SkillBench measures whether your people are getting more capable, using signals from the AI-assisted work they already do.
A vendor cannot neutrally grade its own AI. SkillBench measures capability formation across every provider, read from real work.
SkillBench is the neutral layer that reads capability formation from real work, across every model and outside every provider.
Every enterprise bought a gas pedal. Nobody has the gauges. Existing tools measure activity: commits, velocity, tokens. None of them can tell you whether your AI spend is building capability or eroding it.
The model providers cannot grade their own work. SkillBench measures from outside, with no model of its own to defend.
Plenty of tools now report how much AI your team uses and what it costs. None report whether the people using it are getting better at their jobs.
| Usage dashboards ask | SkillBench asks |
|---|---|
How much AI-assisted output did we ship? |
Are people getting better, or just busier? |
What did the tokens cost, and what did they return? |
Could your org still do the work without the AI? |
Are we moving faster than last quarter? |
Which AI habits build skill, and which erode it? |
Every role breaks into 4–8 expert-validated tasks, and every task into 70–100 certified subtasks, linked into a dependency graph. Capability is then measured against that map, from the real back-and-forth of AI-supported work rather than self-report.
Allocation tools tell you where the effort went. The decomposition tells you what should be delegated to AI next, and what the skilling consequences will be.
Assessment projects what AI will do to a role from job descriptions alone. Gauge measures what it did, once workers opt in. Tint means modeled and solid means measured, here and in the product.
Start from what you already have: job descriptions and org structure. SkillBench decomposes every role into its working parts and shows executives where AI can reshape work, skills, and structure, before touching a single internal system.
A personal dashboard each worker owns. Consent-gated signals from real AI-supported work show individuals how their skills are growing through coaching. Their data stays theirs.
Workers use AI freely only when they aren't being watched. So the boundary between individual and organization is built into the architecture.
Organizational surfaces see aggregates only, never any individual's data.
Collection requires activation. Each worker activates their own telemetry and sees exactly what is captured and who can see what.
Signals come from work artifacts, sanitized in the collector before anything leaves the machine.
| Data | Worker | Organization |
|---|---|---|
| Raw work artifacts | stays on machine | Never sees |
| Own skill signals | Can see | Never sees |
| Coaching & growth paths | Can see | Never sees |
| Cohort aggregates, k‑anonymous | Can see | Can see |
In one 11-week pilot, blind acceptance of AI output was the strongest predictor of poor outcomes,2 and it is a habit teams can be coached out of.
The kind of signal usage dashboards can't see.
Start with an assessment of your organization: no telemetry, no installation, no risk. A walkthrough takes 30 minutes.