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Platform Overview

1.1 Purpose​

Bike4Mind is a unified platform for AI-augmented research, notebook-based workflows, and agentic application development. It is designed to support both SaaS and self-hosted deployments, with full access to customizable tooling, extensible integrations, and observability features suitable for enterprise-scale operations.

1.2 Architecture Overview​

  • Frontend: React
  • Backend: Node.js
  • Database: MongoDB
  • Cloud Infrastructure: AWS-native stack (EC2, Lambda, S3, CloudWatch, CloudTrail, etc.)

The platform follows a service-oriented, stateless architecture and is optimized for distributed team development and cloud-native deployment models.

1.3 Deployment Options​

Bike4Mind supports multiple deployment configurations:

  • SaaS (Bike4Mind-Managed): Hosted, multi-tenant architecture with shared infrastructure.
  • Self-Hosted (Customer AWS): Single-tenant deployments in a customer-managed AWS account.
  • White-Labeled: Fully re-skinned deployment with custom domain and organizational branding.

Full source-code licensing is available for customers requiring complete control and extensibility.

1.4 Functional Scope​

  • Notebook Interface:

    • Chat-style threaded interface with semantic tagging and context memory
    • Forking (forward/backward), summarization, and embedded RAG capabilities
    • Version history and export/import support across LLM vendors

  • LLM Model Support:

    • OpenAI, Anthropic, Gemini, XAI, and Amazon Bedrock-compatible providers
    • SOTA voice/image generation via ElevenLabs and Black Forest Labs

  • Project System:

    • Bundles Notebooks, System Prompts, Tools, and Files
    • Supports multi-level sharing (user, group, org, global)

  • System Prompts:

    • Layered prompt architecture with weighted conflict resolution
    • Configurable at project, user, or org level

  • File Ingestion & RAG:

    • Supports PDF, DOCX, TXT, CSV, HTML, JSON, and scraped web content
    • Files are chunked, vectorized, and indexed with metadata and summaries
    • Fully accessible within Notebook queries via RAG

1.5 Integrated Tools​

Bike4Mind includes native integrations for commonly used agent tools and APIs:

  • WebSearch
  • Accuweather
  • Mermaid Charts
  • Recharts
  • Puppeteer (headless browser)
  • Math parsing/evaluation
  • LinkedIn API
  • Others via extensible plugin interface

1.6 Security and Access Control​

  • Authentication:
    • Supports OAuth2 (including Okta, Google, GitHub)
    • JWT token-based authentication
  • Authorization:
    • Role-based access control (RBAC)
    • CASL-based permission modeling
  • Governance:
    • Admin-level feature toggling and audit logging
    • Explicit data sovereignty support (via customer-managed deployments)

1.7 Observability and Telemetry​

  • Embedded analytics capturing:
    • User sessions, interaction timing, feature usage, content ingestion rates
    • Browser/OS breakdown, screen resolution, and active time
  • System telemetry includes:
    • Latency, error rate, throughput, model invocation stats
    • Slack/email daily and weekly summaries with AI-generated executive summaries
  • Dashboards are included out-of-the-box; no third-party BI integration required

1.8 CI/CD and DevOps​

  • CI/CD powered by SEED and AWS-native services
  • Developers provision full local environments mirroring cloud stack
  • Rollback-on-error enabled via deployment automation
  • Pre-mortem and post-mortem playbooks included for issue management
  • GitHub Projects used for backlog grooming and release orchestration

1.9 Customization and Extensions​

  • White-label support: theming, logos, color schemes
  • API surface for external integration
  • Full-stack custom development offered under work-for-hire agreements
  • All generated IP in custom projects is owned by the customer

1.10 Summary​

Bike4Mind is designed to support enterprise needs for AI-native tooling, research, and agentic development in a secure, governed, and extensible manner. Its architecture, observability, and deployment flexibility make it suitable for regulated industries and advanced internal AI initiatives.