DriftKit Framework

Production-ready AI framework for Java - Complete prompt lifecycle management from development to production

🚀 Why choose DriftKit?

Framework comparison

🎯 Unique features

1. Complete prompt lifecycle platform - The ONLY framework with full Dev→Test→Prod workflow
2. Prompt state machine: DRAFT → AUTO_TESTING → MANUAL_TESTING → CURRENT → REPLACED
3. Version control, A/B testing, and folder organization
4. Test sets with multiple evaluation methods

5. Cost tracking (USD), token usage, and latency percentiles

6. Production tracing with cache metrics - Real-time observability for every LLM call:

7. Unified cache metrics across Claude (prompt cache), OpenAI (auto cache), DeepSeek (prefix cache)
8. Per-request cache hit/write/miss token counts with hit ratio
9. Hierarchical agent tracing (SequentialAgent, LoopAgent)

10. Expandable conversation context and system message display

11. Prompt Playground - Side-by-side prompt comparison:

12. Execute two prompts against the same variables
13. Dataset sweep mode — run against entire test sets

14. Pipeline playground — test prompt overrides in production pipelines

15. Workflow as maintainable graph - Build complex agents with cross-workflow composition

16. Simplified LLM SDK - High-level Agent API for quick prototyping and production
17. Hot-swap AI models - Change models via config without code changes or recompilation
18. Type-safe AI integration - Direct Java objects, no JSON parsing needed
19. Multi-agent orchestration - Loop, Sequential, and Hierarchical patterns
20. Built-in audio processing - VAD, transcription, and streaming capabilities
21. Spring AI integration - Use DriftKit prompts with Spring AI ChatClient, full tracing support

🏆 Business solutions

Customer support automation

Problem: Support teams overwhelmed with repetitive inquiries, inconsistent responses, high costs
Solution: DriftKit automates 80% of common requests while maintaining brand voice

Technical Implementation: - driftkit-context-engineering: Create and A/B test response templates for different customer scenarios - driftkit-workflow-engine-core: Intelligent routing - simple questions to AI, complex issues to specialists - driftkit-vector: Knowledge base search for accurate, up-to-date information - driftkit-clients: Multi-model support (GPT-4/Gemini 2.5 Pro/Claude Opus 4 for complex, GPT-4o-mini/Gemini 2.5 Flash/Claude Haiku for simple queries) - driftkit-common: Conversation memory to maintain context across multiple interactions

Business Impact: 60% reduction in response time, 40% cost savings, 95% customer satisfaction

Financial document processing

Problem: Manual processing of contracts, invoices, compliance documents - slow, error-prone, expensive
Solution: Intelligent document analysis with 99%+ accuracy and structured data extraction

Technical Implementation: - driftkit-clients: Multi-modal AI (GPT-4 Vision/Gemini 2.5/Claude with vision) for processing PDFs, images, scanned documents - driftkit-embedding: Document similarity for duplicate detection and categorization
- driftkit-vector: Store processed documents for quick retrieval and compliance auditing - driftkit-workflow-engine-core: Multi-step validation workflows with human-in-the-loop for critical decisions - driftkit-common: Structured output extraction directly into your ERP/accounting systems

Business Impact: 90% faster processing, 95% error reduction, full compliance automation

E-commerce personalization engine

Problem: Generic product recommendations, poor conversion rates, high customer acquisition costs
Solution: AI-powered product matching and hyper-personalized customer journeys

Technical Implementation: - driftkit-vector: Product catalog embeddings for intelligent similarity matching - driftkit-embedding: Customer behavior analysis and preference modeling - driftkit-context-engineering: Dynamic product description templates for different customer segments - driftkit-workflow-engine-core: Real-time recommendation pipelines with A/B testing - driftkit-clients: Multi-model optimization (fast models like GPT-4o-mini/Gemini Flash/Claude Haiku for real-time, advanced models like GPT-4/Gemini Pro/Claude Opus for deep analysis)

Business Impact: 35% increase in conversion rates, 50% higher average order value, 60% improved customer lifetime value

Content marketing at scale

Problem: Consistent content creation across multiple channels, languages, and brand voices
Solution: Automated content generation maintaining brand consistency across all touchpoints

Technical Implementation: - driftkit-context-engineering: Brand voice templates with automated testing against brand guidelines - driftkit-workflow-engine-agents: Multi-stage content pipelines using SequentialAgent pattern - driftkit-vector: Content similarity checking to avoid duplication across channels - driftkit-embedding: SEO keyword optimization and content clustering - driftkit-clients: Model selection by content type (creative writing with GPT-4/Claude vs technical documentation with Gemini)

Business Impact: 10x content output, 80% cost reduction, consistent brand messaging across 50+ channels

HR and recruitment automation

Problem: Resume screening bottlenecks, unconscious bias, poor candidate experience
Solution: Intelligent candidate matching with bias reduction and automated communications

Technical Implementation: - driftkit-common: Resume parsing and structured data extraction (skills, experience, education) - driftkit-embedding: Candidate-job matching based on semantic understanding, not just keywords - driftkit-vector: Talent pool management and similar candidate discovery - driftkit-workflow-engine-core: Interview scheduling, personalized communications, feedback collection - driftkit-context-engineering: Personalized outreach templates optimized for response rates

Business Impact: 70% faster hiring process, 40% improvement in hire quality, 90% candidate satisfaction

Intelligent banking assistant

Problem: Banking customers need 24/7 support for complex transactions, account management, and financial advice - but current chatbots are limited to simple FAQ responses
Solution: Multi-step conversational AI that handles everything from balance inquiries to loan applications with seamless human handoff

Technical Implementation: - driftkit-workflow-engine: Advanced conversational workflows with automatic message tracking and human-in-the-loop support - driftkit-clients: Dynamic model selection (GPT-4/Claude Opus for financial advice, GPT-4o-mini/Claude Haiku for simple queries) with structured outputs for transaction data - driftkit-workflow-engine-agents: Multi-agent orchestration for complex financial analysis - LoopAgent for iterative refinement of investment advice - driftkit-vector: Knowledge base for financial products, regulations, and personalized investment recommendations - driftkit-context-engineering: Compliance-tested prompt templates for different financial scenarios with A/B testing for conversion optimization - driftkit-common: Persistent session management with encrypted conversation history and document processing for uploaded statements - Database Integration: Direct connections to core banking systems, CRM, and fraud detection APIs - Legacy System Integration: REST/SOAP connectors to existing banking infrastructure with real-time transaction processing

Conversation Flow Examples: - Simple: "What's my balance?" → Direct database query → Formatted response (2 seconds) - Complex: "Help me apply for a mortgage" → Identity verification → Document collection → Credit check → Pre-approval calculation → Loan officer scheduling (15-minute guided process) - Emergency: "My card was stolen" → Fraud detection → Card blocking → Replacement ordering → Temporary credit setup → Follow-up scheduling

Business Impact: 85% reduction in call center volume, 60% faster loan processing, 24/7 availability, 40% increase in product cross-sell, 95% customer satisfaction for complex transactions

🧩 Framework modules