AI Intelligence Layer · HRMS Integration

How intelligence
moves through your
HRMS stack.

pegarecruiter doesn't replace your HR systems — it amplifies them. The intelligence layer reads from every data source in your HRMS, synthesizes signals in real time, and writes explainable decisions back into the tools your team already uses.

340+
Signal types processed
98ms
Avg evaluation latency
100%
Explainable outputs
0
Black box decisions
Signal Confidence
92.4
Candidate: Avery Chen
HRMS Sync
✓ Greenhouse
Last sync: 2s ago · 12 records
NLP Tokens
3,241
Parsed from resume + notes
Bias Monitor
0 alerts
Disparate impact: Clear
Model Version
v2.4.1
Fine-tuned · your org context
Throughput
2.4k/hr
Current processing rate
Intelligence
Engine
📥Ingest
🔬Parse
📤Output
🔁Learn
📋ATS
👥HRIS
💰Payroll
🎓LMS
01 · The Intelligence Loop

A continuous cycle of
read, reason, return.

pegarecruiter operates as a persistent intelligence layer — constantly reading from your HRMS ecosystem, reasoning over live signals, and returning actionable outputs back into the same systems your team uses daily.

📥
Ingest from every HRMS source
ATS records, HRIS profiles, payroll bands, LMS completion data, performance reviews — all normalized into a unified candidate schema.
🔬
Parse, weight & reason
340+ signal types extracted, scored against role requirements, and assembled into a calibrated confidence output with full explainability metadata.
📤
Return to your stack
Scores, explanations, risk flags, and shortlists write back into your ATS, Slack, dashboards, and data warehouse automatically.
🔁
Learn from outcomes
Hire decisions, 90-day performance, and attrition data feed back into the model weekly — continuously improving precision for your context.
02 · HRMS Integration Map

Connected to every
layer of your HR stack.

pegarecruiter acts as the intelligence backbone — reading from and writing back into every system in your HR technology ecosystem, without replacing any of them.

pegarecruiter
Intelligence
Real-time layer
ATS Layer
Applicant Tracking
Greenhouse · Lever · Workday · iCIMS · SAP SF
Bidirectional sync
HRIS Layer
People & Workforce
BambooHR · Rippling · Workday HCM · ADP
Profile enrichment
Compensation
Payroll & Bands
Salary bands · comp ratios · equity data
Read-only sync
Talent Development
LMS & Skills Data
Cornerstone · Degreed · LinkedIn Learning
Skills enrichment
Performance
Perf & Reviews
Lattice · Leapsome · Culture Amp · 15Five
Outcome feedback
Analytics
Data Warehouse
Snowflake · BigQuery · Redshift · Looker
Live export stream
Collaboration
Comms & Notify
Slack · Teams · Gmail · Calendar
Push notifications
03 · Intelligence Modules

Seven AI modules.
One unified score.

Each module handles a distinct intelligence domain. They run in parallel on every candidate evaluation and their outputs are assembled by the Confidence Aggregator into a single, explainable result.

Module 01
📝
NLP Signal Extractor
Processes unstructured text — resumes, cover letters, ATS notes, LinkedIn — through a fine-tuned transformer model to extract structured signals.
Entity extraction: skills, roles, orgs, dates, achievements
Sentiment & tone analysis on cover letters and notes
Ambiguity resolution for job titles across industries
Extracted Tokens
PythonDistributed SystemsStaff ICMIT CS4.2yr tenureContract gapRustSan FranciscoKubernetes
Module 02
🕸️
Skill Graph Engine
Maps extracted skills onto a 2.4M-node taxonomy graph to identify adjacencies, infer implied competencies, and calculate match depth against role requirements.
Infers adjacent skills not explicitly stated in resume
Distinguishes depth vs breadth of skill clusters
Updates weekly from job market signal data
Skill depth vs role requirement
Module 03
📈
Career Trajectory Model
Analyzes career arc, tenure patterns, progression velocity, and compensation growth to predict retention likelihood and future performance potential.
Avg tenure, job-hop frequency, promotion indicators
Seniority velocity — how fast they've leveled up
Industry-normalized benchmarks per role type
Module 04
💰
Compensation Calibration Engine
Cross-references candidate expectations against your internal pay bands (from HRIS/payroll), real-time market comp data, and internal equity ratios. Flags misalignments before they become offer declines.
Candidate vs Band Analysis
Band Min
Candidate
↑ $18k
Band Max
Data Sources Used
HRIS pay band data
Levels.fyi market data
Internal equity ratios
Offer acceptance history
Module 05
⚖️
Fairness & Bias Monitor
Runs continuous disparate impact analysis across every batch of evaluations. Monitors selection rates across gender, race, age, disability, and veteran status.
4/5ths rule monitoring with real-time threshold alerts
Signal-level audit — identifies which features contribute to gaps
NYC Local Law 144 & EU AI Act compliant outputs
Module 06
🔮
Performance Predictor
Uses historical outcome data from your HRIS/performance system to build org-specific models that predict 90-day ramp time and first-year performance rating.
Trained on your actual hire/performance outcome pairs
Predicts ramp time, 90-day rating, 1yr retention probability
Confidence bounds shown — never overfit to thin data
Module 07
🎯
Confidence Aggregator
Assembles outputs from all six modules into a single calibrated confidence score (0–100), with weighted explainability metadata and a structured human-readable summary.
Overall
87%
High confidence · 6/7 modules strong signal
04 · End-to-End Data Flow

