Performance reviews are universally hated. They're infrequent, heavily biased by recent events, and rarely reflect the nuanced reality of an employee's day-to-day impact. Mitable changes this by providing continuous, AI-driven evaluations that are rooted in objective data.
The Recency Bias Problem
When a manager sits down to write an annual review, they typically only remember the last month of work. If an employee had a stellar first half of the year but a quiet December, their review will disproportionately reflect December.
Furthermore, subjective evaluations often punish employees who do critical "glue work"—mentoring juniors, reviewing code, or unblocking other teams—because this work isn't easily visible in a Jira board.
How Mitable Evaluates
Because our system captures work context across all apps (Slack, VS Code, Figma, Notion), our evaluation engine has a complete picture.
The Evaluation Pipeline
- Data Aggregation: We pull the comprehensive activity summaries generated by our AI analysis engine.
- Benchmark Comparison: We run these summaries against the specific benchmarks defined for that employee's role.
- Semantic Scoring: Using a powerful hybrid semantic search system, we pull similar past work to ensure scoring consistency.
- Actionable Feedback: We don't just output a number. Mitable generates specific coaching nudges, like "You've been writing great code, but your PR reviews have dropped off this week."
Privacy and Trust
We believe evaluations should empower employees, not police them. Employees can see exactly which data points contributed to their score, and they can contest or add context to AI-generated summaries before their manager sees them.
By automating the objective measurement of work, we free up managers to do what they do best: actually managing and mentoring their team.