Jovis AI
AI account reconciliation: a controlled workflow
Design an AI-assisted reconciliation workflow with deterministic matching, evidence-backed exception review, clear controls, and accountable approval.
Implementation guides
How to investigate software delivery risk before a release
Build an evidence-based delivery risk review that connects planned work, code changes, deployments, incidents, and customer impact.
How to build a semantic layer for AI agents
Define metrics, entities, joins, permissions, and tests so AI agents can work with business data consistently and show their evidence.
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 support ticket analysis: a practical workflow
Turn support conversations into evidence-backed product decisions with a repeatable process for themes, trends, review, and ownership.
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.
Turn recurring business questions into useful AI agents
Find a repeated, consequential question and turn it into a bounded AI-agent workflow with trusted sources, tests, an owner, and a clear next action.
How to build an AI agent your team will actually use
A practical six-step guide to choosing one business job, defining approved context, testing the agent, and making it part of an existing workflow.