From HRMS record to
explainable decision — step by step.

↓ HRMS Data Sources
ATS
Candidate Record
Application, resume, assessments, stage history, recruiter notes
Live sync
HRIS
Workforce Profile
Role context, team structure, skills taxonomy, headcount plan
Read
Compensation
Pay Band Data
Internal bands, comp ratios, equity levels, offer history
Encrypted
Performance
Outcome History
Past hire ratings, 90-day reviews, attrition signals, peer data
Feedback loop
Layer 1 · Normalization
Unified Candidate Schema
All HRMS sources normalized into a consistent structured record. Field mapping, deduplication, and enrichment applied. Output: standardized JSON candidate object with metadata provenance.
Schema v3.2Field mappingDedup logic
Layer 2A · NLP
Text Parsing Pipeline
Transformer model extracts 340+ signal types from all unstructured fields. Latency: ~40ms
~40ms
Layer 2B · Graph
Skill Graph Traversal
Skills mapped onto 2.4M-node taxonomy. Adjacency scoring and depth analysis computed. Latency: ~25ms
~25ms
Layer 2C · Model
Predictive Models
Tenure, comp calibration, performance prediction, bias monitor — run in parallel. Latency: ~30ms
~30ms
Layer 3 · Confidence Aggregation + Explainability
Score Assembly & Explanation Generation
Module outputs weighted and assembled into final confidence score. SHAP values computed for explainability. Human-readable summary generated. Bias check run on final output. Total pipeline latency: 94ms avg, 180ms P99.
94ms avgSHAP valuesBias clearedAudit logged
↓ Outputs — Written Back to Your Stack
ATS Write-back
Candidate Score
Score, rank, flags, and explanation written into ATS candidate record
Auto-write
Dashboard
TA/Recruiter View
Role-based views: ranked pipeline, signal breakdown, pre-interview brief
Role-gated
Compliance
Audit Trail
Immutable log: model version, input hash, output, bias status, timestamp
Immutable
Analytics
Warehouse Export
Full event stream to Snowflake, BigQuery, or Redshift for org analytics
Real-time
05 · HRMS Module Detail

What pegarecruiter does
with each HR system.

For every system in your HR stack, here's exactly how pegarecruiter connects, what data it reads, what intelligence it applies, and what it returns — with no black boxes.

