AI spend isn't the risk. Slop wearing a good ROI number is.

Most tools stop at "who spent what." Merit checks if the work was any good — reverts, rewrites, regeneration loops — before it counts as value.

31%

of organizations have accurate visibility into their AI software spend

Flexera, 2026 State of ITAM Report

59%

say wasted AI spend increased in the last year

Flexera, 2026 State of ITAM Report

Half

of companies running AI in production still can't prove it delivers business value

Plug and Play, 2026 Enterprise AI Survey

Estimate your AI ROI

Engineers using AI tools

Average fully-loaded comp

AI license cost, per engineer

$/mo

Time saved, per engineer

hrs/wk

Rework/review overhead, per engineer

hrs/wk

Fill in the fields above to see an estimate

Illustrative, not a personalized audit — "time saved" and "rework overhead" are numbers you supply here. Connect Merit to replace these guesses with measured numbers from your own GitHub, Jira, and AI usage history.

Quality risk scoring

Reverts. Heavy rewrites. Regeneration loops. A slop score that plain spend attribution can't produce.

Spend, by team and tool

Every dollar, every tool — Anthropic, OpenAI, Copilot, and the rest — rolled up by team, with person-level detail when you need it. Necessary. Not sufficient.

Value, weighed against risk

Spend correlated against outcomes: PRs merged, tickets closed, deals moved. Then weighed against the slop score, because attributed isn't the same as good.

Team & company rollups

Rolled up by team, role, and trend. One click, not a spreadsheet.