A KPI system you can trust
Launch a new business metric without writing code. One shared engine reads each metric from configuration. Screens and AI agents get the same answer.
Case studies from my day job: what each project does and the decisions behind it.
Launch a new business metric without writing code. One shared engine reads each metric from configuration. Screens and AI agents get the same answer.
One shared model, not each app, protects personal and financial columns. The privacy team picks the private columns. Each user group reads the matching copy.
Each person sees the rows their place in the sales structure allows. Benchmarks widen that view to the whole sales team, with no hand-kept list.
Mapping the cloud bill to the pipelines and teams behind it. Then cutting compute and storage with lighter engines, less indexing and retention rules.
Agents that find duplicate metrics, review code changes before people do, and draft missing metric descriptions.
An assistant that finds, explains, debugs and creates KPIs and charts from questions in plain words. A fixed set of test chats decides when it is ready.
A summer intern project. An AI pipeline drafts the metadata for each KPI. People review and approve every draft before anyone uses it.