C64AI Evolves with DCH 3.0: A Unified Intelligence Layer
DCH 3.0 is an architectural transformation: a redesigned v2 UI, unified Projections, versioned Workflows, dedicated Intake Agents, and a platform-wide draft → test → publish → archive lifecycle.
As organizations integrate AI deeper into engineering workflows, a critical principle emerges: intelligence requires structure. Models cannot deliver dependable results without foundational systems managing context, data, orchestration, and reasoning. DCH 3.0 represents more than incremental improvements — it is an architectural transformation that unifies how DCH and M4AI function as one integrated platform.
A Unified, Next-Generation DCH UI
Previous platform versions split functionality between two interfaces: the GBS UI for system management and the Explorer UI for graph navigation, both relying on legacy v1 endpoints. DCH 3.0 introduces a completely redesigned UI built on modern v2 APIs, reimagining interaction across the platform's operational lifecycle rather than combining older interfaces.
The new DCH UI serves as the central operational portal, encompassing:
- Graph exploration and modelling
- Projections (views and functions)
- Workflows and orchestration
- Ingestion via intake agents
- Service and worker monitoring
- Reasoning traces and M4AI execution visibility
This interface provides a unified visual language, consistent interaction patterns, and real-time visibility into how data, memory, and reasoning circulate throughout the system.
DCH + M4AI: Two Engines, One Intelligence System
DCH 3.0 clarifies the architectural separation while strengthening collaboration between components. DCH manages orchestration, graph operations, lifecycle management, data transformation, observability, and ingestion. M4AI handles agent execution, contextual reasoning, action handling, and streaming output. This structure creates a synchronized intelligence engine where all pipeline components remain controlled, observable, and governed by identical lifecycle principles.
Projections: A Unified, Versioned Data-Shaping Layer
DCH 3.0 introduces Projections, consolidating Views and Functions under one managed model offering consistent data-shaping for graph access, complete versioning (draft → test → publish → archive), built-in testing and validation, seamless Linked Data API integration, and stable context surfaces for M4AI agents.
Workflows: Modern Orchestration for the Entire Pipeline
Workflows replace Load Plans with a contemporary execution layer managing ingestion routines, graph builds, memory reload operations, rule transformation steps, and scheduled or triggered workflows. All operations follow the same versioned lifecycle as projections and agents, bringing orchestration, data preparation, and memory management into unified, transparent control.
Clear Architectural Boundaries Across the Platform
DCH 3.0 establishes explicit boundaries across major subsystems: the Linked Data API as the graph and data operations foundation, M4AI as the agent reasoning and memory backend, and DCH as the orchestration layer connecting everything. This clarity enables independent subsystem evolution while maintaining consistent interactions, simplifying onboarding and enterprise integration.
Intake Agents: A Dedicated Ingestion Tier
DCH 3.0 formalizes ingestion as a dedicated subsystem, replacing implicit Worker service logic with Intake Agents that provide clear definitions for external data entry, structured graph and memory flows, dedicated configuration and monitoring UI, and enhanced reliability and observability.
A Platform-Wide Versioning Model
A central theme is lifecycle consistency. All core intelligence artifacts now follow identical progression — draft → test → publish → archive — applying to projections, workflows, and M4AI agents. This improves auditability, governance, and operational control, which is particularly valuable in enterprise contexts requiring traceability.
Transparent, End-to-End Intelligence Flows
The consolidated model renders the entire intelligence pipeline visible: ingestion → graph → projections → memory → agents → results. Every stage remains observable, versioned, and controlled, enabling teams to track data movement, context shaping, and reasoning execution.
From Separate Components to a Cohesive Intelligence Engine
DCH 3.0 represents a significant transition. Previously independent yet powerful components now function as one unified platform where DCH manages structure and visibility, M4AI executes reasoning, and all components share governance models — creating a system architected for scalability, governance, explainability, and deep integration.
Workflows: The New Execution Backbone of DCH 3.0
In DCH 3.0, Load Plans evolve into Workflows — a versioned orchestration layer with draft/published lifecycle, declarative tasks, and Apache Airflow execution. Governed automation replaces ad-hoc pipelines.
The Trillion-Dollar Memory Layer: Decoding the Gartner Hype Around Context Graphs
Context graphs are being positioned as the next foundational layer of enterprise AI. The opportunity is real — but only when we understand what they are actually designed to solve.
Knowledge Graph vs. Context Graph: The Architectural Difference
A knowledge graph tells AI what exists. A context graph tells it what matters now, why it matters, and how a decision should be made — the operational layer enterprise agents need.
Build AI on connected engineering context.
Explore the platform that turns fragmented engineering data into governed, AI-ready context — or talk to the team about your use case.