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1. Individual Team Member Research(1 week)

  • Shubham: While others shipped features, he made sure they wouldn’t set the server on fire.

    Technical Infrastrucure

    • Researched and designed Supabase (PostgreSQL + Auth + RLS) to be GDPR-compliant.
    • Data residency, right-to-erasure, audit logging while supporting system-level requirements such as role-based authorization.
    • Privilege isolation, and row-level security for multi-tenant workloads (targeting <50ms auth overhead per request)
    • Analyzed cloud hosting and blockchain node deployment strategies including containerized services (Docker).
    • Chain RPC reliability (99.9%+ uptime targets) to maintain both backend availability and continuous on-chain event indexing.
    • Benchmarked multiple chains for gas efficiency and throughput.
    • Comparing average transaction costs (e.g., Solana <$0.002, Polygon ~$0.01–$0.05, Ethereum L1 $1–$20+)
    • Research on TPS metrics to select infrastructure suitable for high-frequency automation workloads.

  • Utkarsh: He made sure the first impression matched the engineering depth.

    Creativity for Zynthax

    • His UI decisions were informed by industry findings that effective UX design can increase conversion rates by up to 200–400% and improve retention through intuitive layouts and responsive feedback loops.
    • By applying research-driven design principles and analytics, Utkarsh ensured the product’s interactions reduced user friction, which studies show is a key driver of long-term engagement and lower churn.
    • He iterated and validated UI patterns against real metrics such as task completion rates and responsiveness because usability research consistently links measurable design improvements to higher satisfaction and loyalty

  • Jami: Structured market intelligence while defining who Zynthax is built for and how it scales.

    Clarity in business research

    • Studied crypto transaction volume and activity patterns to quantify where automation delivers real economic impact.
    • Evaluated multiple Web3 ecosystems and funding models before proposing Solana-aligned startup pathways.
    • Translated market needs into concrete product logic: spend computation, transaction persistence, and workflow outputs.
    • Treated strategy like engineering: measurable inputs, structured logic, and predictable outcomes.

  • Aditya:Thinks in systems breaking chaotic problems into workflows, security models, and infrastructure that actually scales.

    Thinking in systems Research

    • Used documented benchmarks and research findings to influence every core decision, from workflow orchestration to encrypted secret handling, ensuring the design reflects real world needs.
    • Built sandboxed, deterministic nodes and least-privilege OAuth connectors because stolen credentials dominate real attacks (88% in basic web app attacks).
    • Designed every connector as a security boundary (scoped tokens + rotation), aligned with OWASP’s #1 risk: Broken Access Control (318k+ occurrences; tested in 94% of apps)
    • Engineered low-code pipelines with strict validation + isolation, treating integrations as the primary blast-radius—exactly where breach data shows attackers win via credentials

2.Group Research in initial Phase(1-3 days):

  • Evaluated how strongly the industry needs Zynthax by mapping real automation gaps across crypto trading, DAOs, and on-chain operations.
  • Compared multiple tech stack combinations to balance developer velocity, security, maintainability, and team-wide productivity.
  • Defined a shared vision for where Zynthax should stand in the crypto automation ecosystem and what differentiates it from existing tools.
  • Broke each question into dedicated discussion points and validated conclusions against real world scenarios and failure cases.
  • Analyzed funding strategies and chain efficiency to identify sustainable paths for growth and low cost infrastructure.
  • Modeled user acquisition channels and adoption drivers based on how crypto teams already discover and trust tooling.
  • Agreed on concrete realworld success metrics (usage, reliability, automation accuracy, security incidents) to objectively measure product impact.

Conclusion of group thinking:

  • Each of these questions was analyzed by team members within their core domains of expertise.
  • The outcomes reflected in their individual research and contributions.
  • This collaborative approach allowed us to distribute responsibility effectively, parallelize problem-solving, and apply specialized thinking where it mattered most.