mairit People
Mairit · For people teams

Your AI is making people decisions. Make sure a human signed them off.

Mairit plugs into the AI your HR teams already use and routes performance reviews, hiring decisions, and pay-equity analyses to a qualified senior reviewer before they're acted on. Internal HRBPs first. Vetted specialists when nobody inside fits. Audit trail that meets EU AI Act, NYC AEDT, and GDPR Article 22.

Works inside the agents your teams already use
Claude· ChatGPT· Cursor· Lovable· OpenClaw· Any MCP agent
Claude · 2026-h1-review-amelia-r.md
/review
 
mairit  Detected: performance review (annual). 3 qualified reviewers available.
1 Aisha Khan · People Director (internal)
  38 calibration reviews · available now · ~22 min
2 Marc Bouvier · Head of HRBP (internal)
  22 similar reviews · backlog 2h · ~30 min
3 Senior performance specialist · Mairit network · $180
  220+ similar reviews · available now · ~25 min
mairit  Sending to Aisha. Bias-checked and calibrated, back in your agent.
The problem

AI is making people decisions faster than your governance can keep up.

You rolled out AI across HR this year. You also rolled out a problem nobody warned you about.

01

AI-touched HR work is up 10x. Senior review isn't.

Performance reviews drafted by ChatGPT. Candidate scorecards generated by your ATS's AI. Pay-equity dashboards summarized by an AI agent. Drafted in minutes. Signed off in seconds. Defended later in court.

02

Your senior HRBPs are the calibration. They know it.

The People Director re-reading manager reviews to catch bias. The Head of Talent who actually checks the AI screen for adverse impact. The Comp lead who notices when the model recommends a 6% gap between two equivalent roles. They're maxed out. 90% of what they should see, they don't.

03

The EU AI Act now requires what your policy already promised.

Employment is high-risk under EU AI Act Annex III. NYC AEDT mandates bias audits and candidate notice. Colorado, Illinois, and Texas have followed. Right now, your evidence of human oversight is a Slack thread and a manager's good faith. That's not going to hold up to a regulator. And it's not going to hold up to a wrongful-termination claim.

How it works

The review gate, inside your HR agent.

One command. The right reviewer. Reviewed output back in minutes. No new tabs. No chasing. No calibration meetings on Sunday.

1

AI finishes the work

A draft review. A candidate shortlist. A pay-equity readout for leadership. Something that would normally sit in a doc waiting for a senior signoff.

claude finished drafting
   2026-h1-review.md
2

One command invokes Mairit

Type /review. Mairit reads the work, identifies the domain, and surfaces the two or three people best placed to check it.

/review
   3 reviewers matched
3

A qualified human reviews

Your internal HRBP by default. A vetted specialist from our network when nobody inside fits. Structured rubric. Bias, calibration, framing. No essays.

aisha.khan
   reviewing · 9 of 12
4

Back in your agent, attested

Reviewed output returns inline. Cryptographically signed. Audit-logged. Article 14 ready. Holds up to regulators and employment tribunals.

review complete
   attested · ready to ship
2030 min
Typical end-to-end review time, request to attested output
3×
More AI-touched HR work reviewed without adding HRBP headcount
100%
Audit-logged, signed, and ready for EU AI Act, AEDT, and GDPR
Today vs. with Mairit

What shipping AI-drafted HR work looks like today vs. with Mairit.

Same AI-drafted PIP. Two very different outcomes.

Today
  • 01Send as-is. Hope the bias claim doesn't land.
  • 02HR rewrites from scratch. 90 minutes per review.
  • 03Slack employment counsel. Wait three days.
  • 04Pay outside firm $1,200. Wait a week.
With Mairit
  • Type /review. Pick a reviewer.
  • 22 minutes later, reviewed in the agent.
  • Senior HRBP sign-off, cryptographically attested.
  • Full audit trail. Compliance, legal, and the CHRO all happy.
Use cases

Three motions at launch. The ones where getting it wrong gets reported to a regulator.

Performance and comp. Hiring decisions. People analytics. The HR work that needs senior oversight before it's acted on.

