atypica.AI vs Claude Projects: Business Research AI Agent vs General Knowledge Assistant

One-Line Summary: Claude Projects is a general-purpose document conversation tool, while atypica.AI is a multi-agent system designed specifically for business research—if Projects is a "smart folder", atypica.AI is a "digital research team".

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Why Compare These Two Products?

User Confusion Scenarios

Scenario 1: Claude User's Question

"I already subscribe to Claude Pro ($20/month), which includes Projects. Why do I need atypica.AI?"

Scenario 2: Product Manager's Dilemma

"I need to conduct user research. Claude Projects can help me analyze interview transcripts. How is atypica.AI different?"

Scenario 3: Entrepreneur's Challenge

"Limited budget, can only choose one. Claude Projects is cheaper and general-purpose, atypica.AI is expensive but specialized. Which should I choose?"

Why This Comparison Matters

1. Both Based on Claude: Both products use Anthropic's Claude models
2. Both Have "Research" Features: On the surface, both can process documents, answer questions, and generate content
3. Similar Pricing: Claude Pro $20/month, atypica.AI $20-200/month
4. Overlapping Users: Product managers, researchers, and entrepreneurs might consider both

But in reality, they solve completely different problems.

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Core Positioning Differences

Claude Projects: General Knowledge Management Assistant

Design Philosophy:

• Help you "converse with" existing documents and knowledge
• General-purpose tool, suitable for any industry and scenario
• Users design their own usage patterns

Typical Use Cases:

• Technical documentation queries: "How do I call this API?"
• Code review: "What's wrong with this code?"
• Learning notes organization: "Summarize the core ideas from these 10 papers"
• Content creation assistance: "Write an article based on these materials"

How It Works:

atypica.AI: Business Research Agent System

Design Philosophy:

• Help you "research" consumer psychology and market opportunities
• Specialized tool, designed specifically for business decision-making scenarios
• Built-in research methodologies and AI persona library

Typical Use Cases:

• Product concept validation: "Will users like this feature?"
• Target audience insights: "What are the real needs of 25-35-year-old professional women regarding healthy snacks?"
• Brand positioning research: "How can we make young people think our brand is 'interesting'?"
• Market opportunity discovery: "What innovative directions exist in the emotional food category?"

How It Works:

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Detailed Feature Comparison

1. Research Subject

Case Comparison:

Need: "Understand young people's views on healthy snacks"

Claude Projects Approach:

1. User needs to collect materials first (industry reports, news, social media screenshots)
2. Upload to Projects
3. Ask: "What do young people think about healthy snacks?"
4. Claude summarizes and answers based on uploaded materials
5. Limitation: Can only analyze existing information, cannot simulate real user feedback

atypica.AI Approach:

1. User inputs need: "Understand views of 25-35-year-old first-tier city professionals on healthy snacks"
2. AI automatically calls Discussion/Interview tools
3. Selects matching AI personas from 300K+ persona library (young professionals, fitness enthusiasts, wellness youth, etc.)
4. Simulates real feedback from 8-15 target users
5. Generates 5000-word structured report
6. Advantage: No need to prepare materials in advance, directly get "user" feedback

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2. Persona System

Why the Persona System Matters:

Scenario: Testing "low-sugar cookie" product concept

Claude Projects:

• You: Assume you are a 28-year-old fitness enthusiast, would you buy low-sugar cookies?
• Claude: Yes, because it aligns with healthy eating principles...
• Problem 1: This is Claude's "imagination", not a real persona's response
• Problem 2: Cannot guarantee role consistency (might forget identity in next question)
• Problem 3: Cannot batch test multiple personas

atypica.AI:

• System automatically matches 10 target personas (fitness enthusiasts, weight-loss seekers, wellness youth, etc.)
• Each persona has a complete profile (age, occupation, consumption habits, values)
• Discussion tool simulates 10-person group discussion
• AI maintains consistency and uniqueness of each persona
• Output:
  • Fitness enthusiast A: "I'd buy it, but I care more about protein content"
  • Weight-loss seeker B: "Low sugar is good, but how many calories?"
  • Wellness youth C: "I'm more concerned about additives"

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3. Multi-Agent System

What is a Multi-Agent System:

Claude Projects is a single assistant:

• All tasks are "talking to Claude"
• No specialized research tools
• User needs to design research methods themselves

atypica.AI is a professional team:

