Explore the breakthrough combination of synthetic
personas and real-world data for faster, smarter insights.
Learn how multi-agent architecture and real-time data deliver tailored, nuanced perspectives for more effective market research.
Human data is essential to keeping AI aligned with real-world experiences and nuance. Invisibly uses diverse human insights to ensure our synthetic data stays up-to-date on all emerging trends in the market.
Discover peer-reviewed and scholarly articles that affirm the
efficacy of combining AI with human insights.
From Cornell University: LLMs, when aggregated, perform comparably to human crowd forecasts, and their accuracy improves further when informed by human predictions.
From Harvard Business School: How LLMs, can help researchers understand consumer preferences by generating multiple responses to survey questions. The study demonstrates that AI responses can produce realistic estimates of willingness-to-pay and proposes a fine-tuning method to enhance the alignment of AI's predictions with human responses in market research contexts.
From Harvard Kennedy School: AI-assisted polling is emerging as a potential enhancement to traditional methods by leveraging LLMs to simulate survey respondents and predict political opinions across diverse demographics.
From Natural Language Processing Journal: LLMs can enhance survey research by addressing issues like question-wording and response bias. With a careful, measured approach, LLMs can be used effectively and ethically while mitigating weaknesses.
Learn how we ensure that AI-driven insights are
accurate, unbiased, and ethically sourced.
Learn what Retrieval-Augmented Generation (RAG) is and how it enhances AI by combining real-time data retrieval with language models to deliver accurate, context-aware responses.
Learn how AI’s consistency and humans’ contextual awareness can drive innovative market research solutions.
Request your invitation to the private beta launch and stay tuned for more details from the Invisibly team.
