For Product Teams
Build Complete Customer Experiences with 1-2 People
How Product Work Changes
Traditional vs. AI-Augmented Product Development
Traditional Product Development
AI-Augmented Product Development
Team of 20+ people (product, design, engineering, QA, compliance)
1-2 people (product + AI, or product/engineer + AI)
Spec → design → build → test → deploy: 3-6 months
Spec → build → iterate: days to weeks
Sequential handoffs, coordination overhead, waiting
Direct collaboration with AI—spec, validate feasibility, implement in real-time
Build one feature at a time (resource constraint)
Build complete experiences (house-buying journey, retirement planning, etc.)
"Can we build this?" is the question
"What should we build?" is the focus (execution speed is no longer the bottleneck)
Concrete Example: Building a House-Buying Experience
Traditional Approach
Team required: Product manager, UX designer, frontend engineer, backend engineer, QA, compliance officer, architect
Timeline: 4-6 months
What you build: One feature (e.g., mortgage calculator on website)
Process:
- Week 1-2: Product spec, stakeholder alignment
- Week 3-4: UX design, mockups, user testing
- Week 5-6: Architecture review, compliance review
- Week 7-14: Frontend development
- Week 15-18: Backend integration
- Week 19-20: QA testing
- Week 21-24: Bug fixes, final reviews, deployment
AI-Augmented Approach (with Factflow + Agent Prism)
Team required: 1 product person + AI (Claude Code)
Timeline: 1-2 weeks
What you build: Complete house-buying journey (mortgage calculator, personalized UI, budget tracker, document checklist, rate monitoring, orchestration across accounts/loans/insurance)
Process:
- Day 1: Spec the customer journey with AI, validate feasibility in real-time
- Day 2-3: Build personalized UI components with AI, leveraging Factflow for mortgage knowledge
- Day 4-5: Implement orchestration logic using Agent Prism (connect to accounts, loans, insurance systems)
- Day 6-7: Test with real customer scenarios, iterate based on feedback
- Week 2: Refine, add proactive monitoring (rate changes, document reminders), deploy
What You Get Access To
Factflow (Knowledge Layer)
What it does: Gives AI complete knowledge of DNB's products, policies, regulations
How you use it:
- Ask AI about any product, it knows the answer
- Build features that require cross-product knowledge (mortgages + savings + insurance)
- No need to manually research policies or regulations
Agent Prism (Orchestration Layer)
What it does: Enables AI to execute multi-step workflows across systems
How you use it:
- Define customer journeys (not just features)
- Orchestrate across accounts, loans, investments, KYC, fraud detection
- Template-based agents—configure and deploy in hours
AI-Augmented Development
What it does: You + AI work like a full product team
How you use it:
- Write specs, AI validates feasibility immediately
- Describe UI, AI generates code
- Define workflows, AI implements orchestration
- Iterate in hours, not weeks
What Changes for You
✅ You Can Do More
- Own end-to-end customer experiences (not just features)
- Build what used to require a full team
- Iterate based on customer feedback in days, not quarters
🔄 Your Role Evolves
- From: Writing specs, coordinating teams, waiting for delivery
- To: Defining customer value, collaborating with AI, shipping directly
- You become a "product engineer" or "AI-augmented product leader"
⚡ Speed Changes Everything
- Try 10 ideas to find the 1 that works (vs. betting everything on 1 idea)
- Learn what customers want in days, not months
- Product boundaries disappear—you can orchestrate across accounts, loans, investments because AI handles complexity
Evidence: We're Already Doing This
This conversation right now is the proof:
- Product leader + AI collaborating in real-time
- Creating a comprehensive strategic document
- In traditional model: Would require strategy consultants, writers, designers, weeks of coordination
- With AI augmentation: Real-time iteration, publication-ready output in one session
Apply this to product development:
- 1 person + Claude Code can build what used to require a full product team
- Spec writing, feasibility validation, implementation, testing, documentation—all done by the same human-AI pair
- Teams can own end-to-end customer experiences instead of individual features
Get Started
Want to Try This?
Radical AI is looking for product teams to adopt this model.
What we offer:
- Access to Factflow (knowledge layer) and Agent Prism (orchestration layer)
- Training on AI-augmented product development
- Support from Radical AI team
- Proof that you can build 10X faster with 1/10 the team size
What we need from you:
- Willingness to experiment with a new way of working
- One product initiative you can run as a pilot
- Commitment to share learnings (what worked, what didn't)