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Why can’t I just do the same thing on ChatGPT? Standard LLMs vs. Invisibly for Market Research

Learn how multi-agent architecture and real-time data deliver tailored, nuanced perspectives for more effective market research.

You could ask ChatGPT or any other LLM the same questions you might ask Invisibly, but you may be curious as to the differences in the answers you receive. The distinction comes down to how each system is designed and what they’re optimized to deliver. Where general-use LLMs serve up broad, “most likely” answers based on their vast data sets, Invisibly’s AI digs into the weeds, offering insights tailored to the exact personas you’re trying to understand.

Here’s why that makes all the difference.

1. Persona-Based Customization

Standard LLMs like ChatGPT excel most at producing answers that satisfy the lowest common denominator. They pool data from a broad set of sources, which results in responses that reflect the most probable perspective. For general queries, this can be useful—but for businesses looking to understand niche customer segments, it can be tricky to guide the AI toward the specificity required for the task.

Invisibly harnesses an AI system that jumps this hurdle for you. Our persona-driven system allows you to represent the entire market in a statistically rigorous way, and then precisely target specific demographics, behaviors, and psychographics to generate responses that reflect the real beliefs, preferences, and actions of the exact customer profile you’re after. Whether you’re targeting Gen Z consumers, suburban parents, or young urban professionals, Invisibly delivers insights that capture the nuance, creativity, and human variety missing from untrained LLM outputs.

2. Invisibly is your Invisible Market Research Agent

When you use Invisibly’s AI, you’re not just querying a single model. We leverage a multi-agent architecture, where multiple personas interact and engage to produce a more diverse and layered understanding of your target audience. This is where the power of RAG (Retrieval-Augmented Generation) comes into play—enhancing contextual understanding and allowing for memory across numerous interactions and data points.

Invisibly’s Synthetic Audiences are built to maintain context across multiple interactions, simulating long-term behaviors and relationships. This capability allows for stunningly accurate insights into how your customers think and behave over time—not just at a single moment. For example, Invisibly’s AI can simulate how a group’s preferences shift over weeks or months, and from one market segment to the next, offering a more dynamic, real-world perspective.

3. Real-Time Data and Adaptive Intelligence

Another crucial difference is data freshness. Markets change quickly—whether it’s in response to cultural moments, emerging trends, or economic shifts—and businesses need insights that keep up with the pace of change.

Our AI excels in this particular environment because we continuously feed it real-time data. Using our own user survey data, consumer transaction data, and open-web polling, Invisibly keeps its models in sync with the latest market trends, ensuring that the insights you receive are timely, accurate, and reflective of the current landscape.

Invisibly’s Synthetic Audiences can show you how different segments of your audience will react to a new product feature, break down purchasing behavior by demographic, or highlight emerging trends that you can act on right now.

Invisibly is the Superior Choice for Market Research

If you’re looking for representative, nuanced insights, Invisibly’s Synthetic Audience is the clear choice. We specialize in delivering a rich variety of perspectives that are as granular and diverse as your audience. Ready to make data-driven decisions that reflect the real-time behaviors and beliefs of your customers? Get in touch with us today and see how Invisibly can transform your market research.

 

Dave is Invisibly’s Chief AI Officer, spearheading the company’s gen-AI research platform. He holds degrees in Mechanical Engineering and Philosophy-Neuroscience-Psychology from Washington University in St. Louis, along with an MBA from St. Louis University. His expertise spans AI, machine learning, natural language processing, data science, and product development, driving innovation in our synthetic data solutions.

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