STRYV Labs

Research Question

How can we create an automation layer that intelligently routes tasks across multiple systems without hard-coded dependencies?

Methodology

Designed and tested a declarative routing system that uses context-aware decision trees. Experimented with different pattern matching strategies, failure handling mechanisms, and dynamic endpoint discovery. Built iterative prototypes with increasing complexity to validate architecture assumptions.

Key Findings

• •Event-driven architectures require careful state management across async boundaries
• •Declarative routing reduces maintenance burden but requires robust schema validation
• •Circuit breaker patterns are essential for multi-system reliability
• •Dynamic discovery improves flexibility but adds latency to initial routing decisions

Outcomes

Successfully implemented a production-ready workflow engine that reduced integration time by 60% for new service connections. The system now handles over 10,000 daily task routings with 99.8% success rate. Research findings have been applied to multiple client projects requiring complex automation.

Research Question

Can we create a reusable framework for building context-aware, multi-step AI agents that maintain conversation state across complex workflows?

Methodology

Explored various agent architectures including ReAct, Plan-and-Solve, and custom hybrid approaches. Conducted experiments with different context management strategies, tool integration patterns, and memory systems. Built multiple prototypes testing token efficiency, response quality, and error recovery.

Key Findings

• •Step-by-step reasoning dramatically improves complex task completion rates
• •Tool selection accuracy increases by 40% when using embedding-based similarity matching
• •Context window management is critical for maintaining coherence in long conversations
• •Graceful degradation strategies prevent total failure when external tools are unavailable
• •Structured output formats (JSON schema) reduce hallucination in tool invocation

Outcomes

Developed a production framework that enables rapid construction of specialized AI assistants. The framework has been used to build domain-specific assistants for trading analysis, code review, and documentation generation. Research insights contributed to improved accuracy and reliability in all implementations.

Research Question

What combination of tools, patterns, and workflows enable turning conceptual ideas into functional prototypes within hours rather than weeks?

Methodology

Systematically analyzed time-to-prototype across 20+ project starts. Documented bottlenecks, identified reusable patterns, and tested various tool combinations. Measured velocity improvements from different acceleration techniques including component libraries, API-first design, and template systems.

Key Findings

• •Component-first development reduces initial build time by 50%
• •API-first design enables parallel frontend/backend work with minimal coordination overhead
• •Pre-configured authentication and database schemas eliminate 2-3 days of setup
• •Template repositories with common patterns reduce cognitive load on project initialization
• •Automated deployment pipelines enable immediate user testing of prototypes

Outcomes

Established a repeatable prototyping methodology that consistently delivers functional builds within 6-12 hours. This pipeline has been used to validate 15+ product concepts, allowing rapid iteration on core ideas before committing to full development. Several prototypes evolved into production products.

Research Question

How can we build a reliable confidence scoring system for AI-powered trading signal classification that balances precision with actionable frequency?

Methodology

Analyzed thousands of historical market signals to understand pattern characteristics. Experimented with different AI model configurations, feature engineering approaches, and confidence threshold strategies. Built A/B testing framework to measure signal accuracy across different confidence levels and market conditions.

Key Findings

• •Multi-factor analysis (technical indicators + news sentiment + volume patterns) improves accuracy by 35%
• •Confidence scores below 50% introduce significant noise with minimal actionable value
• •Dynamic threshold adjustment based on market volatility improves signal quality
• •News sentiment analysis requires careful prompt engineering to avoid false positives
• •Real-time data freshness is critical—signals degrade significantly after 15 minutes

Outcomes

Developed a tiered confidence system that categorizes signals into actionable tiers. High-confidence signals (>70%) demonstrate 68% accuracy in predicting momentum moves. The research methodology now informs all AI-driven classification systems. Findings published in internal research documentation.

Research Question

What synchronization strategies and conflict resolution patterns work best for mobile apps that need to function reliably without network connectivity?

Methodology

Researched existing offline-first patterns (CQRS, CRDTs, operational transforms). Built test implementations using different sync strategies. Conducted stress testing with simulated network failures, concurrent edits, and large data volumes. Measured user experience impact of different conflict resolution approaches.

Key Findings

• •Last-write-wins is insufficient for user-generated content—requires domain-specific conflict resolution
• •Optimistic UI updates significantly improve perceived performance during network interruptions
• •Background sync queues need careful ordering to maintain data consistency
• •Client-side schema validation prevents corrupted data from propagating to server
• •Incremental sync reduces bandwidth by 80% compared to full dataset transfers
• •Local-first architecture improves battery life by reducing constant network polling

Outcomes

Designed and implemented an offline-first architecture for a relationship management app that maintains full functionality without network connectivity. The system handles complex sync scenarios including relationship data, notes, and reminders. Research findings guide all mobile app architecture decisions.

Research Question

How can we reliably orchestrate workflows that span multiple services (APIs, databases, AI models, automation tools) while maintaining data consistency and handling failures gracefully?

Methodology

Designed integration patterns for workflows connecting 5+ different systems. Tested various orchestration strategies including centralized coordinators, distributed event sourcing, and peer-to-peer messaging. Evaluated failure modes, retry strategies, and transaction boundaries. Built monitoring and observability tools to track cross-system flows.

Key Findings

• •Idempotency keys are essential for reliable retry mechanisms in multi-system workflows
• •Saga patterns enable distributed transactions but require careful compensation logic design
• •Event sourcing provides excellent audit trails but increases storage requirements
• •Webhook reliability requires exponential backoff and dead-letter queue handling
• •Circuit breakers prevent cascade failures when downstream services degrade
• •Distributed tracing is non-negotiable for debugging complex multi-system flows

Outcomes

Established a set of proven integration patterns that have been applied to automate workflows across trading platforms, CRM systems, and AI pipelines. The research led to a reusable integration framework that reduces implementation time by 50% for new multi-system workflows. Currently powering production systems processing 100K+ daily operations.