The Essence of Business Research
Business research is the science of understanding human decision-making. Humans don't make decisions based purely on rationality, but are influenced by narratives, emotions, and cognitive biases. Therefore, understanding the mechanisms that influence decision-making is at the core of business research.
If "physics" models the "objective world," then "language models" have the opportunity to model the "subjective world." Atypica.AI can capture human decision-making mechanisms that traditional data analysis handles poorly, providing deep insights for personal and business decision problems.
- Simulate consumer personalities and cognition by building "user agents"
- Analyze consumer behavior and decisions through "interviews" between "expert agents" and "user agents"
- Automatically generate detailed research reports with visual insights
./atypica.ai --model "subjective-world"
Initializing subjective world model...
Loading user agent library...
Loading expert agents...
Setting up multi-agent interaction environment...
Preparing long reasoning model...
[Ready] atypica.AI launched
run-research "Choose the right Chinese restaurant for birthday dinner"
Starting research process:
- Clarifying research question...
- Designing task sequence...
- Browsing social media data sources...
- Building user agent models...
- Starting agent interviews...
- Analyzing user feedback...
- Generating research report...
[In Progress] Research progress: 34%
Research Process
Using Atypica.AI, you simply need to ask a specific business research question, and the system will provide a detailed research report through 10-20 minutes of "long reasoning."
Clarify Problem
Design Tasks
Browse Social
Build Agents
Interview Sim
Summarize
Generate Report
"Nerd Stats" records how much time, steps, agent roles, and tokens were consumed during the work process, which is also a form of "Proof of Work" for agents.
Use Cases
Testing
Evaluate marketing content topics and effectiveness, predict audience reactions
Example: Which Logitech mouse topic would be more popular on Xiaohongshu?
- 【Light as a feather, powerful as a tiger】Logitech MX Keys Mini daily efficient office companion
- From keys to chips: Revealing how Logitech keyboard silent technology works
- One-key multi-device switching: My tips for doubling work efficiency with Logitech FLOW technology
- 30-day battery life is not a dream: The energy-saving technology behind Logitech keyboards
- The story behind ergonomic design: How Logitech ERGO K860 saved my carpal tunnel syndrome
Insights
Discover user experience pain points, understand customer feedback and experiences
Example: I'm the LV Shanghai regional manager. What feedback do customers have about our Shanghai store shopping experience? Which aspects need improvement, and which are done well and should be strengthened? Please give me a comprehensive report.
Co-creation
Co-create with simulated users to develop new products and services
Example: Co-create new product ideas for Mars' "Crispy Rice" with young parents from first-tier cities?
Planning
Develop marketing strategies and product roadmaps
Example: INAH Non-alcoholic Grape Drink Marketing Plan
Personal Decision Support
Although Atypica.AI is designed as a business research and analysis agent, it can also conduct personal decision research:
Planning Questions
How should a swimming specialist plan to study high school in the US or UK?
Technical Origins & Development
Origins of Atypica.AI
Multi-Agent Interaction
The Stanford Town paper "Generative Agents: Interactive Simulacra of Human Behavior" introduced us to the concept of multi-agent interaction for the first time, but this paper didn't truly demonstrate how agents interact with each other.
Model Tool Calling
OpenAI released GPT-4's Function Calling feature, enabling models to call external tools; in November 2024, Claude's MCP protocol showed us the possibility of models operating tools (like our content management tools). This technological advancement opened up entirely new application scenarios, allowing models to go beyond chatbox interactions and actively connect with the external world.
Language Models for Subjective World Modeling
The Stanford Town research team published the groundbreaking paper "Generative Agent Simulations of 1,000 People," which successfully simulated the behavioral patterns of 1,000 random Americans. Researchers conducted in-depth interviews with real humans through AI, building agents that accurately reflect individual behavior and decision-making patterns. Remarkably, these agents achieved over 85% behavioral consistency with real people, demonstrating unprecedented simulation accuracy.
This research revealed the tremendous potential of agent simulation for real human behavior and effective interviewing. Traditional methods of studying users (like studying orange juice) involve analyzing constituent elements (labels), but even with all labels, it's difficult to completely reconstruct user complexity. This new method is equivalent to refining orange juice into concentrated powder, then using language models as "water" to reconstitute it back into orange juice.
Divergence-First Long Reasoning
Deepseek R1 showed us transparent reasoning processes, helping us understand how to design reasoning architectures on top of foundation models. Unlike reasoning methods for objective world/scientific problems that emphasize "convergence," reasoning for subjective world/business problems needs to emphasize "divergence." We define this across four dimensions: 1) Learning from past cases, 2) Eureka moments, 3) Quality of feedback, 4) Number of iterations.
Based on these four dimensions, we began developing the multi-step, long-divergence reasoning model architecture "Creative Reasoning," forming optimization for thinking, analyzing, and researching general business problems.
Multi-Agent Product Form
The release of Manus, Claude's Artifacts, and Devin showed us the possibilities of multi-agent product design. Especially Manus's product innovation in expressing agent work processes and enabling replay. Seeing how agents work indeed creates more empathy for their results.
Technical Limitations & Outlook
Limitations of Atypica.AI
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1Input Question Quality
The accuracy of input questions largely determines report quality
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2Model Accuracy Limitations
Stanford research showed this method can accurately simulate 80% of consumers' complex decision-making processes, but has limitations in predicting highly emotional or context-dependent decisions, and insufficient accuracy in simulating emerging niche consumer groups (synthetic orange juice is still different from natural orange juice)
-
3Data Integration Complexity
Large differences in data quality and structure make integration difficult; data cleanliness issues may cause model distortion; this method is better at simulating users' positive and negative feedback but not good at simulating users' biases and limitations
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4Innovation Prediction Difficulty
Difficult to predict responses to truly breakthrough innovations
Methodology Analogy
This method is equivalent to refining orange juice into concentrated powder, then using language models as "water" to reconstitute it back into orange juice.
Although this "synthetic orange juice" is not completely natural, it strives to simulate the taste, color, and nutritional characteristics of real orange juice.
Future Outlook
With the continuous development of language models and enhanced multimodal capabilities, Atypica.AI will continue to improve in the following areas:
- • More precise user profiling and behavior models
- • Deeper psychological model integration
- • More nuanced group difference modeling
- • More transparent AI reasoning and explanation systems
HippyGhosts
The visual identity of atypica.AI comes from the HippyGhosts.io community, which represents the geek spirit of joyful hippy ghosts.
In the world of Atypica.AI, the physical embodiment of each "agent" is a "Hippy Ghost," representing the fusion of technology and creativity, and symbolizing our pursuit of building AI agents with personality and warmth.