Bridging Human Insight & AI

The Future of Behavioral Intelligence

Concrete is pioneering a new frontier in behavioral intelligence, transforming its extensive experience in human-centered design and technology expertise into a predictive AI-driven Large Behavior Model (LBM). This innovative model, "Concrete Behavioral Intelligence," leverages years of proprietary data around social behavior and nearly two decades of aggregated learning and generalized knowledge from human-centered research and design to provide profound and predictive insights into digital human behavior for businesses, investors, and the broader industry. Unlike conventional AI, which often relies solely on data, Concrete incorporates human psychology, cognitive biases, and emotional triggers to forecast behavior more accurately. The result is actionable intelligence that facilitates improved decision-making, enhanced user engagement, smarter product design, and more effective marketing.

The Problem: The Gap in Behavioral Intelligence

Companies face a fundamental issue: understanding and predicting human behavior is complex. Whether designing new products, crafting marketing strategies, or personalizing user experiences, businesses rely on intuition, outdated research methods, and incomplete data—leading to costly miscalculations, failed launches, and low adoption rates. In today's increasingly intricate digital landscape, characterized by an overwhelming influx of data, traditional analytics often fall short of elucidating the underlying reasons behind user behavior. Businesses encounter specific hurdles in comprehending user motivations, forecasting future actions, and optimizing digital experiences. AI-driven insights have improved decision-making, but they fall short in contextual understanding. Existing models excel at processing numbers but lack the ability to interpret why people make decisions, leading to surface-level conclusions that don’t capture deep human motivations. Traditional research firms often rely on slow, manual analysis, while AI analytics platforms often lack deep behavioral context. Consequently, there is a growing imperative for advanced behavioral insights to inform strategic choices across diverse sectors.

Our Solution: The Concrete Behavior Model

Concrete is developing "Concrete Behavioral Intelligence," an advanced machine-learning Large Behavior Model trained on proprietary social behavior insights and nearly two decades of aggregated learnings from human behavior research. It synthesizes insights from qualitative user studies, ethnographic research, cognitive psychology, and AI-driven analytics to create a comprehensive, predictive framework. This model builds on Concrete's 18 years of generalized knowledge amassed through work in human-centered design, technology, and communication with numerous prominent clients. Concrete’s approach aligns with the core principles of LBMs, focusing on understanding how users act, interact, and react, utilizing a behavior-centric lens. "Concrete Behavioral Intelligence" is built upon a rich history of observing and analyzing human behavior in digital environments. The model goes beyond simply processing data to understanding the underlying "why" behind user actions, incorporating cognitive science models and ethnographic insights with machine learning.

Understanding Large Behavior Models

Large Behavior Models (LBMs) represent a significant evolution beyond Large Language Models (LLMs) by incorporating principles of action and experience-driven learning. While LLMs excel at analyzing text and generating responses, LBMs integrate behavioral data over time to understand and actively engage with the world in human-like ways. This involves learning intricate behaviors that include decision-making processes, contextual awareness, preferences, and actions, often trained on sequences of actions and outcomes from behavior-rich environments.

How LBMs Learn Like Humans

LBMs aim to replicate human learning processes through several key mechanisms:

• Dynamic Learning: Humans adapt to new situations by recognizing patterns and adjusting their approach, rather than just memorizing facts. LBMs strive for this by using feedback loops to refine knowledge and improve their understanding as they experience new situations. For instance, an LBM-powered robot could learn to navigate a building through exploration. This adaptability allows LBMs to update their knowledge and strategies in real-time, making them suitable for unpredictable scenarios.

• Multimodal Contextual Understanding: Unlike LLMs limited to text, humans integrate sights, sounds, touch, and emotions to understand the world. LBMs aim for a profoundly multimodal understanding, capable of recognizing not just spoken commands but also gestures, tone of voice, and facial expressions. They use multi-modal data (text, images, videos, and sensor data) to analyze complex behaviors.

• Generalization Across Domains: A hallmark of human learning is the ability to apply knowledge across various domains. While LLMs can generate text for different fields, LBMs are designed to generalize knowledge across diverse contexts. For example, an LBM trained for household chores could adapt to an industrial warehouse setting, learning as it interacts with the environment instead of needing complete retraining.

How It Works

1. Data Ingestion – Structured and unstructured behavioral data are collected from 18 years of user research, observations, and interviews.

2. Pattern Recognition – AI identifies common decision-making frameworks, cognitive biases, and emotional triggers. Concrete's data extends beyond conventional usage metrics to capture subtle behavioral patterns and contextual understanding.

