The Platform
Most large enterprises sit on decades of legacy applications that cannot be replaced. These systems hold the operational core of the business — order management, customer records, network provisioning, claims processing, policy administration, supply chain transactions — whatever sits at the operational core of your business. They were never designed for real-time intelligence, event-driven integration, or AI-powered decision making (what we call a governed, unified, model-driven real-time data platform). Yet the business demand for all three has never been greater. The data and AI landscape has fragmented into specialized silos. Event streaming platforms, Lakehouse architectures, and AI inference engines each solve part of the problem in isolation. No commercial platform unifies them. The result is integration complexity, governance gaps, and AI capabilities disconnected from the operational systems that need them most. GUM-RTDP — the Governed, Unified, Model-Driven Real-Time Data Platform — solves this. It connects the EDA Platform, the Lakehouse Platform, and the AI Speed Layer into a single governed whole — without modifying legacy applications, without forcing a choice between real-time and batch, and without sacrificing governance for speed.
The platform is built around a foundational principle: legacy applications are sovereign. They are never modified. The EDA Platform integrates around them — capturing every meaningful business occurrence through CDC and the Business Event Constructor, persisting a governed operational history in the Event Store, and making those events available in real time across the enterprise. AI inference enriches those events the moment they occur, publishing scored results back as governed AI events and persisted in its own AI Event Store. Legacy EDA extensions — purpose-built components alongside legacy applications — consume those AI events and act on them, triggering approvals, rejections, thresholds, and workflows without a single line of legacy code being touched. The legacy application gains real-time AI intelligence it was never designed to have.
Every business and AI event is ingested in real time into the Bronze zone of the Lakehouse — the permanent, immutable record of everything that happened. Conformed and enriched in Silver, aggregated into dimensional models in Gold. History is never overwritten — corrections are applied as additive changes through compensation, preserving the full audit trail. The result is a quality data foundation for machine learning, operations research, and business intelligence — built on the full richness of business and AI events in context, refreshed every ten minutes, always current. Dashboards built on this foundation reflect not just what happened, but what the AI detected, what decisions were made, and how the business responded.
Governance is not an afterthought. The Event Registry provides design-time contract governance for every business and AI event — payload specifications, lifecycle management, and consumer migration from a single authoritative source. The Data Governance Platform — anchored in the CEDM and DEDM enterprise data models — governs data quality, lineage, and policy across the Lakehouse.
GUM-RTDP is infrastructure and storage-agnostic by design — built on proven open-source technologies, deployable on-premises or on any cloud, integrating with your existing data, BI, and event streaming investments. No vendor lock-in. No forced migration.
Three platforms. One transformation.
The Problem Space
Legacy Applications Are Here To Stay
Every large enterprise runs on applications built over decades. Core banking systems, insurance policy engines, ERP platforms, order management systems — these are not candidates for replacement. They are too deeply embedded, too operationally critical, and too expensive to rebuild. Yet they were designed in an era before real-time data, before event-driven architecture, and before AI inference was a business expectation.
The challenge is not replacing these systems. The challenge is making them intelligent without touching them.
Three Disciplines, No Common Ground
The modern enterprise data and AI landscape has evolved into three distinct disciplines, each with its own tooling, its own specialists, and its own blind spots.
Event streaming — architects and engineers who understand Kafka, event-driven patterns, and real-time data flows. Deep expertise in messaging infrastructure, little connection to the Lakehouse or AI worlds.
Lakehouse and analytics — data engineers and architects who understand batch pipelines, Delta Lake, dimensional modeling, and analytical workloads. Deep expertise in data at rest, limited real-time capability.
AI and machine learning — data scientists and ML engineers who build and deploy models. Deep expertise in inference, limited integration with operational systems or governed event pipelines.
Each discipline solves its part of the problem. None of them solves the whole problem. And the enterprise pays the integration tax every time it tries to connect them.
The Real-Time Data Platform Gap Nobody Else Bridges
The specific gap that GUM-RTDP fills does not exist as a recognized category in the market. No platform currently offers:
- Real-time capture of business events from legacy applications — without modifying those applications
- A governed event fabric that makes those events available to downstream consumers with full lifecycle management
- A Lakehouse pipeline that ingests events in real time and builds the analytical foundation for AI
- An AI Speed Layer that scores business events in real time and publishes inference results back into the EDA Platform
- A unified governance model that spans all three layers from a single architectural framework
Organizations attempting to assemble this capability from individual tools face years of integration work, ongoing maintenance complexity, and governance gaps that grow with every new component added. GUM-RTDP delivers it as a unified, governed platform.
Three Platforms. One Transformation.
GUM-RTDP consists of three integrated platforms, each independently valuable, collectively transformative. EDA Platform, Lakehouse Platform with AI Speed Layer and Data Governance Platform.