ATS · Applicant Tracking System
Greenhouse, Lever, Workday, iCIMS
The primary data source. pegarecruiter ingests every inbound application in real time and writes scores, explanations, and stage recommendations back — keeping your ATS as the system of record.
How it works
Reads: Application record, resume file, assessment results, recruiter notes, stage history, custom fields
Processes: NLP extraction, skill graph match, tenure analysis, comp calibration against role
Writes back: Confidence score, signal explanation, risk flags, recommended stage action, shortlist rank
ATS RecordParse + ScoreScore + Explanation
HRIS · Human Resource Information System
BambooHR, Rippling, Workday HCM
HRIS data enriches candidate evaluation with real organizational context — headcount plans, team structure, internal mobility history, and workforce benchmarks from inside your own org.
How it works
Reads: Org chart, team composition, role taxonomy, skills inventory, headcount authorizations
Processes: Calibrates required skills against internal benchmarks; identifies internal mobility candidates
Applies to: Skill match scoring, team fit context, internal candidate prioritization logic
Org ContextRole CalibrationAdjusted Score
Compensation · Payroll & Pay Bands
Internal Bands + Market Data
Salary misalignment is the #1 cause of late-stage offer declines. pegarecruiter cross-references candidate expectations against your internal bands and live market data — flagging gaps before they waste everyone's time.
How it works
Reads: Pay bands per level/role (encrypted), internal equity ratios, historical offer-acceptance data
Processes: Infers candidate expectation range from resume signals and market benchmarks (Levels.fyi, Radford)
Writes back: Salary fit score (0–100), alignment flag (in-band / above-band / below-band), recommended handling
Band DataExpectation ModelFit Flag
LMS · Learning Management System
Cornerstone, Degreed, LinkedIn Learning
LMS data tells you what skills your team has actually been developing — and by inference, what adjacent skills a new hire needs to complement the team, or what gaps exist that only external hiring can fill.
How it works
Reads: Team skill completions, certification data, learning pathway progress
Processes: Builds team skill map; identifies gaps between current team capability and role requirement
Applies to: Skill priority weighting — skills absent from team get higher match weight for candidate scoring
Team SkillsGap AnalysisWeighted Match
Performance Management
Lattice, Leapsome, Culture Amp, 15Five
This is where the intelligence loop closes. Past hire performance data feeds directly back into pegarecruiter's predictive models — so the system learns from your actual outcomes, not industry averages.
How it works
Reads: 90-day review scores, performance ratings, manager assessments, voluntary attrition events
Processes: Matches outcomes to original candidate signals; identifies which signals predicted good/bad outcomes
Applies to: Weekly model fine-tuning — improving signal weights and prediction accuracy over time
Performance DataSignal AttributionModel Update
Analytics & Data Warehouse
Snowflake, BigQuery, Redshift, Looker
Every evaluation, decision, and outcome event is exported to your data warehouse in real time — enabling your analytics team to build custom TA reporting, workforce planning models, and diversity tracking dashboards.
How it works
Exports: Full evaluation events, candidate signals, decision metadata, model version, audit log
Schema: Documented star schema — candidate_dim, evaluation_fact, signal_fact, outcome_fact
Latency: Real-time stream via Kafka connector or 15-min batch via native connectors
All EventsWarehouse StreamBI / Analytics
06 · Live Intelligence Example

What an evaluation
actually looks like.

pegarecruiter · candidate evaluation · eval_id: eval_7x9abc · latency: 92ms
✓ Bias cleared · Audit logged
Input · Candidate Record (from Greenhouse + HRIS)
Avery Chen
Role: Staff ML Engineer
Applied via: Greenhouse · Stage: Initial review
Skills detected: Python, PyTorch, Rust, distributed systems, MLOps, Kubernetes
Education: Stanford MS CS · 2019
Experience:
Anthropic · Staff IC · 3.2yr · 2 promotions
Google Brain · Senior SWE · 2.1yr
6-month contract gap (2022-Q2)
Comp expectation (inferred): $340k–$380k
Band max (HRIS): $320k · ⚠ Above band
Performance data: N/A (external candidate)
Output · Intelligence Result
Skill Match
9.5
Tenure
8.2
Career Arc
8.8
Comp Fit
5.5
Team Fit
9.0
Bias Status
87%High confidence · 92ms
Strong hire signal 2 promotions ⚠ Salary gap $20–60k Gap: 6mo (2022) Stanford MS
Recommended action: Advance to phone screen.
Priority flag: Negotiate comp before technical loop.
Signal sources: Greenhouse resume · HRIS band data
· Skill graph v2.4 · Career model v1.8 · Market comp
Model: pega-eval-v2.4.1 · Audit ID: audit_7x9abc
07 · The Learning Loop

Gets smarter with
every hire you make.

Most AI hiring tools are static. pegarecruiter runs a continuous learning loop — connecting hiring decisions to performance outcomes and feeding that signal back into the model weekly. Your context improves the intelligence for your specific org.

📊
Outcome Collection
Hire/no-hire decisions, 90-day ratings, and attrition events flow from your ATS and performance system into the feedback layer automatically.
🔬
Signal Attribution
Each outcome is traced back to the original signals that produced the score — identifying which features were predictive vs misleading in your specific context.
⚙️
Weekly Fine-Tuning
Signal weights are adjusted weekly. Features that consistently predicted good outcomes get higher weight; poor predictors get dampened. Privacy-preserving — no candidate PII used in training.
📈
Measurable Improvement
Customers see a 12–34% reduction in first-year attrition after 6 months of feedback loop operation. The model learns your definition of a great hire.
Model
Core
01 · Hire decision
ATS Outcome
Hire/no-hire recorded in Greenhouse or Lever. Auto-tagged to original evaluation.
02 · Performance
90-Day Review
Rating from Lattice or Leapsome ingested. Paired with original candidate signals.
03 · Attribution
Signal Analysis
Which signals predicted this outcome? Attribution computed with counterfactual analysis.
04 · Update
Model Fine-Tune
Weights adjusted. Precision improves. Next evaluations are more calibrated to your context.

See the intelligence
work on your data.

Connect your HRMS in minutes. No data science team needed. No model training required. Intelligence starts flowing on day one.

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