3. Predictive Analysis – Concrete generates behavioral predictions based on real-world scenarios.

4. Actionable Insights – Companies receive practical, evidence-based recommendations for product design, marketing, and personalization strategies. The model aims to assist software and technology-driven companies in identifying and overcoming obstacles that hinder productivity enhancement and the scaling of delivery and adoption.

Key Abilities of LBMs

LBMs possess several key abilities that distinguish them:

• Behavioral Observation: They can watch and replicate human activities.

• Contextual Awareness: They understand and react based on the surrounding situation and temporal sequences.

• Decision-Making: LBMs are designed to learn and replicate intricate behaviors that involve decision-making processes.

• Adaptability: They can update their knowledge and strategies in real-time.

• Multimodal Learning: LBMs integrate visual, spatial, and temporal data to enhance understanding.

Proprietary Advantage

Unlike traditional AI solutions that rely on historical transactions and consumer interactions, Concrete is contextually aware - understanding not just what users do, but why they do it. This is built upon an unparalleled foundation of years of data, providing a unique and deep historical perspective. Concrete's human-centered approach and long-standing expertise in understanding the "human side of AI" and technology set it apart. The model employs sophisticated Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to analyze this extensive historical data, extracting meaningful insights. Concrete’s 18 years of high-value data is an unrivaled competitive advantage and represents a first-mover AI model combining behavioral science with machine learning.

Key Capabilities and Applications

The "Concrete Behavioral Intelligence" model, as an LBM, offers enhanced capabilities:

• Deeper Audience Understanding: Providing nuanced insights into user motivations, preferences, and decision-making processes by analyzing not just actions but the context and potential drivers behind them, allowing for the identification of key behavioral segments and their distinct characteristics.

• Predictive Behavioral Analytics: Enabling more accurate anticipation of future user behavior and trends by learning from sequences of actions and incorporating contextual awareness, helping companies optimize for the present, near-term, and long-term by proactively identifying potential opportunities and risks.

• Product and Service Development: Informing the creation of more user-centric and effective digital offerings by understanding user needs and preferences at a deeper behavioral level, ensuring the development of indispensable products.

• Optimizing User Interfaces and Experiences: Leveraging insights into user behavior and decision-making (drawing on Concrete's expertise in UI design) to create more intuitive and engaging experiences.

• Communication and Marketing Strategies: Identifying the most effective channels and messaging by understanding user communication preferences and likely responses, leading to the personalization of user experiences and marketing efforts with messaging that emphasizes the "human side of AI".

• Improved Team Productivity and Organizational Development: Understanding team dynamics and individual behaviors, considering factors like communication styles and decision-making processes, identifying areas for enhanced communication and workflows, and helping companies address factors hindering team performance.

These insights have potential applications across key industries, including the technology sector (software adoption, developer experience, healthcare (patient engagement, healthcare IT), finance (customer behavior, financial services), education (user engagement, learning platforms), and retail (customer behavior, online and in-store experiences). Concrete aims to assist software and technology-driven companies in identifying and overcoming obstacles that hinder productivity enhancement and the scaling of delivery and adoption. LBMs are also finding applications in robotics, simulators and games, training and education, healthcare consumer engagement, and social simulation.

Market Opportunity & Competitive Edge

There is a substantial market opportunity for advanced behavioral intelligence solutions. The model's unique capabilities and market demand present the potential for a significant return on investment. Concrete bridges the gap between traditional research firms and AI analytics platforms by combining deep behavioral context with advanced analytical capabilities. Concrete provides actionable intelligence, offering practical, data-driven insights for direct business implementation and has a proven track record and experience with Fortune 50 companies and the world’s most admired brands, lending significant credibility.

Business Model

Concrete will offer several revenue streams:

• SaaS Model: Subscription-based access to behavioral AI insights for businesses.

• Enterprise Licensing: Custom AI models designed for large enterprises.

• API Integrations: Plug-and-play behavioral intelligence for third-party platforms.

• AI-Driven Consulting: Expert insights merging AI predictions with human-driven strategies.

Improved Decision-making, Enhanced User Engagement, Smarter Product Design, and More Effective Marketing

"Concrete Behavioral Intelligence," as a leading example of a Large Behavior Model, possesses the transformative potential to benefit businesses, investors, and the industry as a whole by facilitating an unprecedented understanding of digital human behavior, leading to improved outcomes and strategic advantages. By moving beyond traditional data analysis and incorporating the nuances of human action and context, Concrete is poised to redefine behavioral intelligence, merging human insight and artificial intelligence to create a smarter, more intuitive future. We welcome further discussions and potential partnerships to capitalize on the power of LBMs for deeper human connection. For more information, please reach out to us in the Contact Us form below.