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Performance

Performance reviews that run themselves

Taito.ai starts each performance cycle, sends every reminder, and collects evidence from Slack, Linear, and your other tools. Managers write only the judgment.

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Taito.ai assembles a Q1 performance review for Daniel Hayes, pulling evidence from Gemini, Slack, Linear, and Google Sheets, then drafting a summary of the evidence: Daniel consistently delivered ahead of plan in Q1, leading the auth migration end-to-end and working closely with new hires across the platform team. Calibration suggests strong technical impact, with growth opportunity in cross-team… It then suggests a development discussion agenda that starts with Q1 highlights from the auth migration, moves to a plan for cross-team communication, and aligns on a Q2 stretch goal of leading a platform initiative across two teams.

A New performance cycle card defines three review questions for values, performance, and other feedback, with the review check toggle on and a Run now action. Below, a result card cycles through the per-employee actions the cycle triggers: a feedback request sent to Hannah Reid and three peers, a discussion scheduled between Olivia Hartley and Daniel Hayes, and requested feedback for Daniel Hayes from four sources.

How it works

Automated from cycle start to calibration

Managers write reviews from evidence, with the reminders and scheduling done for them.

Cycle setup and reminders

Set the participants, questions, and schedule for the performance cycle. Taito.ai sends the reminders, schedules calibration, and writes outcomes to the employee profile. Managers decide the ratings.

Draft reviews in Claude

Reviewers can draft in Claude or another assistant, with evidence from the tools they connect. Taito.ai checks the draft against the expectations for the employee's role before the reviewer submits it.

Policy compliance, built in

Each performance cycle stays in line with your organization's performance management policy, for example the one you keep for ISO 27001 or SOC 2 certification.

Expectations, coaching, insights

Every role knows what good looks like

Performance enablement means acting on results, not filing them. Expectations are generated for every role, coaching draws on real data, and insights show people leaders where to act.

Expectations for every role and level

Taito.ai generates your job catalog and the expectations for each role and seniority level, then analyzes automatically how well each employee meets them.

Coaching on real data

Employees reflecting on their own performance, and managers reviewing their direct reports, get guidance drawn from the work itself, not from memory.

Insights to act on

Performance insights show people leaders where the results of a cycle call for action: who is exceeding expectations, and who needs support.

A performance insights app window showing a quarterly table of teammates with columns for name, job title, probation status, review status, and overall performance. Each row tags an employee with status labels. For example, Daniel Hayes shows Passed probation, a Submitted review, and Exceeding performance, while Noah Patel shows Ongoing probation, an Overdue review, and Needs support performance. Tabs above the table switch between Quarter, Year, and All time views, with a filter input alongside.

Faculty
Rolling out Taito.ai across the organisation resulted in the smoothest performance review cycle we've run to date.
Andy Brookes
CTO, Faculty

Use cases

Where performance cycles usually break

P / 1

Late reviews and 80 follow-ups

Taito.ai sends the reminders and escalations, schedules calibration, and writes outcomes back to the employee profile on time.

P / 2

Calibration meetings with no data

Each calibration meeting comes with context from Slack, Linear, and Fireflies, plus flagged outliers, so the meeting is for judgment.

P / 3

Expectations nobody wrote down

Expectations are generated for every role and seniority level, and each review is analyzed against them, so a rating means the same thing in every team.

Frequently asked questions

Does Taito.ai write the reviews?
No. It drafts a summary of the evidence and flags gaps and patterns. The manager writes the judgment.
What can the agents see?
Only the data your permissions let them see, and only the integrations you enable. Every access is logged.
Where do review outcomes go?
To the employee profile. Ratings and review history are saved with the rest of the employee's data.
How do performance cycles stay compliant with our policies?
Taito.ai keeps each performance cycle in line with your organization's performance management policy automatically, for example the policy you keep for ISO 27001 or SOC 2 certification.

Related features

Review outcomes are saved to the employee profile

  • People directory

    People directory

    One employee profile per person. Pre-payroll, documents, and performance read from it.

  • Time off and attendance

    Time off and attendance

    Requests and approvals in Slack, local rules per country, and data kept in sync with your payroll provider.

  • Documents and eSign

    Documents and eSign

    Employment contracts and letters from templates, signed with eSign, and saved to the employee profile.