DataWalk Composite AI: Unified Enterprise AI for Intelligent Business Solutions

According to Gartner, Composite AI is the combined application of different AI techniques to improve learning efficiency, broaden knowledge representations, and solve business problems more effectively. This approach integrates multiple AI methods into a single platform, overcoming the limitations of individual techniques. This article reviews the DataWalk Composite AI solution and its key benefits for enterprise architecture.

Figure 1: Composite AI in DataWalk

Enterprise-Ready Architecture

DataWalk is an enterprise-grade solution designed to operate in environments with strict operational, regulatory, and security requirements. The platform's architecture includes compliance with security policies, granular full access control (LDAP/SAML, tokenization), and separate development, test, sandbox and production environments. It also features comprehensive auditing, logging, monitoring, versioning of configurations, and is ready for integration into any enterprise IT ecosystem.

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Ontology and Knowledge Graph

A foundational element of Composite AI is the knowledge graph. DataWalk provides a knowledge graph built on a unique ontology that combines the flexibility and reasoning capabilities of RDF (Resource Description Framework) with the performance and scalability of LPG (Labeled Property Graph) for large datasets. This hybrid ontology architecture enables contextual data integration, automatic relationship discovery, flexible modeling of complex dependencies, and advanced inferencing. The knowledge graph is fully operationalized and accessible to users through the Universe Viewer—an interactive workspace for visually answering complex, multi-hop queries without the need for coding.

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Powerful Inference Component

Reasoning is a key capability for Composite AI. DataWalk features a fully functional inference engine that automatically creates new, derived relationships and attributes based on rules, graph traversal paths, and analytical logic. Unlike typical systems, inference in DataWalk is a native architectural component. The DataWalk Dependency Refresh Engine ensures continuous incremental refresh of inferred data whenever source inputs change, guaranteeing accuracy and reducing maintenance overhead. Inference can be configured through the DataWalk UI and API using methods like Virtual Paths, Calculated Columns, and Graph Algorithms.

Figure 2: This enables continuous refresh of inferred data whenever source inputs change—ensuring accuracy and reducing maintenance overhead.

Auditability, Lineage & Explainability

All results in DataWalk-from scores to relationships-are auditable with complete logging, explainable with full data lineage, and compliance-ready to support traceability for every decision. The built-in operations graph provides full transparency into the relationships between rules, data, models, and results, making them easy to analyze and validate. This ensures accountability and trust in the analytical outcomes.

Machine Learning and Scoring

DataWalk supports the full ML model lifecycle, allowing users to build internal models or integrate with external libraries such as scikit-learn, XGBoost, and PyTorch. It facilitates feature engineering using tabular, graph, NLP, or scoring data. To ensure scalability and performance with large datasets, DataWalk performs complex analysis and feature engineering directly in the database engine, avoiding memory limits and speeding up operations without moving the data. For additional flexibility, users can also run in-memory computations within Jupyter notebooks, taking advantage of the full Python ecosystem—ideal for smaller datasets that comfortably fit in memory.

Graph Analytics and Traversal

Graph analytics is another key element of Composite AI. DataWalk supports complex multi-hop, recursive, and pattern-based queries. It includes built-in algorithms like PageRank, and community detection, and allows for the materialization of inferred relationships. The Visual Queries interface enables scalable multi-hop graph exploration without requiring any coding, making advanced graph analysis accessible to all users.

NLP and Ontology Extraction

Natural Language Processing (NLP) is an important facility in DataWalk's Composite AI. Integrated NLP tools extract entities and relationships from unstructured text, converting raw documents, emails, and reports into ontological structures. This enriches the knowledge graph with new insights, and the NLP outputs can be fed directly into rules, scoring, and inference processes.

Search & Aggregation

DataWalk provides robust search and aggregation capabilities. The platform supports full-text, conditional, structural, and vector search across all objects, relationships, NLP content, scores, and paths. For aggregation, engine-native OLAP functionality allows for measures, distributions and pivotal operations across entities and relationships without requiring data movement and seperate data piplines.

Applications Development Framework

DataWalk’s Application Development Framework, powered by its unified knowledge graph, lets users safely run scripts, open-source tools, AI, and ML models directly inside the platform. With a built-in Jupyter environment and the controlled App Center pipeline, teams can rapidly prototype, deploy, and operationalize custom applications that integrate seamlessly with DataWalk’s data and analytics—extending functionality with machine learning, LLMs, image recognition, and more to meet unique business needs quickly and flexibly.

LLM Integration – Model Context Protocol (MCP)

To support the growing importance of Large Language Models (LLMs), DataWalk utilizes the Model Context Protocol (MCP). This protocol allows for secure integration with external LLMs and AI agents. It provides controlled access to DataWalk's querying, scoring, and graph capabilities, supporting Graph-RAG and knowledge-centric agent workflows. This architecture enables Agentic AI, where LLMs can act in concert with the DataWalk knowledge engine.

Key Benefits

The DataWalk Composite AI architecture delivers significant advantages. It provides a single computation engine within one database and one version of truth in a unified knowledge environment, with fully integrated inference, exploration, and diverse analytical techniques. This monolithic architecture ensures high performance and rapid responses to the most complex questions across massive data volumes, while also enabling easy and fast scaling—thanks to having a single, tightly integrated component instead of many fragmented parts. The platform ensures end-to-end, automated data consistency and lineage management, unifying support for AI, ML, search, graph, OLAP, NLP, and LLMs within an enterprise-ready, compliant framework. This results in transparent, explainable decision-making and allows organizations to move from raw data to deployed solutions with Extreme Agility and speed.

Summary

Composite AI in DataWalk is not a toolkit; it is a set of powerful capabilities built into an enterprise-grade, integrated architecture. It unifies data, logic, and models into a single environment to build intelligent, scalable, and explainable AI applications. Aligned with Gartner’s vision, DataWalk merges diverse AI methods to enable broader knowledge representation, greater learning efficiency, and operational decision intelligence.

Figure 3: A high-level architectural concept for DataWalk shows how composite AI (composite analytics) is powered by a unified computation layer running on a single hybrid database.