with obesity and the social and physical challenges she faces.

Question 1 Response 44

Let us look first at what this patient is saying. The patient starts with social impacts, moves briefly into the physical effects, then returns to interpersonal troubles that can come from obesity, which underscores that the judgment she perceives from other people is the most keenly-felt aspect of her condition.

The way that the patient goes through the list is also worth some scrutiny. When listing both physical and social symptoms of obesity, the patient trails off and labels the consequences as “various/a lot of things.” This tracks both with her feeling resigned to these effects and the specific problems obesity brings about being too many to name.

This resignation is confirmed when we take our 

 to this data, where we see negative emotionality (‘valence’) and low enthusiasm (‘activation’) throughout her description of obesity. These signals corroborate what you can likely already hear - a depressed-sounding affect in this patient’s speech.

By taking these perspectives into account, we can draw out some of the most important parts of the patient experience with obesity: a chronic, cumulative physical burden which is difficult to disentangle from the interpersonal impacts of the disease. Whether these patients are possible treatment users or potential enrollees in a clinical trial, manufacturers need to be able to address this complexity in order to properly help obesity patients.

Explore the complex relationship between obesity and thyroid health through patient perspectives. Learn how physical and social challenges impact those living with obesity, featuring real patient insights.

In Support of Obesity Awareness Week and Thyroid Awareness Month

At inVibe, we’ve been working with AI since our founding in 2013, and we were among the first market research agencies to adopt 

large language models (LLMs) back in 2021

, and we released our LLM tools to clients in 2023. AI isn’t a gimmick or a buzzword for us, it’s central to 

: To develop technology that provides cost-effective, high-speed healthcare market research while maintaining the highest quality.

As AI takes a center stage in healthcare with the release of popular LLMs like ChatGPT and Claude, we’ve continued to integrate the latest AI models, features, and functionality into more of the work we do at inVibe. Working with AI every day, we quickly started to see the cracks and have prioritized finding ways to ensure the accuracy and integrity of AI-generated content.

To ensure our clients have reliable AI insights, we designed and implemented an evaluation system to validate our AI tools. Our language experts and prompt engineers leverage this system to iteratively test the quality and reliability of the outputs. By testing different prompts and LLMs in this system, we’re able to ensure that our AI tools meet our clients’ needs and our high standards.

Practically speaking, we do this by having two trained language experts compare two outputs presented next to one another on one screen. Once we review the two outputs, we score each across 6 categories that are based on best practices for evaluating the quality of language data and conversational LLMs.

: To what extent does the output address everything it was asked to?

: How closely does the output align with our understanding of the voice data? (Rating: 1-4)

: How clear is the relationship between the findings and the verbatim evidence in the output? (Rating: 1-4)

: To what extent is the output presently coherently and in the prescribed way? (Rating: 1-4)

: Does the output include consistent formatting that enhances readability? (Rating: 1-2)

: Are findings in the output supported by citations in the prescribed format? (Rating: 1-2)

Let’s take a look at one of these comparisons in action. In the below image, we’re seeing a quality test of two different LLMs that were prompted to report the key findings from the same set of voice data. After reviewing both outputs, the language expert scrolls down to where they assign scores to each output and select a ‘winner.’

Once we have a significant number of ratings for what we’re assessing, we compare the total scores, sub-scores, and selected winners to determine an overall winner (i.e., the model that performed better). This overall winner becomes our new baseline for this task in the next round of testing and becomes the default way that our AI tools perform this operation in our dashboard—until the next upgrade at least.

Through many iterations of this testing, we’ve improved the quality of our models by 46.73% over the last 12 months (and we expect to continue this pace in the next 12 months)! This means that our clients can rest assured that our AI tools will remain best-in-class: offering comprehensive, accurate, transparent, and comprehensible outputs tuned to market research best practices for qualitative voice data.

Are you interested in learning more about how our AI tools can make your qualitative research simpler, more systematic, and more scalable? 

Pioneer in AI-powered healthcare research since 2013: See how inVibe validates enterprise LLMs with a rigorous testing framework that's improved insights quality by 46.73%. Discover our proven approach to reliable, scalable market research.

Responsible AI: Validating LLM Outputs

Good insights require good research, and good research requires high-quality data. At inVibe, this data comes from pre-designed voice response questions, which we use to elicit the depth of response of traditional qual research without the scheduling and moderation needs of an interview. The resulting voices can provide powerful insights into HCPs and patient behaviors.