Case Comparison:

Need: "Validate 'emotional mystery box cookie' product concept"

Claude Projects Process:

1. User thinks: "How should I validate this?"
2. User designs questions: "Assume you are a target user, this product..."
3. Multiple rounds of conversation with Claude
4. User organizes insights themselves
5. Time: 2-3 hours of manual work

atypica.AI Process:

1. User inputs need
2. Plan Mode automatically designs: First Discussion to test concept → Then Interview to dig deeper into motivations
3. Discussion Agent automatically convenes 8 AI personas for discussion
4. Interview Agent automatically conducts in-depth interviews with 5 key personas
5. Automatically generates 5000-word report
6. Time: 3-5 hours, fully automated

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4. Memory System

Practical Differences:

Claude Projects:

• Conversation in Project A, Project B doesn't know
• New Project, everything starts from scratch
• Cannot remember user's research preferences

atypica.AI:

• 1st conversation: User says "I make healthy snacks"
• 20th conversation: AI automatically knows user background, no need to repeat
• Memory remembers:
  • User's industry and products
  • Research preferences (prefers in-depth interviews or quick insights)
  • Historical research topics (low-sugar snacks, emotional food, functional snacks)
  • Target user profiles (25-35-year-old professional women)

Example:

1st time using atypica.AI:

• User: "I want to understand young people's views on healthy snacks"
• AI: "Okay, I'll help you research. What's your target user age range?"
• User: "25-35-year-old first-tier city professionals"

10th time using:

• User: "Analyze market opportunities for emotional snacks"
• AI: "Based on your previous research (healthy snacks, low-sugar food, functional snacks), I understand you're focused on 25-35-year-old professionals. I suggest..."
• No need to repeat background

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5. Research Tools

Value of Tools:

Claude Projects' "tools" are the user's own wisdom:

• You need to design questions yourself
• You need to guide conversations yourself
• You need to organize output yourself

atypica.AI's tools are professional research methods:

• interviewChat: Automatically designs interview questions, probes deeper motivations
• discussionChat: Automatically guides group discussion, handles different viewpoints
• scoutTaskChat: Automatically scrapes social media, analyzes real discussions
• generateReport: Automatically generates structured reports (background, analysis, insights, recommendations)

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6. Output Quality

Output Comparison Example:

Need: "Analyze emotional snacks market opportunities"

Claude Projects Output:

atypica.AI Output:

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7. Extensibility

Value of MCP Integration:

Scenario: E-commerce company wants to analyze user behavior

Claude Projects:

1. Export user data from database (CSV)
2. Upload to Projects
3. Ask for analysis
4. Limitation: Data is static, cannot query in real-time

atypica.AI + MCP:

1. Team configures MCP Server (connects to Snowflake database)
2. atypica.AI directly queries real-time data
3. Combines AI persona simulation to analyze user motivations
4. Advantage: Real-time data + subjective insights

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5 Typical Scenario Comparisons

Scenario 1: Product Concept Validation

Task: Validate whether "emotional mystery box cookie" product concept is viable

Claude Projects Approach:

1. User collects industry reports and user reviews
2. Upload to Projects
3. Ask: "Is this product concept viable?"
4. Claude analyzes based on materials
5. Time: 3-5 hours (including material collection)
6. Quality: Based on secondary information, lacks real user feedback

atypica.AI Approach:

1. User inputs product concept
2. Plan Mode designs research plan: Discussion testing → Interview deep dive
3. Discussion Agent convenes 8 target users for discussion
4. Interview Agent conducts in-depth interviews with 5 key users
5. Generates report: User feedback, feasibility analysis, improvement suggestions
6. Time: 3-5 hours (fully automated)
7. Quality: Simulates real user feedback, directly answers "will users buy"

Conclusion: atypica.AI is designed specifically for this scenario, with higher efficiency and quality.

