Dawiso AI Context Layer

Product Introduction

1. Definition: The Dawiso AI Context Layer is a specialized metadata management and enrichment platform designed as a semantic backbone for enterprise artificial intelligence (AI). It transforms traditional data catalogs into a dynamic, AI-ready context layer by automatically defining meaning, ownership, access controls, and relationships within an organization's data landscape.
2. Core Value Proposition: Dawiso exists to solve the critical failure point of enterprise AI: lack of context. It ensures AI agents and large language models (LLMs) deliver accurate, trustworthy, and relevant answers by providing them with a governed understanding of business semantics, data lineage, and operational processes, connected via the Dawiso Model Context Protocol (MCP).

Main Features

1. AI-Generated Context & Intelligent Relationship Mapping:
   • How it works: Dawiso employs automated metadata scanning across structured (databases, warehouses) and unstructured data (documents, PDFs). AI algorithms then enrich this metadata, generating business-ready descriptions, definitions, and crucially, mapping relationships between data assets and business terms. This includes automatic data lineage discovery and linking business glossary terms.
   • Technologies: AI/ML for metadata enrichment and relationship inference, automated metadata scanners, graph-based relationship mapping.
2. Unified Structured & Unstructured Data Governance:
   • How it works: Provides a single platform to apply consistent governance policies, access controls, and business context definitions across all data types. This ensures AI agents have a complete, governed view, whether querying a database table or extracting insights from a contract PDF.
   • Technologies: Centralized metadata repository, policy enforcement engine, connectors for diverse data sources (over 40+ platforms).
3. Dawiso Model Context Protocol (MCP) Integration:
   • How it works: Dawiso provides its own MCP Server implementation. This acts as a secure gateway, allowing AI agents and LLMs to dynamically query the governed context layer (business glossary, lineage, access rules, data product definitions) in real-time during interactions. This grounds AI responses in trusted enterprise knowledge.
   • Technologies: Dawiso MCP Server (conforming to the open MCP standard), API-based integration.
4. Human-in-the-Loop Governance & Business Process Modeling:
   • How it works: While automation drives initial context creation, Dawiso integrates governance workflows where humans (data stewards, domain experts) review, refine, and approve AI-generated context. It also allows enriching the context layer with business process models, showing how data flows through real-world operations, further grounding AI understanding.
   • Technologies: Workflow engine, collaborative annotation tools, business process modeling interface.

Problems Solved

1. Pain Point: AI Hallucinations and Inaccuracy in Enterprises. AI fails because it lacks the business context to interpret data correctly, leading to incorrect, irrelevant, or non-compliant outputs, especially in critical areas like financial reporting.
2. Target Audience:
   • Chief Data Officers (CDOs) & Data Governance Teams: Responsible for data quality, compliance, and ensuring trustworthy AI.
   • AI/ML Engineers & Developers: Building and deploying enterprise AI agents and LLM applications needing reliable context.
   • Data Stewards & Domain Experts: Subject matter experts who define and validate business terminology and rules.
   • Business Analysts & Operations Managers: Rely on accurate data insights and need AI tools that understand business processes.
   • Compliance & Risk Officers: Ensuring AI usage meets regulatory standards (e.g., GDPR, financial regulations).
3. Use Cases:
   • Context-Aware Financial Reporting AI: Ensuring AI-generated financial summaries or reports use the correct definitions, calculations, and authorized data sources, guaranteeing accuracy and auditability.
   • Accurate Customer Service Chatbots: Enabling customer service bots to understand complex product or account details by accessing governed business glossaries and customer data relationships via MCP.
   • Trusted Data Analysis for Decision-Making: Providing business users with AI tools that automatically understand data lineage and business definitions, leading to reliable insights.
   • Accelerated & Governed AI Agent Deployment: Drastically reducing the time and manual effort needed to prepare governed context for new AI applications.

Unique Advantages

1. Differentiation: Unlike traditional semantic layers (focused primarily on translating technical data to business terms) or basic data catalogs (focused on discovery), Dawiso's Context Layer integrates automated context generation, comprehensive governance (including unstructured data), business process context, and direct AI integration via MCP into a single platform. It offers significantly faster time-to-value and lower cost (claimed >50% less) compared to manual or piecemeal approaches.
2. Key Innovation: The core innovation is the automated generation of an AI-ready, governed context layer through the combination of AI-driven metadata enrichment and relationship mapping, coupled with seamless integration for AI agents via the Dawiso MCP Server. This eliminates the traditional bottleneck of manual context creation and provides a dynamic, queryable knowledge base for AI.

Frequently Asked Questions (FAQ)

1. What is the difference between an AI context layer and a semantic layer?
   • A semantic layer translates technical data structures into consistent business terms and metrics. An AI context layer, like Dawiso's, builds upon this by adding critical elements AI agents need: automated data lineage showing data origins and transformations, strict data governance rules (access, quality), business process context, and direct integration protocols (like MCP) for real-time AI querying, ensuring trustworthy and compliant AI outputs.
2. How does Dawiso MCP enable context-aware AI?
   • Dawiso MCP (Model Context Protocol) provides a standardized interface. The Dawiso MCP Server allows AI agents to securely query the governed context layer in real-time. Agents can ask "What does 'Q3 Revenue' mean here?", "What's the lineage of this customer data?", or "Is this user authorized to see this data?" directly within their workflow, grounding their responses in accurate, up-to-date enterprise knowledge.
3. Why is a data catalog alone insufficient for enterprise AI?
   • Traditional data catalogs focus on data discovery and basic technical metadata. For reliable AI, agents need deep business context: precise definitions (business glossary), understanding of how data is created and transformed (lineage), knowledge of who owns it and who can access it (governance), and how it relates to business operations. Dawiso's context layer enriches the catalog to provide this AI-specific metadata foundation.
4. How does Dawiso ensure the AI-generated context is accurate and trustworthy?
   • Dawiso employs a human-in-the-loop governance model. While AI automates initial scanning and context suggestion (descriptions, relationships), data stewards and domain experts review, refine, and approve this context through integrated workflows. This combines automation speed with human oversight, ensuring the context layer remains relevant, accurate, and compliant.
5. How quickly can Dawiso's AI Context Layer be implemented?
   • Dawiso emphasizes rapid deployment. Automated metadata scanning and AI enrichment allow customers to generate a usable data catalog and initial business glossary within a day, significantly faster than manual cataloging and glossary creation projects that can take months. Integration with existing data sources is facilitated by over 40+ pre-built connectors.