The reviews every manager drafts. The ones HR has to defend.

Your managers use AI to draft annual reviews, comp recommendations, and PIPs. Today HR catches the worst ones at calibration, too late, after they've already shaped a manager's mental model. With Mairit, every AI-touched review routes to a senior HRBP for a structured bias and calibration check before the manager hits send. Inconsistent ratings flagged. Gendered language flagged. Comp recommendations sanity-checked against your bands. Shipped with an audit trail your CHRO can put in front of a regulator.

  • Annual reviews, mid-year check-ins, PIP drafts, comp recommendations
  • HRBP sign-off in 20 minutes, not 2 weeks of calibration
  • Built on your performance philosophy and rating distribution, not a generic rubric
Request a pilot
Sample review · 2026 H1 manager review, Senior PM
⚠ Bias flagThree "communicates assertively" mentions. Check against peer reviews for gendered pattern.
⚠ CalibrationRating exceeds team distribution by one band. Recalibrate or document rationale.
NoteEvidence section is strong. Specific examples cited.
✓ ApprovedAfter bias rewrite and calibration note.

AI screens 500 candidates. A human still has to defend the 8.

Your ATS now ranks candidates with AI. Your recruiters use ChatGPT to draft interview feedback. Your hiring managers ask Claude to recommend hire or no-hire. NYC AEDT requires you to bias-audit the system and notify candidates. The EU AI Act treats it as high-risk. Mairit puts a qualified reviewer between AI's output and the candidate's outcome. For the decisions that matter. With a record that meets the regulators' bar.

  • Shortlist decisions, interview feedback, hire and no-hire recommendations
  • Adverse-impact and rubric-consistency checks built into every review
  • AEDT and EU AI Act documentation generated automatically
Request a pilot
Sample review · Shortlist for Senior Engineer, 47 → 8 candidates
⚠ Adverse impactPass-rate gap of 19% between protected groups. Re-score with bias-mitigated rubric.
⚠ Rubric driftTwo scorecards weighted "culture fit" 30% above the agreed rubric.
NoteTop candidate flagged for non-traditional path. Reads stronger than AI score suggests.
✓ ApprovedAfter re-scoring. AEDT-compatible audit log retained.

AI drafts the analysis. A senior catches what it missed.

Your people analytics team uses AI to draft attrition models, DEI dashboards, pay-equity reports, and engagement readouts for leadership. AI gives you a credible draft. What it can't do is pattern-match against fifty similar analyses the way a senior people-analytics lead can. Catch the missing control. The overstated finding. The framing that won't survive a board question. Mairit routes the analysis to someone who's seen the shape before, sharpens it, and signs it before it reaches the C-suite.

  • Pay equity, DEI, attrition, engagement, workforce planning
  • Senior people-analytics specialists, not generalists
  • Your VP People stops being the methodology check at midnight
Request a pilot
Sample review · Q1 pay-equity analysis, leadership packet
⚠ MethodologyControls don't include tenure-in-role. Gap may be overstated.
⚠ Framing"No statistical evidence of bias" overclaims given sample size n=84.
NoteVisualizations are clean. Cohort cuts make the point.
✓ ApprovedWith methodology footnote and softened framing for board.
Built for the HR stack

The review infrastructure your CHRO and DPO have been quietly asking for.

MCP-native. Directory-aware. Attested. Built for the data your HR teams actually produce.

Routing

Your HRBPs first. Our specialists when yours can't cover.

Mairit reads your HRIS and knows which of your senior people are qualified for what motion. They're the default reviewers. When nobody internal fits, or they're not available, Mairit falls back to a curated specialist network you don't have to manage.

  • Integrates with your HRIS and directory. No new identity system to maintain.
  • Matching is explained, not black-boxed. You always see why.
  • 30 to 50 vetted external specialists across performance, hiring, and people analytics.
Reviewer options for this review
Aisha Khan
People Director · 38 similar
Internal
Marc Bouvier
Head of HRBP · 22 similar
Internal
Senior performance specialist
Mairit network · 220+ reviews
External
Rubrics

Structured checks. Not free-form essays.

