atypica.AI

An evolution journey from user discovery to step-by-step research, finally becoming a conversational research assistant 

import { fetchUserChatById } from "@/data";
import { authOptions } from "@/lib/auth";
import { getServerSession } from "next-auth/next";
import { forbidden, redirect } from "next/navigation";
import { Metadata } from "next/types";
import { StudyPageClient } from "./StudyPageClient";

export async function generateMetadata({
  searchParams,
}: {
  searchParams: Promise<{ id?: string }>;
}): Promise<Metadata> {
  const id = (await searchParams).id;
  if (!id) {
    return {};
  }
  const studyUserChatId = parseInt(id);
  const studyUserChat = await fetchUserChatById(studyUserChatId, "study");
  return studyUserChat.title ? { title: studyUserChat.title } : {};
}

export const dynamic = "force-dynamic";

export default async function StudyPage({
  searchParams,
}: {
  searchParams: Promise<{ id?: string }>;
}) {
  const { id } = await searchParams;
  if (!id) {
    redirect("/");
  }
  const studyUserChatId = parseInt(id);
  const studyUserChat = await fetchUserChatById(studyUserChatId, "study");

  const session = await getServerSession(authOptions);
  if (!session?.user) {
    redirect("/auth/signin?callbackUrl=/study");
  }
  if (studyUserChat.userId !== session.user.id) {
    forbidden();
  }

  return <StudyPageClient studyUserChat={studyUserChat} readOnly={false} replay={false} />;
}
atypica@localhost:~

git log --oneline | head -3

ee7febb update headers and usermenu, add pricing and study history on page header
044ebf3 add study list page
1ebfa76 fix lint issues

cat README.md | grep -i "atypica"

# atypica.AI

atypica.AI is a user research platform for subjective world modeling, driving research processes through large language models to help researchers quickly gain insights and generate research reports.

Welcome to submit Issues and PRs to improve atypica.AI together!

./storytell.sh --format timeline

Telling the evolution story of atypica.AI...

$ ./story --highlight "journey"

The story of atypica.AI began in early March 2025, when we faced a problem:

"User research takes too long. From finding target users, conducting interviews, to organizing reports, it usually takes weeks or even months. Could we use AI to accelerate this process?"

With this question, we began more than two months of rapid iterative development, evolving four different stages of product forms, each more powerful and user-friendly than the previous one.

Version One: User Discovery Tool 2025.3.8-3.13

It all started with a simple feature: using AI to search and analyze Xiaohongshu user data, automatically generating user personas.

Product Form
  • Single scout page where users input research objectives
  • AI automatically searches and analyzes relevant user data
  • Generates and saves user personas

"The first time I saw AI automatically collect user information from the internet and generate personas, I couldn't believe my eyes. This process usually takes me a whole day." — Early test user

scout.tsx
import { fetchUserChats } from "@/data";
import { Message, useChat } from "@ai-sdk/react";

export function ScoutChat() {
  const {
    messages,
    input,
    handleSubmit,
    status,
  } = useChat({
    api: "/api/chat/scout",
    body: {
      scoutUserChatId: scoutUserChatId,
    },
  });

  return (
    <div>
      <div>
        {messages.map((message) => (
          <ChatMessage
            key={message.id}
            role={message.role}
            content={message.content}
          />
        ))}
      </div>
      <form onSubmit={handleSubmit}>
        <textarea
          value={input}
          placeholder="Enter your research topic..."
        />
      </form>
    </div>
  );
}
Milestone: March 12, 2025 - First successful automatic user persona generation from Xiaohongshu data

Version Two: Step-by-Step Research Process 2025.3.14-3.19

Building on the success of the first version, we expanded the feature boundaries and built a complete user research process, with AI assistance and guidance at every step.

Product Form
scout
→
personas
→
analyst
→
interview
→
report
  • Multi-page step-by-step process with detailed guidance at each step
  • Researchers complete one task per page before moving to the next step
  • AI assists at every stage, reducing manual operations
  • Research results transfer seamlessly between steps

"The step-by-step design is very intuitive. I just need to follow the process step by step to complete the entire research. The guidance prompts on each page are also very thoughtful, making it easy for a novice like me to get started." — Market Researcher

Analyst

Research topic creation & management

Interview

AI-driven user interview simulation

Report

HTML report generation & rendering

Milestone: March 15, 2025 - Implemented complete step-by-step research process, reducing user operation burden through guidance

Version Three: Conversational Research Assistant 2025.3.20-3.27

Finally, we achieved a revolutionary breakthrough: integrating the entire research process into a single conversational interface, where users only need to chat with the Study Agent, and all steps are completed automatically in the background.