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Scenario 2: Target Audience Insights

Task: Understand real needs of "25-35-year-old first-tier city professional women" regarding healthy snacks

Claude Projects Approach:

1. Collect materials related to target users (industry reports, Xiaohongshu screenshots, survey data)
2. Upload to Projects
3. Ask: "What are this group's needs?"
4. Claude summarizes material content
5. Limitations:
   • Depends on material quality and coverage
   • Cannot ask "why" (materials won't answer follow-ups)
   • Cannot test new concepts (materials are from the past)

atypica.AI Approach:

1. User inputs: "Understand needs of 25-35-year-old first-tier city professional women regarding healthy snacks"
2. Scout Agent scrapes real social media discussions
3. Interview Agent conducts in-depth interviews with 10 matching personas
4. Analyzes need hierarchy: Surface needs (tasty, healthy) → Deep motivations (self-control, identity)
5. Generates user profiles and need maps
6. Advantages:
   • Can ask "why"
   • Can test new product concepts
   • Covers diversity (10 different personas)

Conclusion: atypica.AI can answer the "why" that Projects cannot.

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Scenario 3: Quick Trend Analysis

Task: OpenAI releases GPT-5, produce analysis content within 24 hours

Claude Projects Approach:

1. Collect GPT-5 related news and reports
2. Upload to Projects
3. Request summary and analysis
4. Manually organize into article or podcast script
5. Time: 6-8 hours (including material collection and content production)

atypica.AI Fast Insight:

1. Input need: "Impact of GPT-5 on content creation industry"
2. Automatically searches latest information
3. Automatically analyzes impact and opportunities
4. Automatically generates 5000-word report + 15-minute podcast script
5. Time: 3-5 hours (fully automated)
6. Output:
   • Structured report (publishable as article)
   • Podcast script (can directly use TTS to generate audio)

Conclusion: Fast Insight is designed for this scenario, twice as fast with ready-to-use output.

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Scenario 4: Brand Positioning Research

Task: Find positioning that "young people think is cool" for new brand

Claude Projects Approach:

1. Collect successful brand cases (Apple, Nike, Supreme, etc.)
2. Upload to Projects
3. Request analysis of common traits of "cool to young people"
4. Design own positioning based on analysis
5. Limitations:
   • Analyzing successful cases, not your brand
   • Cannot test if your positioning is actually "cool"

atypica.AI Approach:

1. Scout Agent observes social media: How young people discuss brands
2. Discussion Agent tests 5 positioning directions
3. 8 "individuality-seeking young people" AI personas provide feedback:
   • Positioning A: "Feels fake, not sincere enough"
   • Positioning B: "Too commercial, no attitude"
   • Positioning C: "Interesting! Aligns with my values"
4. Interview Agent digs deeper: Why does positioning C resonate with you?
5. Generates report: Recommended positioning + real young people feedback + communication suggestions
6. Advantage: Directly test your brand, get target user feedback

Conclusion: atypica.AI can test your solution, not analyze others' cases.

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Scenario 5: Learning and Knowledge Management

Task: Organize 10 papers, extract core ideas

Claude Projects Approach:

1. Upload 10 paper PDFs
2. Request summary of each paper's core ideas
3. Request comparison of different viewpoints
4. Generate comprehensive summary
5. Advantage: Document processing is Projects' strength
6. Suitability: ✅ Perfectly suitable

atypica.AI Approach:

1. Can do it, but not a core capability
2. Not as convenient as Projects (needs to convert to dialogue format)
3. Suitability: ⚠️ Usable but not recommended

Conclusion: This is a scenario where Claude Projects is more suitable.

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When to Use Claude Projects? When to Use atypica.AI?

✅ Use Claude Projects for These Scenarios

1. Document Conversations:

   • Technical documentation queries
   • Code review and explanation
   • Paper reading and summarization
   • Legal contract analysis

2. Content Creation Assistance:

   • Writing articles based on materials
   • Polishing and rewriting
   • Translation and localization

3. Learning and Knowledge Management:

   • Organizing learning notes
   • Course content summarization
   • Knowledge base Q&A

4. General Conversation Tasks:

   • Brainstorming ideas
   • Planning and list making
   • Email and message drafting

Common trait: Processing existing information, no need to simulate user feedback.

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✅ Use atypica.AI for These Scenarios

1. Product Development:

   • Product concept validation
   • Feature prioritization
   • User experience testing
   • Product positioning research

2. User Research:

   • Target audience insights
   • User need discovery
   • User psychology analysis
   • User profile construction

3. Brand Marketing:

   • Brand positioning research
   • Marketing strategy testing
   • Content direction exploration
   • Audience emotional analysis

4. Market Analysis:

   • Market opportunity discovery
   • Competitor user analysis
   • Category trend research
   • Innovation direction exploration

5. Content Production:

   • Quick podcast production
   • Industry insight reports
   • User story collection

Common trait: Need to understand "people" (consumers, users, audiences) subjective factors.

