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Curious Context Labs

HR and people · Custom app

Performance review summaries

AI performance review summaries for managers

Managers get a balanced first draft of each review, built from goals, feedback and one-to-one notes, with every point linked to its source.

01 today

The problem

When review season comes, managers try to remember six months of work and end up writing about the last few weeks. Feedback from peers sits in forms nobody reads closely, and the quality of reviews depends on which manager you have.

02 the run

What the AI does

  1. Step 1: Collects goals, peer feedback, self-reviews and one-to-one notes from Lattice, BambooHR, Google Docs or Notion for each employee.
  2. Step 2: Groups the evidence by goal and by the competencies in your review framework.
  3. Step 3: Drafts a balanced summary of strengths and areas to work on, linking each point to the note or feedback it came from.
  4. Step 4: Flags gaps, such as a goal with no evidence or feedback that contradicts the self-review, for the manager to look into.
  5. Step 5: Checks the wording for vague or loaded language and suggests plainer, more specific phrasing.

paused: waiting for a person to approve

03 human in the loop

Where a person approves

The manager edits and owns every review, and HR checks ratings across the team before anything is shared with the employee. Pay and promotion decisions are made by people, not by the summary.

04 after

What changes

Reviews cover the whole period, not just the last month, and every point can be traced to something real. Managers spend their time on the conversation instead of the first draft.

Results from a real build

[CASE STUDY] We only publish results we can name and check with the client.

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Tell us how your business runs. We'll tell you where AI fits.

A free 30-minute call. You talk, we ask questions, and you leave with a few concrete ideas — or an honest answer that AI isn't the right fix yet.

Working with teams in the United States, United Kingdom and Australia.