Getting Started
Getting Started with Resurge
This guide will help you understand the basics of Resurge and get you started on your first AI-powered social impact project.
Prerequisites
Before you begin, make sure you have:
- A clear understanding of your organization's mission
- Identified a specific problem you want to solve
- Access to relevant data (beneficiary feedback, program metrics, etc.)
- Basic technical knowledge or a technical team member
Step 1: Explore AI Plays
Start by browsing our AI Plays catalog to see examples of AI solutions built for social impact:
- Beneficiary Feedback Analytics - Analyze thousands of surveys with NLP
- Donor Intelligence - Predict donor behavior and optimize outreach
- Adaptive Learning - Personalize education for rural students
- Satellite Monitoring - Track deforestation in real-time
- Localization Pipeline - Translate materials into multiple languages
Step 2: Define Your Use Case
Answer these key questions:
What problem are you solving?
Be specific about the challenge. For example:
- "We receive 10,000+ beneficiary surveys but can't analyze them all"
- "Our donors aren't engaged, and we don't know why"
- "Students in rural areas need personalized learning paths"
Who benefits?
Identify your stakeholders:
- Direct beneficiaries (students, patients, community members)
- Program staff (teachers, healthcare workers, field teams)
- Leadership (decision-makers who need insights)
What data do you have?
Catalog your available data:
- Structured data (databases, spreadsheets)
- Unstructured data (text surveys, images, videos)
- Real-time data (sensor readings, location tracking)
Step 3: Choose Your Approach
Option A: Start with an AI Play
Use one of our pre-built solutions as a starting point. Benefits:
- Faster time to value
- Proven methodology
- Lower risk
Option B: Build Custom
Create a tailored solution from scratch. Benefits:
- Perfect fit for your needs
- Full control and flexibility
- Opportunity to innovate
Step 4: Gather Your Team
Assemble a cross-functional team:
- Domain Expert: Understands the problem deeply
- Technical Lead: Manages implementation
- Data Specialist: Handles data quality and privacy
- Program Manager: Connects tech to impact
Step 5: Start Small, Scale Fast
Follow our proven methodology:
- Pilot (Weeks 1-2): Build MVP with sample data
- Test (Weeks 3-4): Validate with real users
- Refine (Weeks 5-6): Incorporate feedback
- Scale (Weeks 7-8): Deploy to full program
Common Pitfalls to Avoid
- ❌ Starting without clear success metrics
- ❌ Ignoring data quality and privacy
- ❌ Building in isolation without user feedback
- ❌ Over-engineering before validation
Next Steps
Ready to dive deeper? Check out:
- Guides - Detailed implementation tutorials
- AI Plays - Real-world examples
- Best Practices - Learn from successful deployments
Need help? Our team is here to support you every step of the way.