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System Architecture

Briefcase AI integrates seamlessly with your existing AI ecosystem, providing governance capabilities without disrupting your current workflows.

Architecture Overview​

Briefcase AI operates as a governance layer positioned strategically between your AI applications and their decision storage:

Briefcase AI positioned as governance layer between AI agents and system of record, surfacing without mutation to enable reconstruction

Layer positioning: Briefcase AI sits one layer below the agent and one layer above the system of record, enabling complete decision reconstruction.

Component Architecture​

Briefcase AI follows a three-layer design that integrates seamlessly with your existing AI ecosystem:

Three-layer Briefcase AI architecture showing external AI ecosystem, core Briefcase AI components, and storage infrastructure

Component architecture: Three-layer design with external AI ecosystem integration, core governance components, and flexible storage infrastructure.

External AI Ecosystem Integration​

Briefcase AI works with your existing stack:

  • LLM Providers: OpenAI, Anthropic, Google Vertex AI, AWS Bedrock
  • AI Frameworks: LangChain, LlamaIndex, AutoGen, CrewAI
  • ML Platforms: MLflow, Weights & Biases, Neptune, Kubeflow
  • Data Platforms: Snowflake, Databricks, Apache Airflow
  • Observability: DataDog, New Relic, Prometheus, OpenTelemetry

Core Briefcase AI Components​

AI Knowledge Repository​

The version control system for your AI knowledge

  • Prompts & Templates: Versioned prompt engineering with SHA-based tracking
  • Policy Documents: Immutable business rules and compliance guidelines
  • RAG Knowledge: Versioned embeddings and vector store references
  • Model Configurations: Tracked hyperparameters and deployment settings
  • Zero-Copy Branching: Instant environment duplication for testing

Decision Observatory​

Complete observability for every AI decision

  • Decision Traces: Immutable snapshots of inputs, outputs, and confidence scores
  • Knowledge Linkage: Exact version references for full provenance
  • Replay Engine: Deterministic reconstruction of historical decisions
  • Correlation Tracking: Multi-step workflow correlation across services
  • Performance Analytics: Latency, throughput, and drift monitoring

Governance Engine​

Policy enforcement and compliance validation

  • Pre-Commit Validation: Policy evaluation before decisions become permanent
  • PII Detection: Automatic identification and redaction of sensitive data
  • Schema Validation: Ensures consistency across decision formats
  • Regulatory Compliance: Automated checks against industry frameworks
  • Audit Trail Generation: Complete chain of custody documentation

Platform Services​

Enterprise-ready infrastructure and operations

Control Plane

  • Multi-tenant isolation with complete data separation
  • Role-based access control and API gateway
  • Usage metering and billing integration
  • Configuration management per tenant

Storage Backends

  • Object Storage: AWS S3, Azure Blob, Google Cloud Storage compatibility
  • Metadata Store: PostgreSQL, SQLite for decision metadata
  • Vector Databases: Integration with Pinecone, Weaviate, Chroma
  • Cache Layer: Redis for high-performance lookups

Operational Services

  • Horizontal scaling with Kubernetes
  • Automated failover and disaster recovery
  • Continuous backup and point-in-time recovery
  • Multi-region deployment capabilities

Multi-Tenant Isolation​

Every tenant operates in a completely isolated environment with no shared data layers:

Three tenant environments showing complete isolation of telemetry, versioned repositories, governance policies, and audit artifacts

Tenant isolation: Complete separation of telemetry, repositories, governance policies, and audit artifacts with no cross-tenant data sharing.

Data Isolation​

Every tenant operates in a completely isolated environment:

  • Separate Databases: No shared tables or schemas between tenants
  • Independent Policies: Governance rules specific to organizational requirements
  • Isolated Networking: VPC/subnet separation in cloud deployments
  • Encrypted Storage: Tenant-specific encryption keys for data at rest

Deployment Flexibility​

  • Cloud-Native: AWS, Azure, GCP with managed services integration
  • On-Premises: Complete platform deployment within customer infrastructure
  • Hybrid: Control plane in cloud, data plane on-premises for data residency
  • Air-Gapped: Fully disconnected deployment for maximum security environments

Decision Capture Flow​

When an AI agent makes a decision, Briefcase AI captures the complete context through a systematic flow:

End-to-end decision capture flow showing agent invocation, SDK instrumentation, ingestion, governance evaluation, and storage persistence

Decision capture: Complete workflow from AI agent decision through governance validation to persistent storage with versioned context.

Integration Points​

External Integrations​

AI/ML Provider Connections

  • OpenAI, Anthropic, Google Vertex AI
  • AWS Bedrock, Azure AI Services
  • Custom model endpoints
  • Framework adapters (LangChain, LlamaIndex)

Observability Platform Exports

  • OpenTelemetry traces and metrics
  • DataDog, New Relic, Splunk integration
  • Prometheus metrics collection
  • Custom webhook notifications

Compliance System Interfaces

  • ServiceNow GRC integration
  • JIRA case management
  • Custom policy engines
  • Audit trail export formats