Document Analysis and Verification

We're deploying AI systems to analyze background checks, certifications, and credentials for authenticity assessment.

Technical Requirements:

• Multi-model AI integration (ChatGPT, Gemini, custom models) for document analysis
• Computer vision for document authenticity detection
• Risk assessment algorithms for identifying potential issues
• Training data management for improving model accuracy
• Secure handling of sensitive verification documents

This requires experience with AI model integration, document processing, and building systems that handle sensitive data appropriately.

Infrastructure and Scale

Serverless Architecture

We're building on serverless infrastructure to handle variable loads as we scale from 10k to 100k users. This includes:

• NeonDB for serverless database performance optimization
• Auto-scaling functions for AI processing workloads
• Event-driven architecture for real-time features
• Database performance tuning for concurrent user growth

Technology Stack

• Frontend: Next.js with full-stack TypeScript
• AI/ML: Vector databases, LLM APIs, custom model training
• Data Science: Vector matching, embedding optimization, recommendation systems
• Infrastructure: Serverless deployment, NeonDB, event-driven systems

Additional Automation Projects

Beyond core platform features, we're implementing AI automation across multiple areas:

Reference Check Automation: NLP systems for conducting and analyzing reference checks with consistent evaluation criteria.

Development Acceleration: AI-assisted development tools for faster code generation, testing, and review processes.

Content Operations: Automated content generation, enhancement, and optimization for marketing and user communications.

Marketing Automation: Campaign systems using user behavior analysis and automated optimization based on performance data.

Content Processing: Automated content cropping, formatting, and reposting across different platforms and specifications.

What We're Looking For

Senior developers with experience in:

• AI/ML system integration and production deployment
• Real-time system architecture and performance optimization
• Full-stack TypeScript development with Next.js
• Data science approaches to matching and recommendation problems
• Serverless architecture and database performance optimization
• Vector databases and semantic search implementation

Technical Challenges You'll Work On:

• Scaling AI-powered features from 10k to 100k users
• Optimizing database performance with NeonDB in serverless environments
• Building real-time systems that integrate multiple AI models
• Implementing vector search and matching algorithms
• Computer vision pipeline development and optimization
• Document processing and analysis systems

Engineering Environment

We work with:

• Next.js and TypeScript for full-stack development
• Vector databases for semantic search and matching
• Multiple AI model APIs and training pipelines
• NeonDB for serverless database optimization
• Real-time WebSocket and event-driven architectures
• Computer vision frameworks for image processing
• Serverless cloud infrastructure optimized for variable AI workloads

The technical problems require building systems that perform well under load while integrating cutting-edge AI capabilities.

Current State

Let's say we're at 10k users and planning infrastructure for 100k users. The technical challenges involve both building new AI-powered features and scaling existing systems to handle increased load.

Most of our current challenges don't have established patterns or obvious solutions. They require engineering judgment, performance optimization, and integration of multiple complex systems.

Interested in working on these problems?

Dancing Dragons is hiring senior software developers. More information about our platform and approach is available at dancingdragons.cc.

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