Jovis AI
AI customer onboarding: a workflow for finding risk early
Build an AI-assisted B2B onboarding review that finds stalled accounts, assembles evidence, and routes the next decision to an accountable owner.
Implementation guides
AI incident investigation: a controlled workflow
Use AI to assemble technical and customer evidence during a production incident while people retain command, mitigation, and communication decisions.
AI account reconciliation: a controlled workflow
Design an AI-assisted reconciliation workflow with deterministic matching, evidence-backed exception review, clear controls, and accountable approval.
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.