Intelletto.ai — EAPMS White Paper

AI Employee Appraisal Performance Managementv1.0 — Oct 02, 2025

The Sidecar Review Engine: embedded, explainable, governed

Executive Overview

Annual and mid-cycle reviews often devolve into email chases, spreadsheet macros, and compliance near-misses—especially at BPO volume. EAPMS rides sidecar to your HCM/ATS, orchestrating the entire review lifecycle from OKR/KPI drafting (informed by JD + Candidate data) through calibration, finalization, and acknowledgment. No rip-and-replace—just faster, more consistent decisions with audit‑ready evidence.

Problem Context

Fragmented tools: goals in one place, feedback in another, signatures elsewhere.
Cycle slippage: managers juggle queues; HRBPs firefight SLA breaches.
Opaque decisions: rating drift, inconsistent calibration, and audit gaps.
Customer-facing impact: delayed QBR evidence and weak traceability.

EAPMS Advantage

Sidecar delivery: operates inside existing systems of record.
AI assistance: draft OKR/KPI from JD + Candidate signals.
Throughput control: aging heatmaps, SLA posture, and nudges.
Governance‑first: reason codes, immutable trails, role‑based access.

What EAPMS Includes

Sidecar Review Engine

Portfolio & program control rooms, queue management, stage heatmaps, SLA posture, and nudges.

AI OKR/KPI Drafting

Turns JD + Candidate profile into measurable goals with suggested metrics and evidence anchors.

Evidence & Governance

Calibration records, finalization snapshots, and QBR evidence packs with source lineage.

Solutions & Benefits

Review Throughput & Control

Stage Aging Heatmap with high‑contrast mode.
SLA posture (due <48h / <24h / breached) and auto‑nudges.
Manager Workbench queue: oldest‑first with one‑click actions.
Portfolio roll‑ups for HRBP / Exec command center.

35–50%Faster cycle completion

+12–18 ptsOn‑time completion

−30–45%Mgr hours on shepherding

−20–35%Escalations

AI‑Drafted OKR/KPI (JD + Candidate)

Role‑aware objective suggestions linked to client SOWs.
KPI libraries by program with measurable targets.
Explainable reason codes and evidence anchors.
HRBP/Manager co‑authoring and version control.

2–3×Faster goal drafting

+10–20%Measurable KPIs used

↓ reworkFewer calibration edits

↑ clarityAudit‑ready goals

Evidence, Finalization & QBR Readiness

Calibration Board with variance tracking and reason codes.
Finalization & Acknowledgment tracker with snapshot hashing.
Client QBR Evidence Pack Builder with export & lineage.
Audit & Governance hub: SoD, retention, and access attestations.

3–5dEarlier QBR readiness

−60–80%Audit exceptions

↓ disputesEvidence‑backed ratings

↑ trustClient transparency

Market timing

Why now: Review cycles must keep pace with volatile scope and staffing. GenAI enables measured automation—drafting OKRs from JD + Candidate and streamlining throughput—while governance requirements intensify across clients and regulators.

What’s changed in performance management

From static forms → dynamic flows: program/site context changes quarterly.
From opinion → evidence: link goals and ratings to customer outcomes and QA.
From after‑action → in‑cycle nudges: intervene before SLAs breach.
From black boxes → explainability: reason codes & lineage for audit/QBR.

ROI of EAPMS

Sidecar delivery accelerates value: embed EAPMS inside existing HCM/ATS, keep users in‑flow, and measure outcomes continuously.

Where the ROI comes from

Throughput: 35–50% faster cycle completion; +12–18 pts on‑time rate.
Quality: lower rework in calibration; better KPI measurability.
Compliance: 60–80% fewer audit exceptions; stronger access controls.
Client trust: earlier QBR readiness; dispute rates down.

Directional ranges for planning; actuals depend on volumes, role mix, and integration scope.

Success matrix

Proof-of-Impact (POI) snapshot: how Sidecar throughput + AI drafting translate into measurable outcomes. Calibrate to your portfolio and cycles.

Area	KPI	Baseline	Pilot lift	Window	Definition
Throughput	Cycle completion time	3–6 weeks	−35% to −50%	Weeks 1–6	Calendar time from kickoff to signed acknowledgment.
Throughput	On‑time completion	Team baseline	+12 to +18 pts	Weeks 1–6	% of reviews completed within SLA.
Quality	Calibration rework	—	−20% to −40%	Weeks 2–6	Edits post‑calibration due to unclear goals or evidence.
Quality	Evidence completeness	Varies	+25–40 pts	Weeks 2–6	Ratings >=2 artifacts linked (QA, tickets, metrics).
Compliance	Audit exceptions	—	−60% to −80%	Quarter	Findings on access, approvals, retention, and lineage.
Client	QBR readiness	T‑0	3–5 days earlier	Quarter	Evidence pack assembled before QBR week.

POI playbook:

Baseline throughput/quality KPIs; define acceptance thresholds.
Pilot Sidecar across 2–3 programs; enable AI OKR/KPI drafting.
Compare cohorts on completion time, on‑time rate, rework, and audit exceptions.
Calibrate weights; expand to more programs; lock governance checks.

Deployment & Integration

Sidecar model

Embeds UI components within HCM/ATS workflows (no context switching).
APIs for lists, details, explainers, exports, and evidence.
Event hooks for 30/60/90‑day feedback loops.

Rollout plan

Weeks 0–1: Connect systems; define programs/sites & KPIs.
Weeks 1–2: Baseline metrics; configure KPI libraries.
Weeks 2–4: Pilot Sidecar; tune nudges & SLA thresholds.
Weeks 4–8: Enable evidence packs; finalize governance.

Trust, Privacy & Governance

Explainability

Each suggestion or score has reason codes and artifacts. Stakeholders can audit “why this rating?”

Responsible AI

Monitors drift and adverse impact; interventions recorded with justifications.

Privacy

Role‑based access, retention, and SoD; evidence lineage for exports.

Indicative unit economics (per review)

OKR/KPI drafting assist: ~$0.08–0.15
Explainable scoring/explainers: ~$0.10–0.18
Evidence assembly (exports): ~$0.02–0.05

Directional for planning; excludes storage/egress; subject to model/provider choice and volume tiers.