Platform Architecture

A modular stack combining orchestration, knowledge, and security layers so you can compose production AI faster.

1. Knowledge Graph (The Semantic Foundation)

The knowledge graph acts as the platform's "brain," organizing siloed data into a web of interconnected entities and relationships.

Contextual Grounding

Grounds AI models in business reality by defining how entities like customers, products, and processes relate, significantly reducing AI "hallucinations".

Data Integration

As a virtual semantic layer, allows organizations to access distributed data across different systems without physical replication.

Discovery and Analytics

Platforms like SAP HANA Cloud and Neo4j use these graphs to uncover hidden patterns and accelerate decision-making through complex relationship traversal.

2. Workflow Engine (The Execution Layer)

This layer translates the insights from the knowledge graph into coordinated actions.

Orchestration

Manages the sequence of tasks, such as creating a purchase request before a purchase order, ensuring business logic is followed.

Agentic AI

Modern engines support "agentic workflows," where AI agents use the knowledge graph to discover and execute the most relevant APIs in the correct order.

Efficiency

By automating routine logic (e.g., "if status = X, then send email Y"), frees human operators to focus on higher-level reasoning.

3. Security Layer (The Governance Framework)

In 2025, the security layer is increasingly integrated directly with the knowledge graph to provide dynamic, context-aware protection.

Least Privilege & Zero Trust

Enforces strict identity and access management (IAM) across all platform assets, ensuring users and AI agents only access permitted data.

Compliance & Monitoring

Security operations (SecOps) use knowledge graphs to map cascading failures and detect redundant alerts, improving incident response speed.

Data Privacy

Specialized platforms like Obsidian Security use knowledge graphs to map every identity and activity across a SaaS estate to manage risk and sprawl.

4. Integration in Platform Architecture

A robust 2025 platform architecture typically follows a multi-layered approach:

Storage Layer

Consolidates data in lakes or specialized graph databases like Spanner or Neo4j.

Computational Layer

Handles processing and transformation of data using Apache Spark or Databricks.

Semantic/Reasoning Layer

Where the Knowledge Graph sits, bridging raw data and user intent.

User Interface/API Layer

Allows interaction through natural language queries or traditional dashboards.

Data Onboarding

Connect nearly any source with adapters and automated profiling.

1. Structured Data (SQL, Warehouses, OLTP)

This data is typically onboarded via ELT (Extract, Load, Transform) to leverage the massive parallel processing of cloud warehouses.

Direct Replication (Zero-ETL)

For low-latency needs, 2025 architectures use Zero-ETL to replicate operational data directly into analytical stores without managing manual pipelines.

Change Data Capture (CDC)

Systems use CDC to stream incremental updates from OLTP databases, ensuring the data warehouse or lakehouse remains synchronized in near real-time.

Cloud Integration

Seamless connection with Amazon Aurora, Google BigQuery, Snowflake, and other enterprise data platforms.

2. Unstructured Data (Docs, PDFs, Chat Logs, Images)

Unstructured data is the primary driver for Generative AI in 2025, requiring specialized "AI-ready" transformation.

Intelligent Parsing

Platforms like Unstructured.io or IBM watsonx.data automatically extract text, tables, and metadata from complex formats like PDFs and emails.

Vectorization & RAG

Extracted text is chunked and stored in vector databases (or vector-enabled warehouses like Snowflake) to support Retrieval-Augmented Generation (RAG).

Multi-Modal Processing

Advanced OCR and vision models process images, charts, and diagrams alongside text content.

3. Streaming Data (Events, Telemetry, IoT Feeds)

High-velocity data follows a streaming-first architecture to enable immediate reactions to real-time events.

Message Brokering

Tools like Apache Kafka and Google Cloud Pub/Sub act as the backbone, ingesting millions of events per second from IoT sensors and application telemetry.

In-Flight Transformation

Modern pipelines use stream processing (e.g., Apache Flink) to enrich or filter data before it reaches permanent storage.

Real-Time Analytics

Immediate insights from event streams enable proactive decision-making and automated responses.

4. 3rd Party Data (External APIs & Partner Platforms)

Onboarding external data focuses on connectivity and governance to bridge organizational silos.

API Connectors

Platforms use pre-built SaaS connectors (e.g., Fivetran) to automate ingestion from external platforms like Salesforce, Shopify, or HubSpot.

Federated Access

Instead of physical movement, some 2025 architectures use Data Virtualization to query 3rd party datasets in place, reducing data redundancy and costs.

Partner Ecosystem

Secure data sharing with partners through governed APIs and data clean rooms.