Jovis AI
How to measure AI agent ROI: a practical framework
Build a defensible AI-agent business case using a real baseline, quality-adjusted outcomes, full operating cost, and clear scale-or-stop gates.
Articles
AI anomaly detection for business metrics: a workflow
Design anomaly alerts that lead to evidence, accountable review, and action instead of adding noise to an operations queue.
AI agent observability: what to monitor in production
A practical monitoring model for tracing agent runs, data freshness, permission decisions, evidence quality, and business outcomes.
AI support ticket analysis: a practical workflow
Turn support conversations into evidence-backed product decisions with a repeatable process for themes, trends, review, and ownership.
What is agentic BI? A practical business intelligence guide
A practical guide to agentic BI, how it differs from dashboards and copilots, and what teams need before adopting it.
How to evaluate an AI agent for business data
Build a task-specific test set and production acceptance scorecard for correctness, grounding, permissions, usefulness, and failure recovery.
How to build an AI customer health monitoring workflow
Turn usage, support, relationship, and commercial signals into an evidence-based customer-success review with a clear owner and next action.
Connect AI agents to enterprise data: 4 patterns
Compare four ways to connect AI agents to business data across freshness, context, permissions, latency, and operating effort.
Build vs. buy AI agents: a business data decision framework
Decide which parts of an enterprise AI-agent system to build, buy, or combine using business, data, governance, and lifecycle criteria.
AI agent permissions: enterprise data access control
Design AI-agent access across user identity, tools, business systems, approvals, and audit records without relying on the model as a security boundary.