AI’s progress so far has been driven by scaling: bigger models, more data, more compute. But we’re nearing the limits of what size alone can accomplish. The next wave of progress in AI won’t come from brute force. It will come from deep understanding.
So far, AI has excelled at recognizing patterns in language, sound, and images. But it’s still operating at the surface level, missing the deeper, intrinsic signals that are hidden within the data it already processes. These signals are just waiting to be uncovered. AI providers haven’t had the right analytical lens to decode them. And by overlooking this, they’re missing a massive opportunity to unlock AI’s full potential.
The Lessons of Hidden Signals
History is full of transformative discoveries made by uncovering hidden signals in existing data. In 1964, physicists Arno Penzias and Robert Wilson were studying signals from space using a radio antenna. They detected a background noise, which they initially thought was interference from pigeon droppings. But after deeper investigation, they realized it wasn’t interference, it was cosmic microwave background radiation, which was an echo of the Big Bang. Their discovery of a hidden signal confirmed the Big Bang theory and changed our understanding of the the universe. One of the most important discoveries of our time was hidden in plain sight.
During World War II, the German military used the Enigma machine to encrypt their communications. Alan Turing and his team at a British intelligence base, developed new techniques to decode the structure of the Enigma code and uncover hidden signals that revealed information about German military movements and plans. These hidden signals were the intrinsic patterns buried beneath the extrinsic surface of encrypted characters. Their discovery of hidden signals saved countless lives and changed the outcome of the war.
The Key to Unlocking AI’s Full Potential
Just as Penzias and Wilson and the Enigma codebreakers demonstrated, surface-level signals are only the starting point. The next stage in AI’s evolution is about understanding the deeper layers of the data it already processes. To unlock these deeper signals, AI needs the right tools.
That’s where Receptiviti comes in. Receptiviti provides the analytical lens that decodes the hidden psychological signals within language. This isn’t about massive overhauls or retraining existing models, it’s about enhancing AI with the ability to understand the nuanced human psychology embedded in the language it already analyzes.
Moving Beyond Surface Signals
Currently, AI understands extrinsic signals: patterns in language, structure, tone, and surface-level markers. But this approach misses the incredibly important intrinsic signals hidden within the data it already processes. These hidden psycholinguistic, cognitive, social, and contextual signals are not immediately obvious; they require a more sophisticated approach to uncover.
By integrating these hidden signals, AI can understand much more than what’s explicitly stated. It can reveal the psychological and contextual nuances that shape human thought and behavior. This is the missing layer that will allow AI to move beyond surface-level recognition into true understanding.
Receptiviti's proven models, grounded in more than 30,000 research citations, enable AI companies to achieve a step-function improvement in model performance by tapping into the psychological nuances that current LLM models can't see.
Our approach doesn’t require retraining massive models or scaling compute. Instead, it adds the missing layer that allows AI to recognize and interpret the psychological signals that have always been there but are invisible to current LLMs. By integrating Receptiviti’s contextual psychological layer, LLMs gain access to a deeper class of signal, the psychological, social, and cognitive cues that shape, and often predict, human behavior.
Real-World Applications
Receptiviti’s contextual layer of psychology dramatically enhances AI’s performance. From brand marketing and customer support to fraud detection and professional sports, our models have proven their value across diverse industries, demonstrating how integrating psychological signals into LLMs unlocks entirely new capabilities:
Marketing and Brand Voice: Marketing organizations use Receptiviti to analyze both audience and brand-generated content, generating psychographic insights that power segmentation, personalization, and brand alignment. Unlike LLMs, which often produce inconsistent, ungrounded psychological insights, Receptiviti provides LLMs with scientifically validated, repeatable metrics that enable marketers to scientifically compare audience segments, refine brand voice, and tailor strategies that resonate deeply with specific audiences, delivering more meaningful customer engagement.
Health and Wellness: One of the world's largest online weight loss programs used Receptiviti to uncover the subtle psychological shifts in participants' language that are strongly predictive of weight loss outcomes and program attrition, signals missed by LLMs. When integrated with LLMs, Receptiviti enhanced AI's ability to understand participants' trajectories, allowing for more personalized and effective health interventions.
Professional Sports: Receptiviti was used by the Senior Bowl to analyze interviews with NFL prospects, enabling scouts and coaches to assess players based on psychological traits like emotional regulation, cognitive style, and resilience. Integrating Receptiviti’s psychological layer into LLMs allows teams to uncover deeper insights into players' psychology, helping teams make more informed decisions about selection, coaching, and strategy.
Fraud Detection: Researchers used Receptiviti’s models to identify linguistic markers associated with fraudulent statements during quarterly earnings calls. This demonstrates how embedding psychological and contextual signals in LLMs can help flag financial risks with unprecedented accuracy.
Mental Health: Receptiviti’s models enable the detection of subtle language patterns linked to depression and chronic stress, insights that traditional LLMs miss. Integrated into LLMs, these models can identify psychological distress earlier and more accurately, enhancing AI’s ability to support mental health interventions with greater precision.
Customer Support and Conversational AI: Research shows that certain aspects of language, such as concreteness, can significantly impact customer satisfaction. Receptiviti helps AI recognize and guide the use of psychologically informed, empathetic language in customer interactions, improving customer satisfaction and engagement
Unlocking New LLM Capabilities
By uncovering over 200 new signals and providing deeper understanding, Receptiviti enables LLMs to reveal previously undiscovered relationships, insights, and predictions. Our proven models have revolutionized scientific research, created new business opportunities, and solved challenges across industries, and when used in conjunction with LLMs, they unlock smarter, more human-centric AI systems.
The next leap in AI won’t come from scaling bigger models, it will come from enabling models with deeper and more meaningful understanding. The intrinsic psychological signals embedded in language hold the key to that breakthrough. They’ve always been there, hidden in plain sight, waiting for the right lens. Receptiviti unlocks that layer, no retraining, no overhauls, just a smarter, more human understanding of the data LLMs already process.
The AI leaders who integrate this missing layer will define the next generation of truly intelligent AI systems.
If you’re working on foundational models or building applications that rely on a deeper understanding of people, feel free to reach out. 🤔