MVP Creation for AI-Based Knowledge Platform
Budget: $3,000 – $5,000 NZD
Platform MVP - Technical Overview
Project Summary
We're building a web-based knowledge platform that helps users navigate complex regulatory information through location-based queries and AI-powered assistance. The platform combines geospatial data, document intelligence, and natural language processing to deliver personalized guidance.
Core Features
Dual-Input System
Location lookup: Users input addresses which are geocoded to coordinates for location-specific data retrieval
Natural language queries: Users ask questions that are answered using context-aware AI
Combined workflow: Location + question = personalized, context-specific answers
Key Functionalities
Real-time data integration from multiple authoritative sources
AI-powered Q&A with retrieval-augmented generation (RAG)
Interactive map visualizations with data overlays
Document analysis and automated updates
User accounts with subscription management
Progressive web app with mobile-first design
Technology Stack
Frontend
Framework: Next.js 14+ (React 18)
Styling: Tailwind CSS
Maps: Mapbox GL / Google Maps (TBD based on requirements)
State Management: React Context / Zustand
PWA: Service workers for offline capability
Backend
Runtime: Node.js with Express or Next.js API routes
Database: PostgreSQL (via Supabase)
Authentication: Supabase Auth (JWT-based)
Payments: Stripe integration
Hosting: Vercel (serverless functions)
AI/ML Infrastructure
LLM: OpenAI GPT-4 API
Vector Database: Pinecone for semantic search
Document Processing: LangChain for RAG pipeline
Embeddings: OpenAI text-embedding-ada-002
Data Integration
Geocoding: Multiple provider support (Google, Mapbox, local providers)
GIS Services: RESTful APIs and WMS/WMTS endpoints
Document Ingestion: Automated scrapers for PDFs and web content
Change Detection: Scheduled jobs to monitor source updates
Technical Architecture
API Design
POST /api/lookup # Geocode address and fetch location data
POST /api/query # Process user questions with context
GET /api/location/:id # Retrieve cached location information
POST /api/documents # Upload and process documents
Data Flow
User inputs address → Geocoding API → Coordinates
Coordinates → GIS/Data APIs → Location context
User question + Context → Vector search → Relevant documents
Documents + Question → LLM → Contextual answer
Results → Frontend → Interactive visualization
Key Technical Challenges
Multi-source Integration
Standardizing data from various APIs
Handling different authentication methods
Managing rate limits and quotas
Performance Optimization
Caching strategies for expensive operations
Efficient vector search at scale
Fast map rendering with multiple overlays
Data Freshness
Automated change detection
Version control for regulatory documents
Update propagation to vector embeddings
Scalability Considerations
Serverless architecture for cost efficiency
Database connection pooling
CDN for static assets
Queue system for background jobs
Development Approach
Phase 1: Core MVP (Months 1-3)
Basic dual-input interface
Single region/data source integration
Essential AI Q&A functionality
User authentication and basic subscription
Phase 2: Enhancement (Months 4-6)
Multiple data source integration
Advanced mapping features
Document upload and analysis
Performance optimization
Phase 3: Scale (Months 6+)
Multi-region support
Mobile app development
Advanced analytics
Enterprise features
What We're Looking For
Technical Skills
Strong full-stack development experience
Proven ability to integrate multiple APIs
Experience with AI/LLM applications
Understanding of geospatial data
Clean, maintainable code practices
Soft Skills
Independent problem-solving
Clear communication
Ability to make architectural decisions
Interest in long-term collaboration
Next Steps
Selected candidates will:
Sign an NDA to receive detailed specifications
Review comprehensive technical documentation
Discuss specific implementation approaches
Provide time/cost estimates for MVP delivery
Note: This is a high-level overview. Detailed requirements, data sources, and business logic will be shared after NDA signing.
Project Summary
We're building a web-based knowledge platform that helps users navigate complex regulatory information through location-based queries and AI-powered assistance. The platform combines geospatial data, document intelligence, and natural language processing to deliver personalized guidance.
Core Features
Dual-Input System
Location lookup: Users input addresses which are geocoded to coordinates for location-specific data retrieval
Natural language queries: Users ask questions that are answered using context-aware AI
Combined workflow: Location + question = personalized, context-specific answers
Key Functionalities
Real-time data integration from multiple authoritative sources
AI-powered Q&A with retrieval-augmented generation (RAG)
Interactive map visualizations with data overlays
Document analysis and automated updates
User accounts with subscription management
Progressive web app with mobile-first design
Technology Stack
Frontend
Framework: Next.js 14+ (React 18)
Styling: Tailwind CSS
Maps: Mapbox GL / Google Maps (TBD based on requirements)
State Management: React Context / Zustand
PWA: Service workers for offline capability
Backend
Runtime: Node.js with Express or Next.js API routes
Database: PostgreSQL (via Supabase)
Authentication: Supabase Auth (JWT-based)
Payments: Stripe integration
Hosting: Vercel (serverless functions)
AI/ML Infrastructure
LLM: OpenAI GPT-4 API
Vector Database: Pinecone for semantic search
Document Processing: LangChain for RAG pipeline
Embeddings: OpenAI text-embedding-ada-002
Data Integration
Geocoding: Multiple provider support (Google, Mapbox, local providers)
GIS Services: RESTful APIs and WMS/WMTS endpoints
Document Ingestion: Automated scrapers for PDFs and web content
Change Detection: Scheduled jobs to monitor source updates
Technical Architecture
API Design
POST /api/lookup # Geocode address and fetch location data
POST /api/query # Process user questions with context
GET /api/location/:id # Retrieve cached location information
POST /api/documents # Upload and process documents
Data Flow
User inputs address → Geocoding API → Coordinates
Coordinates → GIS/Data APIs → Location context
User question + Context → Vector search → Relevant documents
Documents + Question → LLM → Contextual answer
Results → Frontend → Interactive visualization
Key Technical Challenges
Multi-source Integration
Standardizing data from various APIs
Handling different authentication methods
Managing rate limits and quotas
Performance Optimization
Caching strategies for expensive operations
Efficient vector search at scale
Fast map rendering with multiple overlays
Data Freshness
Automated change detection
Version control for regulatory documents
Update propagation to vector embeddings
Scalability Considerations
Serverless architecture for cost efficiency
Database connection pooling
CDN for static assets
Queue system for background jobs
Development Approach
Phase 1: Core MVP (Months 1-3)
Basic dual-input interface
Single region/data source integration
Essential AI Q&A functionality
User authentication and basic subscription
Phase 2: Enhancement (Months 4-6)
Multiple data source integration
Advanced mapping features
Document upload and analysis
Performance optimization
Phase 3: Scale (Months 6+)
Multi-region support
Mobile app development
Advanced analytics
Enterprise features
What We're Looking For
Technical Skills
Strong full-stack development experience
Proven ability to integrate multiple APIs
Experience with AI/LLM applications
Understanding of geospatial data
Clean, maintainable code practices
Soft Skills
Independent problem-solving
Clear communication
Ability to make architectural decisions
Interest in long-term collaboration
Next Steps
Selected candidates will:
Sign an NDA to receive detailed specifications
Review comprehensive technical documentation
Discuss specific implementation approaches
Provide time/cost estimates for MVP delivery
Note: This is a high-level overview. Detailed requirements, data sources, and business logic will be shared after NDA signing.