Product Form
  • Single conversational interface for complete research workflow
  • Split-screen design: left conversation, right tool console
  • Real-time display of AI execution process and results
  • No page jumping required, one-stop research completion

"This is simply magic! I just need to tell the AI what I want to research, then I can watch it automatically complete all the work, from finding users to generating reports, everything happens in real-time before my eyes." — Product Manager

study.tsx
Chat Interface
I want to understand the factors that influence young mothers' choice of baby formula
AI
I will help you research the key factors influencing young mothers' choice of baby formula. I will automatically perform the following steps:

1. Find target user groups
2. Generate user personas
3. Design and conduct interviews
4. Analyze results and generate reports
Tool Console
$
scoutTaskChat
Searching Xiaohongshu for discussions by young mothers about baby formula...
$
savePersona
Saved 3 typical user personas:
- 25-year-old first-time mother, focuses on safety
- 28-year-old mother of two, values cost-effectiveness
- 32-year-old working mother, prioritizes convenience
$
interview ●
Conducting interviews...1/3 completed
Milestone: March 20, 2025 - Launched unified conversational interface, implementing study agent coordination of other agents work model

Version Four: Commercialization & Platform Optimization 2025.3.28-5.12

After perfecting the core research functions, we turned to commercial operations and global deployment, adding features such as subscriptions, token billing, multi-language support, and more.

Product Form
  • Complete SaaS service with free and paid tiers
  • Multi-language interface and global deployment support
  • Token consumption statistics and recharge process
  • Research history and learning lists
  • Advanced interview project functionality

"The token billing model is very fair, I only pay for the research I actually use. Moreover, I can check consumption statistics at any time to understand the cost of each research project." — Enterprise User

Tokens

Token consumption stats & billing

i18n

Multi-language support & global deployment

Subscription

Pro subscription & permission management

Milestone: April 20, 2025 - Launched complete subscription and token billing model, achieving commercial operation

$ ./analyze --type "evolutionary_leaps"

01 Evolution of Operation Methods

  • v1 Multi-step manual operations
  • v2 Step-by-step guided operations, reducing user burden
  • v3 Conversational one-stop operation, AI auto-executes full workflow
  • v4 Commercial one-stop research platform, global deployment

02 Evolution of Interface Design

  • v1 Single-function page
  • v2 Multi-page sequential workflow
  • v3 Single-page split-screen design, conversation left, execution right
  • v4 Unified global navigation, multi-language support, responsive design

03 Evolution of AI Collaboration

  • v1 Single AI assistant
  • v2 Multiple independent AI functions
  • v3 Commander mode, Study Agent coordinates overall workflow
  • v4 Multi-model strategy, intelligently switching LLMs based on tasks

$ ./analyze --type "commercial_model"

04 Evolution of Business Model

  • v1-v3 Feature validation phase, no business model
  • v4 Introduced token economy and subscriptions, balancing user experience with sustainability

Main Revenue Sources

Pay-as-you-go Tokens
Top-up and use
Pro Subscription
Monthly/Annual
Enterprise Solutions
Customized

$ ./architecture --visualize

Multi-Agent Collaboration System

Study Agent

Full-process coordinator, guiding users to clarify research needs

Scout

Discover target users

Interviewer

Conduct professional interviews

Persona

Simulate user responses

Tool Integration
reasoningThinking

Deep thinking analysis tool

interview

Automated interview management

scoutTaskChat

User discovery and persona building

generateReport

Report generation and rendering

$ ./conclusion

In just over two months, atypica.AI has completed its transformation from a single-function tool to a commercial research platform. The core evolution progressed from "multi-step operations" to "step-by-step guided operations," then to "conversational one-stop operations," and finally became a "commercial one-stop research platform."

The latest version not only maintains a clean user experience but also achieves commercial sustainability, supporting global deployment and multi-language environments. Through token economy and subscription models, it provides powerful and intuitive subjective world modeling tools for various types of users.

"The development journey of atypica.AI embodies our team's core philosophy: continuously lowering the barriers to user research through technological innovation, enabling everyone to easily gain deep insights, while establishing a sustainable business model to ensure the product continues creating value for users." — Project Lead

atypica@localhost:~

cat roadmap.txt

Future plans:
- Expand AI model selection, optimize performance for different tasks
- Enhance cross-language and cross-cultural research capabilities
- Develop advanced data visualization tools
- Add enterprise-level team collaboration and permission management
- Build developer APIs for custom research tools
- Optimize token usage efficiency, reduce research costs

git commit -m "Continue the journey..."

[main 3a7b56c] Continue the journey...
Ready for next chapter