How it works

The loop from goals to a reward number you can explain.

The full cycle a Strivers review period runs — from setting goals to a reward number finance can defend, plus the live view leadership gets throughout.

Goals

Org admins set organizational goals. Managers and executives cascade team goals underneath them; individual contributors add their own. AI suggests goals grounded in the org's priorities and flags vague or misaligned ones before the cycle locks.

Learned weighting

Executives review a set of hypothetical employees — each with a different mix of impact across the org's goals — and decide how they'd split a raise budget between them. A regression infers each goal's actual weight from those calls, revealed fresh each cycle instead of typed in once.

AI-assisted reviews

Self, peer, and manager reviews share one flow: a short interview per goal, an AI-drafted narrative, and an inline chat to refine it before submitting. Peer and manager reviewers also get an AI-suggested score per goal, with a rationale, to accept or override.

Alignment scoring

Once reviews are in, every employee gets an alignment score: individual contributors from their own goal results against the learned weights; managers from a blend of their own results and their team's average, rolled up through the org chart. One number, reflecting how well their output matched what the org values.

Rewards

Reward shares are computed from alignment scores within each manager's group. An amplification dial you control decides how much more top performers pull from the pool — turn it up for winner-take-more, down for flat.

Flat
Winner-take-more

Insights

Leadership gets a standing dashboard of alignment scores by employee, team, and goal — the same live answer to what the org actually rewards that the reward math itself runs on.

The full AI surface

AI isn't a feature here. It's woven through the whole cycle.

Every AI surface reads from the same org-level writing spec, so generated content sounds like your org, not a generic assistant.

Goal suggestions

Grounded in the org's stated priorities, not generic templates.

Conversational drafting

Self, peer, and manager reviews — drafted from a structured interview, refined by chat.

Goal-quality critique

Flags vague or misaligned goals before the cycle locks.

Org writing spec

One standing voice profile every AI surface reads from.

Curious how this looks with your own goal structure?

Free to try — tell us about your goals and we'll show you where they map.