Technical Specifications
Enterprise-grade AI infrastructure built for scale, security, and performance. Detailed architecture and machine learning specifications for technical evaluation.
System Architecture
Frontend Layer
- • React 19 + TypeScript
- • Tailwind CSS 4 (OKLCH)
- • Wouter (client-side routing)
- • tRPC client (type-safe API)
- • Recharts (data visualization)
Backend Layer
- • Node.js 22 + Express
- • tRPC (end-to-end type safety)
- • Drizzle ORM
- • PostgreSQL 15
- • JWT authentication
AI/ML Layer
- • Python 3.11 microservices
- • LightGBM (propensity scoring)
- • GPT-4 (content generation)
- • scikit-learn (segmentation)
- • pandas + NumPy (data processing)
Data Flow Architecture
- Data Ingestion: CSV upload or CRM API integration → Data validation & normalization → PostgreSQL storage
- Feature Engineering: RFM+ calculation (Recency, Frequency, Monetary, Engagement, Tenure) → Feature vector generation
- ML Inference: LightGBM model predicts propensity scores, churn risk, LTV → Results cached in Redis
- Content Generation: GPT-4 API with donor context → Personalized message generation → A/B variant creation
- Campaign Execution: Multi-channel orchestration (email, SMS, phone) → Delivery tracking → Response monitoring
- Analytics Pipeline: Real-time event streaming → Aggregation & metrics calculation → Dashboard updates
Machine Learning Models
Algorithm
LightGBM Gradient Boosting with transfer learning for low-data environments
Features (RFM+)
- • Recency: Days since last donation
- • Frequency: Total donation count
- • Monetary: Lifetime donation value
- • Engagement: Email open/click rates
- • Tenure: Months as active donor
- • Seasonality: Donation timing patterns
Training Data
Pre-trained on 500K+ anonymized nonprofit donor records. Fine-tuned per organization with minimum 100 donors.
Performance Metrics
- • AUC-ROC: 0.87 (validation set)
- • Precision@10%: 0.82
- • Inference time: <50ms per donor
Algorithm
Random Forest Classifier with SMOTE for class imbalance
Prediction Window
90-day forward-looking churn probability (0-100%)
Key Features
- • Donation frequency decline rate
- • Email engagement drop-off
- • Time since last interaction
- • Donation amount volatility
- • Response sentiment scores
Performance Metrics
- • Recall@20%: 0.78 (catches 78% of churners)
- • False positive rate: 12%
- • Early warning: 60-90 days advance notice
Model
GPT-4 Turbo with custom fine-tuning on nonprofit fundraising corpus
Personalization Inputs
- • Donor segment & propensity score
- • Donation history & preferences
- • Past interaction sentiment
- • Campaign goals & impact stories
- • Optimal ask amount (from model)
Output Variants
Generates 3-5 A/B test variants per donor with different subject lines, CTAs, and messaging angles
Quality Control
- • Sentiment validation (positive tone)
- • Factual accuracy checks
- • Brand voice consistency scoring
- • Human-in-the-loop review workflow
Algorithm
XGBoost time-series model with donor-specific engagement patterns
Prediction Granularity
Hourly predictions for next 7 days, personalized per donor
Training Features
- • Historical open/click timestamps
- • Day of week & time of day patterns
- • Timezone & geographic location
- • Campaign type & urgency level
Performance Metrics
- • Open rate lift: +18% vs. random timing
- • Click rate lift: +23% vs. batch send
- • Conversion rate lift: +15%
Security & Compliance
Encryption: AES-256 at rest, TLS 1.3 in transit
Access Control: Role-based permissions (RBAC) with multi-factor authentication
Data Isolation: Multi-tenant architecture with logical database separation
Backup & Recovery: Automated daily backups with 30-day retention, 4-hour RPO
Audit Logging: Comprehensive activity logs for all data access and modifications
GDPR Compliant: Right to access, rectification, erasure, and data portability
CCPA Compliant: California Consumer Privacy Act requirements met
SOC 2 Type II: (In progress) Third-party security audit certification
Data Residency: US-based data centers (AWS us-east-1) with EU option available
PII Handling: Donor data anonymized for ML training, never shared with third parties
Infrastructure & Scalability
Cloud Infrastructure
- • Hosting: AWS (primary), Azure (DR)
- • Compute: ECS Fargate (auto-scaling)
- • Database: RDS PostgreSQL (Multi-AZ)
- • Cache: ElastiCache Redis
- • Storage: S3 (donor files, ML models)
- • CDN: CloudFront (global edge caching)
Performance
- • API Latency: p95 < 200ms
- • Dashboard Load: < 2 seconds
- • ML Inference: < 100ms per donor
- • Concurrent Users: 10,000+ supported
- • Throughput: 1M+ emails/hour
- • Uptime SLA: 99.9% (8.76 hours/year)
Monitoring & Observability
- • APM: DataDog (real-time metrics)
- • Logging: CloudWatch + ELK stack
- • Alerting: PagerDuty (24/7 on-call)
- • Error Tracking: Sentry
- • Synthetic Monitoring: Pingdom
- • Cost Optimization: AWS Cost Explorer
API Overview
tRPC API Endpoints
End-to-end type-safe API with automatic TypeScript inference. All endpoints require JWT authentication.
donors.* - Donor CRUD operations, segmentation, propensity scoring
donations.* - Transaction tracking, revenue analytics
campaigns.* - Campaign management, message generation, A/B testing
analytics.* - Dashboard metrics, time-series data, benchmarking
interactions.* - Donor journey tracking, multi-channel orchestration
Rate Limits
- • Standard Tier: 1,000 requests/hour per organization
- • Pro Tier: 10,000 requests/hour per organization
- • Enterprise Tier: Custom limits, dedicated infrastructure
Webhooks
Real-time event notifications for donation received, campaign completed, churn alert triggered, and more. HMAC-SHA256 signature verification for security.
Technical Roadmap
Q2 2026 - Core AI Features
- • Propensity Scoring & Churn Prediction (production)
- • Generative Outreach Engine (GPT-4 fine-tuning)
- • Optimal Send Time Intelligence
- • Smart Ask Amount Recommendations
Q3 2026 - Multi-Channel Expansion
- • SMS & Voice AI integration
- • Sentiment Analysis (NLP pipeline)
- • Mobile app (React Native)
- • Social media orchestration
Q4 2026 - Advanced Intelligence
- • Grant Writing AI (GPT-4 + RAG)
- • Predictive Major Gifts (deep learning)
- • Peer Benchmarking (federated learning)
2027-2028 - Enterprise Scale
- • Wealth Screening API integration
- • Multi-language support (12+ languages)
- • On-premise deployment option
- • Advanced ML model customization
Technical Questions?
For detailed API documentation, integration support, or custom deployment requirements, contact our technical team.