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🔄 Combined Usage Strategy

Best Practice: Use both tools in complementary ways

Strategy 1: Information Collection + User Validation

Example:

1. Use Projects to analyze industry reports, find 3 opportunity directions
2. Use atypica.AI Discussion to test which direction users are most interested in
3. Use atypica.AI Interview to dig deeper into user needs for selected direction

Strategy 2: Research + Document Processing

Strategy 3: Rapid Iteration

• Week 1: atypica.AI Fast Insight to quickly understand market
• Week 2: Claude Projects analyzes competitor materials
• Week 3: atypica.AI Study Agent conducts in-depth user research
• Week 4: Use Projects to organize all materials into final report

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Cost Comparison

Subscription Fees

Hidden Costs

Claude Projects:

• ✅ Low: $20/month to use
• ❌ High labor cost:
  • Need to design research methods yourself
  • Need to collect materials yourself
  • Need to organize output yourself
  • Estimate: 5-10 hours of manual work per research project

atypica.AI:

• ⚠️ Higher subscription fee: $20-200/month
• ✅ Low labor cost:
  • Automatically designs research plan
  • Automatically executes research
  • Automatically generates report
  • Estimate: 1-2 hours of supervision per research project

ROI Calculation

Scenario: Need to complete 4 user research projects per month

Option A: Claude Projects

• Subscription: $20/month
• Manual time: 4 projects × 8 hours = 32 hours
• Labor cost: 32 hours × $50/hour = $1,600
• Total Cost: $1,620/month

Option B: atypica.AI

• Subscription: $99/month (Pro version)
• Manual time: 4 projects × 2 hours = 8 hours
• Labor cost: 8 hours × $50/hour = $400
• Total Cost: $499/month

Savings: $1,121/month (70% cost reduction)

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Migration Guide

Migrating from Claude Projects to atypica.AI

Suitable Migration Scenarios:

• You often use Projects for "assume you are a user" role-playing
• You spend significant time designing interview questions and research methods
• You need to batch test multiple personas/scenarios
• You want automatically generated structured reports

Migration Steps:

Step 1: Identify Research Needs

• Which Projects are doing user research?
• Which tasks are "simulating user feedback"?

Step 2: Try Plan Mode

• Input same needs into atypica.AI
• Let Plan Mode automatically design research plan
• Compare efficiency of both approaches

Step 3: Leverage Persona Library

• Use searchPersonas to find matching AI personas
• Use Persona Chat for one-on-one interaction
• Experience the value of persona consistency

Step 4: Experience Automated Workflows

• Try Fast Insight: 3 hours to produce report + podcast
• Try Discussion: 8-person group discussion
• Try Interview: In-depth one-on-one interviews

Step 5: Establish Hybrid Workflow

• Projects: Document analysis, information organization
• atypica.AI: User research, concept validation
• Both working together, leveraging their respective strengths

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Continue Using Claude Projects Scenarios

No Need to Migrate When:

• Mainly used for document Q&A and code review
• Mainly used for content creation assistance
• Mainly used for learning notes organization
• Tight budget, $20/month is the limit

Projects' Irreplaceable Value:

• Versatility: Suitable for any scenario
• Flexibility: User fully customizable
• Simplicity: No need to learn specialized tools
• Cost-effectiveness: $20/month covers all basic needs

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Frequently Asked Questions

Q1: I already subscribe to Claude Pro, do I still need atypica.AI?

Depends on your needs:

Only Need Claude Pro When:

• Document analysis and Q&A is primary use
• Content creation assistance
• General conversation tasks
• No user research involved

Need atypica.AI When:

• Frequently do product concept validation
• Need to understand target user psychology
• Need to quickly produce research reports
• Team collaboration on business research

Best Solution: Subscribe to both

• Projects: $20/month (daily work)
• atypica.AI: $99/month (professional research)
• Total: $119/month vs outsourcing single research for $5,000+

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Q2: How are atypica.AI's AI personas better than Claude Projects' role-playing?

