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Performance

Review cycles that orchestrate themselves

Enable once, run forever. Automated cycles, AI-assisted analysis, and bias-flagging built on the data you already have: Slack, Linear, Salesforce, Fireflies, Gemini, anything MCP-enabled.

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Taito.ai assembles a Q1 performance review for Daniel Hayes, pulling source signal from Gemini, Slack, Linear, and Google Sheets, then drafting the reasoning: Daniel consistently shipped ahead of plan in Q1, leading the auth migration end-to-end and pairing closely with new hires across the platform team. Calibration suggests strong technical impact, with growth opportunity in cross-team… It follows up with a suggested development discussion agenda that opens with Q1 highlights from the auth migration, moves to a 30/60/90 plan for cross-team communication, and aligns on a Q2 stretch goal of leading a platform initiative across two teams.

A New review cycle card defines three review questions for culture, performance, and other feedback, with the AI review 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 kickoff to calibration

Reviews backed by real data, not just memory.

AI-assisted cycle setup and orchestration

Design the cycle — participants, prompts, cadence — with AI. Taito.ai handles reminders, calibration, and writes outcomes to the record.

AI-assisted reviews through Taito.ai MCP

Reviewers draft in any AI assistant, pulling from any data source they connect. Taito.ai accepts the submission over MCP, formats it to fit the rubric, and pre-reviews it before it ships.

Agentic feeback review

Taito.ai checks each review for missing details, bias, and outliers, and requests changes if needed.

Real-time insights

Continuous performance, not a quarterly snapshot

AI attributes flag what matters

Flight risk, coaching needs, probation status, culture fit. Surfaced from the data you already have, weeks before they'd come up in a 1:1.

Targeted coaching when it lands

See where teams and managers are stuck while it's still happening, not three months later in a review.

Best practices, lifted from your own org

When a manager's approach moves the numbers, Taito.ai shows it to the managers facing the same problem.

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

Where it pays off

From friction to fixed

P / 1

Reviews land late because someone's chasing 80 people for inputs.

Cycle orchestration runs the chase with reminders, escalations, and calibration scheduling, and writes outcomes back to the record on time.

P / 2

Calibration meetings without prep, and decisions made without data.

Each calibration ships with assembled context from Slack, Linear, and Fireflies, plus AI-flagged outliers, so the meeting is for judgement.

P / 3

Reviews skewed by recency bias, flight risk surfacing in the resignation letter.

Bias-flagging catches missing peers and recency-skewed examples before reviews ship, and AI attributes surface flight risk weeks earlier.

Frequently asked questions

Does the AI write the reviews?

No. It assembles signal, flags gaps, and surfaces patterns. The judgement stays with managers.

What can the agents see?

Only the data your permissions let them see, and only the integrations you turn on. Every access is logged.

Does this work without the People Operations module?

Best together. Performance writes outcomes back to the employee record, but it can run standalone.

How does bias-flagging work?

Heuristics + LLM checks for missing peers, recency-skewed examples, and inconsistent rubric application. You can override anything; everything is logged.

Built to work together

Keep exploring

  • People directory

    People directory

    One record per person, feeding payroll, performance, documents, and your AI agents.

  • Time-off and attendance

    Time-off and attendance

    Slack-native requests, regional policies, and pre-payroll reports your provider can ingest.

  • Docs & eSign

    Docs & eSign

    Contracts generated from templates, signed in Taito, filed against the employee record.