However, our methodology demands particular care in how we ask questions – in an unmoderated survey, we need to make sure that we are prompting respondents in a way that elicits useful content while reducing the need to follow up. To accomplish this, we must strike a delicate balance between an open-ended design philosophy and goal-oriented lines of questioning.

Without the back-and-forth of an interview, it can be difficult to imagine respondents speaking both broadly and deeply enough to provide meaningful qualitative insights. Indeed, from the participant’s perspective, our voice response surveys may seem more like quick quant surveys, at least in terms of how long it takes. But getting expansive responses in this format is easier than you may think, mainly for one simple reason: 

When we reach out to stakeholders, whether they are doctors or patients, they are – in one way or another – an expert in some condition. Whether a patient is discussing their experience with an illness, or an HCP is describing their typical patient, our prompts place the respondent in the role of a teacher, an expert. By giving them that role, we empower respondents to speak with authority and confidence on whatever subject we’re trying to dig into.

This positioning sets us up for success and helps us avoid the dreaded “yes/no” quant-esque responses that come from reticent respondents or under-designed write-in surveys. When respondents feel engaged and confident, they will give us more than that.

So, what does this look like? Frequent readers (or partners) may have seen some examples of our voice-response questions in the past, but here is a good example of a prompt that gets someone talking:

“How would you describe what living with multiple sclerosis is like to someone who is unfamiliar with it? What frustrations or challenges do you experience?

The purpose of a question like this is clear: Elicit a patient description of the MS experience. Something that could be asked briefly and answered even more briefly – indeed, a perfectly valid line of inquiry would be to ask for a few adjectives. But by putting a patient directly into the role of an explainer, we set an implicit expectation. We want a story, not just a description. And by tapping into how MS makes patients feel, we can get just that:

Question 1, Response 34

We set the stage for this respondent, and she took it. By harnessing people’s desire to be the expert, we can get the rich voice data that our insights are made of.

Now that we see the benefit of getting a respondent talking, why not take it further, go broader? If we just need more voice to get the insights, why not just design for maximum response length?

First and perhaps most obviously, we need to respect our experts’ time. 

, and trying to push through it in an unmoderated environment can lead to lopsided or outright bad data.

But second, and equally important, is that designing around the fundamental research questions makes responses more coherent and insights more accessible. One standard part of our survey design process is making sure each prompt we write corresponds to a research question or goal our client has. If our client wants to know a patient’s thoughts on new treatments, we do not use half of the survey breaking down symptom impact. One prompt that gets us context on disease experience can be extremely helpful in interpreting a patient’s later eagerness or hesitation to try something new, but we build the prompt to that specific purpose.

There is a third ‘why-not’ that is more situational but often quite powerful: setting boundaries gives respondents the chance to meaningfully break them. Compare the following examples:

A.  What is your overall experience with Treatment X?

B.  How effective has Treatment X been at managing your condition?

If a patient focuses on side effects when answering A., we can infer that side effects are the most top-of-mind aspect of Treatment X. Efficacy goes un-discussed, which tells us something about the patient’s priorities.

If a patient focuses on side effects when answering B., however, that tells us a little more. Not only are side effects top-of-mind, but we’re seeing the respondent choose to focus on that aspect of treatment X at the expense of efficacy. This signals both the patient’s priorities (which we would have gotten from A) and that whatever efficacy X has is not enough to justify those side effects. We get more insight by providing a little more guidance, even if our respondent disregards it.

This strategy is an example of how we take the linguistic expertise we use when analyzing responses and put it to work on the design side. Awareness of language helps us not only find insights in the data, but elicit the kind of data that will give us plenty to fins.

There is always more to be said about survey design, but the best way to understand how we put these principles into action is through an example. If you want to see how we would handle your research questions, 

Learn how inVibe crafts effective voice response questions for healthcare research. Discover techniques for balancing open-ended prompts with goal-oriented design to elicit meaningful patient and HCP insights.

Unlocking Cross-Study Insights: Introducing inVibe’s Multi-Project AI Chat for Deeper Market Research Analysis

In Observation of Cervical Cancer Prevention Week (January 20-26, 2025)

In Support of Obesity Awareness Week and Thyroid Awareness Month

Responsible AI: Validating LLM Outputs

How we ask: How inVibe writes voice response questions

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