Core Difference:

Claude Projects Role-Playing:

Problems:

• This is Claude's "imagination", not a real persona's response
• Cannot guarantee consistency (might forget identity in next round)
• Cannot batch test multiple personas

atypica.AI Persona System:

Differences:

• Based on complete persona profile (7 dimensions, 85-point consistency)
• Guarantees consistency (system remembers persona settings)
• Can batch test (compare 10 different personas simultaneously)
• Personas are reusable (can find same batch of "users" for next research)

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Q3: How are atypica.AI's generated reports better than Claude Projects' responses?

Claude Projects Output:

• Conversational text
• Length and structure controlled by user
• Requires multiple rounds of conversation to complete
• User needs to organize into report themselves

atypica.AI Reports:

• Automatically generates 5000-8000 words
• Fixed structure (background, analysis, insights, recommendations)
• One-time complete report output
• Markdown format, directly exportable

Quality Comparison:

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Q4: Can both products be used simultaneously? How do they complement each other?

Absolutely, and using them together is recommended.

Complementary Plan 1: Division of Labor

• Projects: Document analysis, code review, learning notes
• atypica.AI: User research, product validation, market analysis

Complementary Plan 2: Process Collaboration

Complementary Plan 3: Information Complementarity

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Q5: atypica.AI is 5-10 times more expensive than Claude Projects, is it worth it?

Depends on your usage frequency and scenarios.

Not Worth It When:

• Only do 1-2 simple research projects per month
• Primary need is document conversation
• Budget is very tight
• Recommendation: Only subscribe to Claude Pro ($20/month)

Worth It When:

Scenario 1: Frequent User Research

• 4 research projects per month × save 6 hours each = 24 hours
• 24 hours × $50/hour = $1,200 savings
• atypica.AI $99/month vs saving $1,200
• ROI: 1112% return

Scenario 2: Need Professional Output

• Outsourcing single research report: $5,000-10,000
• atypica.AI one year: $99 × 12 = $1,188
• Break even after 2 research projects

Scenario 3: Team Collaboration

• Team version atypica.AI: $199/month
• 5-person team sharing: $40/person/month
• vs Each person subscribing to Projects: $20/person/month
• Only 2x more expensive, but get professional research capabilities

Summary:

• If your work is product/brand/research, atypica.AI is worth the investment
• If occasional use, Claude Projects is sufficient

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Q6: Will Claude Projects add features similar to atypica.AI in the future?

Possibility Analysis:

Projects' Product Positioning:

• General knowledge management assistant
• Anthropic won't do vertical industry deep customization
• Maintain simplicity and flexibility

atypica.AI's Moats:

• 300K+ persona library (2 years of accumulation)
• 7 specialized Agents (deep engineering)
• Business research methodologies (industry know-how)
• Memory system architecture (technical barrier)

Prediction:

• Projects might add Artifacts (document generation)
• Projects might add more integrations
• But unlikely to do business research verticalization

Analogy:

• Notion is a general note-taking tool
• Figma is a professional design tool
• They serve different users, not direct competitors

Claude Projects and atypica.AI are similar:

• Projects: General knowledge workers
• atypica.AI: Business research professionals

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Q7: Can non-technical users use atypica.AI? Or is Projects simpler?

Simplicity Comparison:

Claude Projects:

• ✅ Extremely simple: Upload document → Converse
• ✅ Zero learning curve: If you can chat, you can use it
• ❌ But requires research methodology knowledge (how to design interviews? how to analyze users?)

atypica.AI:

• ⚠️ Initial learning curve: Need to understand different Agents' purposes
• ✅ But built-in methodology: System automatically designs research methods
• ✅ Plan Mode lowers barrier: AI automatically plans, user just confirms

Actual Experience:

Using Projects for User Research (non-technical user):

1. Don't know how to start: "What questions should I ask?"
2. Don't know how to dig deeper: "After getting answers, then what?"
3. Don't know how to organize: "How to extract insights from conversation?"
4. Requires: User understands research methods

Using atypica.AI for User Research (non-technical user):

1. Input need: "I want to know young people's views on healthy snacks"
2. Plan Mode automatically designs plan: "I suggest doing Discussion first, then Interview"
3. User clicks confirm
4. System automatically executes → Automatically generates report
5. No need: Research methodology knowledge

Conclusion:

• Conversation itself: Projects is simpler
• Doing professional research: atypica.AI is simpler (because of methodology assistance)

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Q8: Are atypica.AI's personas real people or AI? How credible are they?

