Glossary
Personalized benchmark
What is a personalized benchmark?
A personalized benchmark scores AI on your own work: your real tasks, judged by your team’s own definition of correct, and compared with how your people do the same work.
Why it matters
Public leaderboards rank models on test sets. A personalized benchmark tells you how a model, an agent, or an agent with a reviewer actually performs on your workflows, and at what cost.
Example
A finance team might find that people match 96% of invoices correctly, an agent alone 98%, and an agent with a reviewer 99%, each at a very different cost per invoice.
Learn more
Guide · DecideHow to choose an AI model by cost per taskMeasure cost per task, not price per token, and find where a cheaper model does the same job.Guide · ProveWhat is shadow mode? Testing AI agents on real work before they go liveRun an agent on real work without sending anything, score it against your people, and promote it one step at a time.Guide · ProveHow to benchmark AI on your own work: people, people with AI, and agentsCompare people, people with AI and agents on your own cases, and pick each workflow’s setup by cost per correct result.Guide · DecideBuild vs. buy AI agents: when to build, buy or foregoBuild, buy or forego: the criteria, the full cost of each path, and a worked example.Guide · ProveHow to measure AI impact and ROI: cycle time, quality and cost per taskTake a baseline, track five measures per workflow, and show the board what AI changed.