Verdonz
What is Verdonz?
Verdonz is a governed Business Intelligence (BI) and analytics platform for exploring connected business data, building dashboards and reports, and answering questions using natural language. Its semantic layer provides shared definitions for metrics, entities, relationships, dimensions, and access rules across analytical interfaces.
Key Capabilities
- Analytics and reporting: Verdonz supports data exploration, dashboards, scheduled and on-demand reports, natural-language questions, and contextual follow-up queries.
- Governed data model: The semantic layer defines joins, metric calculations, aggregation rules, dimensions, and data-access policies. Dashboards, reports, SQL queries, agents, and embedded analytics use the same definitions.
- Data investigation: Users can move from summary charts to supporting records, while analytics agents can plan and execute multistep investigations within the requesting user’s permissions.
- Security and administration: Verdonz provides row-level security, column masking, object-level permissions, single sign-on, user and group provisioning, and audit logging.
- AI governance and observability: The platform records questions, query plans, executed queries, model responses, prompts, latency, token consumption, and model costs. Gateway features provide model routing, rate limits, logging, prompt management, and policy guardrails.
- Integration and delivery: Verdonz queries supported data systems in place and provides embedded analytics, an HTTP API, and a Model Context Protocol (MCP) server for external applications and AI clients.
- Operational visibility: Infrastructure monitoring collects metrics, logs, and traces, while incident-management features maintain ownership, timelines, and change records for alerts.
- Knowledge discovery: Enterprise search and a knowledge graph connect dashboards, metrics, definitions, owners, and data dependencies.
Audience & Use Cases
- Audience: Analytics and data teams, finance teams, healthcare operators, platform administrators, and developers building data-connected applications.
- Use cases: Exploring business performance, investigating metric changes, producing governed reports, embedding analytics into applications, monitoring analytical and AI systems, and providing controlled access to business data through AI assistants.
