Six layers.One controlled architecture.
Most platforms give you access to data. D4 gives you a structured way to move from raw signals to reliable, purpose built outputs, with security, validation, and control built into the platform.
6
ARCHITECTURE LAYERS
Agnostic
INPUT SOURCES
Versioned
OUTPUT CONTRACTS
Governed
AI & DATA
Architecture overview
The full stack, from signal to API
Each layer has a clear responsibility. Data is transformed, validated, and enriched as it moves through the platform, with control maintained at every boundary.
Convert & ingest
Protocol native adapters connect each producer type. Format differences are resolved here, before data enters the platform. Authentication and basic validation happen at the boundary.
Collect & verify
Incoming data is authenticated, validated, and checked against the expected structure and metadata. Events that fail verification are rejected before they reach downstream processing.
Store & audit trail
Verified data is persisted with the metadata needed for traceability and auditability. Raw, event, and historical data can be stored separately according to retention and analytical needs.
Stream & filter
Validated data is routed into purpose driven streams. Noise, duplicates, and irrelevant signals are filtered out before reaching orchestration.
Orchestrate & enrich
Workflows combine filtered streams with external context such as location, weather, and business data. Analysis and AI can be added as bounded processing steps within the workflow.
Deliver
Results are exposed through scoped, versioned endpoints. Each endpoint is designed for a specific use case, with access limited to the data that consumer needs.
WHAT THE ARCHITECTURE ENFORCES
Not promises.
Structural properties.
These aren't feature claims. They're consequences of how the stack is designed. Each follows from where a component sits in the architecture, not from a policy document.
Validation is built into the flow
Authentication, format validation, and filtering happen at defined boundaries before data reaches downstream processing.
Explicit paths. Clear boundaries.
Dependencies and data flows are defined explicitly, making workflows easier to validate, observe, and evolve.
AI stays bounded
The LMM operates as an isolated processing step. It receives a bounded, structured summary rather than raw streams, and produces validated interpretation and insight rather than controlling safety critical behaviour.
See what D4 could do with your data
Bring a real data challenge to the table. We'll map it to the D4 architecture, explore the right data sources and workflows, and show how the platform could turn it into a working use case.