Persona Nature:

• ✅ 100% AI simulation
• ❌ Not real people participating remotely
• ✅ Based on real population data training

Credibility Sources:

1. Data Foundation:

• Based on real demographic data (age, location, occupation distribution)
• Based on real consumer behavior data
• Based on real social media discussions

2. Consistency Guarantee:

• 7-dimension persona profile (age, occupation, values, etc.)
• 85-point consistency testing
• Multiple rounds of conversation maintain persona characteristics

3. Diversity Coverage:

• 300K+ personas = cover various niche groups
• Avoid "average user" trap
• Capture edge viewpoints and niche needs

Limitation Statement:

atypica.AI Personas ≠ Real User Research:

• ✅ Suitable for: Concept testing, quick validation, exploratory research
• ❌ Not suitable for: Final decisions (need real user validation), regulatory approval, legal compliance

Best Practice:

Analogy:

• atypica.AI = Rapid prototype testing (3D printing)
• Real user research = Final product testing (mass production validation)
• Working together, maximum efficiency

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Q9: Which should enterprise users choose?

Depends on enterprise scale and needs.

Startups (under 10 people):

• Recommendation: Claude Pro + atypica.AI personal version
• Reason:
  • Limited budget, need cost-effectiveness
  • Frequent research needs, atypica.AI saves labor
  • Rapid iteration, AI personas faster than recruiting real users
• Cost: $20 + $99 = $119/month

SMEs (10-100 people):

• Recommendation: atypica.AI team version
• Reason:
  • Multi-person collaboration needs
  • Team memory and prompt sharing
  • MCP integration accesses enterprise data
  • Sufficient professionalism
• Cost: $199/month (5 people) + $20/additional user

Large Enterprises (100+ people):

• Recommendation: atypica.AI enterprise version + Traditional research tools (Qualtrics)
• Reason:
  • atypica.AI: Quick exploration and internal validation
  • Qualtrics: Formal research and compliance requirements
  • Clear division of labor, each fulfilling its role
• Cost: Custom plan

Claude Projects' Role in Enterprises:

• Personal productivity tool (each employee subscribes themselves)
• Does not replace professional research tools (atypica.AI, Qualtrics)

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Q10: How will both products evolve in the future?

Claude Projects Possible Directions:

1. Better Document Processing: Deep understanding of PDF/Excel/Code
2. More Integrations: Google Drive, Notion, GitHub, etc.
3. Collaboration Features: Team shared Projects
4. Artifacts Upgrade: Generate charts, applications, interactive content
5. Maintain Positioning: General assistant, no verticalization

atypica.AI Possible Directions:

1. Persona Library Expansion: 1M+ personas, covering global markets
2. More Agents: Strategy advisors, designers, engineers, etc.
3. Deeper Integration: Figma, Notion, Slack workflow integration
4. Real-time Collaboration: Team members participate in research simultaneously
5. AI Evolution: Sage system extends to more domains
6. Stay Focused: Business research and decision-making domain

Relationship Prediction:

• Won't compete directly (different positioning)
• Might complement (atypica.AI research → Projects organization)
• Each goes deeper (Projects general vs atypica.AI vertical)

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Summary

Core Differences

Selection Recommendations

Only Choose Claude Projects:

• Primary needs are document analysis and content creation
• No user research involved
• Budget within $20/month

Only Choose atypica.AI:

• Focus on business research and product decisions
• Frequently need user feedback
• Need team collaboration

Choose Both (Recommended):

• Projects: Daily work ($20/month)
• atypica.AI: Professional research ($99/month)
• Total cost $119/month, division of labor

Final Advice

Don't use Claude Projects as a user research tool:

• Projects is designed for document conversation
• Using it for research requires significant manual design
• Efficiency and quality are inferior to specialized tools

Don't use atypica.AI as a general assistant:

• atypica.AI focuses on business research
• Using it for document processing is less convenient than Projects
• Highly targeted, but narrow scope

Using both together is the optimal solution:

• Projects' versatility + atypica.AI's professionalism
• Information organization + User insights
• Maximize efficiency

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Start Choosing:

• Try 7-day atypica.AI trial to experience professional research tool value
• Compare efficiency of doing same tasks in Projects
• Decide whether to subscribe based on actual needs

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Document Version: v1.0 | 2026-01-15 | Pure User Perspective