Reviewers don't write three paragraphs of prose. They answer a rubric built for the specific motion. 15 questions for a performance review. 18 for a hiring decision. 20 for a pay-equity analysis. Faster for them. Consistent for you. Synthesizable across reviews.

  • Rubrics designed with senior CHROs, talent leaders, and people-analytics heads.
  • Review time typically 20 to 30 minutes, not 2 hours.
  • Free-text notes stay where judgment actually needs them.
Performance review rubric
  • Bias check (gendered language)
  • Bias check (rating distribution)
  • Calibration vs. team band
  • Evidence quality (specific, recent)
  • Comp alignment with band
  • Development plan present
  • 9 more...
Attestation

Every review, signed. Every action, logged.

When a reviewer attests, it's cryptographically bound to their identity and timestamped. Every material action produces an immutable audit record. When your DPO, your auditor, or a regulator asks who reviewed what and when, you export the answer in one click. In a format that maps to EU AI Act Article 14, AEDT, and GDPR Article 22.

  • Cryptographic attestation tied to a named, verified reviewer.
  • Tamper-evident audit log, CSV and JSON export.
  • Article 14, AEDT, and GDPR Art 22 export templates included.
Audit record · review #6128
Reviewer · verified · signed
aisha.khan@contentflow.com
Ed25519 · 2026-04-22 09:14Z
Review rubric
performance-review-v3.0
15 questions · 2 flags raised · 1 calibration note
Compliance mapping
EU AI Act Art 14 · GDPR Art 22
Export ready
✓ Attested. Ready to act on.
Compliance & security

Built for the data your HR teams actually produce.

Performance ratings. Comp decisions. Candidate scores. ER notes. Pay-equity records. Special-category, high-risk, regulator-watched. Mairit treats it that way from day one.

EU AI Act ready Annex III

Human oversight evidence for every Annex III employment decision, exportable per Article 14. Maps to Articles 9, 10, 13, and 14 obligations.

NYC AEDT documented Local Law 144

Bias-audit-compatible logging for every employment decision tool output. Candidate notice templates. Annual audit data ready to export.

GDPR Article 22 Art 22

Meaningful human review of automated decisions, attested by a named reviewer. Right-to-explanation export bundled with each review.

Pay transparency CO · NY · CA · WA · EU

Comp decision audit trail across US state laws and the EU Pay Transparency Directive. Defensible record of how comp recommendations were reviewed.

UAE PDPL & UK ERB PDPL · ERB

Workplace AI oversight provisions covered. Special-category data handling defaults applied for HR records.

Illinois AIVIA 820 ILCS 42

AI Video Interview Act compliance for AI-screened candidate interviews. Notice, consent, and demographic reporting captured in the review record.

Security & data handling

SOC 2 underway

Type I in audit. Type II target year 2.

Encryption everywhere

AES-256 at rest. TLS 1.3 in transit.

No training use, ever

Your employee data is never used to train models.

Reviewer access scoped

Per-review only. PII redaction default-on for HR data.

Pilot

The review layer for your AI-touched HR work. Pilot in 30 days.

Pick one motion. Plug into the AI your team already uses. See whether human review at machine speed actually changes how your HR function operates.

The pilot

  • Pick one motion. A calibration cycle, a hiring panel, or a pay-equity review.
  • Plug in. Works with the AI tools your team uses today via MCP. No HRIS migration. No new logins for managers.
  • Internal-first routing. Your HRBPs review by default. Mairit specialists fill gaps.
  • 30-day outcome pack. Throughput numbers, audit-ready documentation, and a clear go or no-go on rollout.

Who this is for

CHROs who'd rather have the audit trail in place before the regulator asks for it.

  • Mid-market companies (200 to 5,000 employees) with a CHRO or VP People in seat.
  • Teams already running AI in HR. At least one of: AI-drafted reviews, AI candidate scoring, AI-summarized people analytics.
  • Regulated, audited, or scrutinized. Public companies, regulated industries, EU operations, or government contractors.
Why now

Build the audit trail before you need it. The day you need it is the day it's too late to build it.