Open Roles
34
Across 8 departments
Pipeline Active
247
AI screened this week
Flight Risk
8
Attrition risk score >0.7
Time-to-Hire
18d
vs 42d pre-AI
π€ AI Agent Status
12 people AI agents across talent, workforce, and culture
Resume Screening247 screened today
Retention Signal Monitor8 flight risks
Interview Intelligence12 interviews scored
Onboarding AI4 new joiners
DEI Bias MonitorAll pipelines clear
Performance IntelligenceQ2 cycle active
π‘ Live People Intelligence Feed
Real-time AI agent actions across talent and workforce
Urgent Talent Signals
EMP-0284 Β· Engineering
FLIGHT RISKS. Park, Senior Engineer L5
AI: Comp below band median, no promo discussion in 14 months
CAND-4721 Β· Product
STRONG FITM. Torres, Sr. PM Candidate
AI: Exceeds criteria on 8/9 dimensions β move to final round
ROLE-0847 Β· Engineering
TIME PRESSUREStaff Engineer β 47 days open
AI: JD may be over-specified β 3 req changes suggested
Why HRTalentOS
β± Time-to-Hire Cost
Every open role costs 1.5β2Γ annual salary in lost productivity. At 34 open roles and $180K avg salary, that's $9.2M in annual drag. HRTalentOS cuts time-to-hire from 42 to 18 days β recovering 57% of that loss.
πΈ Attrition Cost
Replacing a senior engineer costs $200Kβ$400K in recruiting, onboarding, and productivity loss. With 8 flagged flight risks, that's $2.4M at risk. Retention Agent identifies the signal 60β90 days before resignation.
βοΈ Hiring Bias Risk
Biased hiring creates legal exposure and talent homogeneity. DEI Bias Monitor scans every JD, screening rubric, and interview score sheet for language and pattern bias β EEOC-defensible hiring by design.
Applied
847
AI Screened
247
Interviewing
38
Offers Out
4
Hired (30d)
11
Top Screened Candidates
CAND-4721 Β· Sr. PM
0.91 MATCHM. Torres
CAND-4698 Β· Staff Eng
0.88 MATCHJ. Okafor
CAND-4734 Β· Sr. PM
0.76 MATCHA. Kim
CAND-4712 Β· Staff Eng
0.74 MATCHP. Sharma
CAND-4756 Β· PM
0.61 MATCHR. Davis
Candidate Profile β CAND-4721
M. Torres β Senior Product Manager
Applied: May 16 Β· Source: LinkedIn Β· Currently: PM @ Stripe
Experience Match
8yr Β· Fintech β
Skills Match
8 of 9 criteria β
Comp Expectation
$185K β in band
Urgency
3 competing offers
AI Recommendation
Move to final round immediately. Candidate has 3 competing offers with 1-week decision deadline. Strengths: fintech domain, data-driven PM, cross-functional leadership. Gap: no enterprise experience (ask in final interview). Hiring risk if no action in 48h.
Total Agents
12
Decisions Today
1,284
Flight Risks
8
Bias Events
0
Talent Acquisition Agents
Resume Screener
Scores CVs against structured rubrics built from job requirements. Ranks candidates on skills, experience, domain fit, and signals. Zero keyword matching β semantic understanding.
Running Β· 247 screened
Reflection + RubricJD Intelligence
Generates inclusive, precise job descriptions from role briefs. Flags over-specification, gendered language, and unrealistic requirements. A/B tests JDs for application conversion rate.
Running Β· 34 roles
Reflection + DEIInterview Intelligence
Generates structured interview guides per role. Scores interview notes for consistency, halo effect, and recency bias. Synthesises panel feedback into calibrated recommendation.
Running Β· 12 scored
Reflection + BiasWorkforce Intelligence Agents
Retention Signal Monitor
Analyses 40+ signals: performance trends, compensation positioning, LinkedIn activity, engagement survey scores, 1:1 frequency, and promotion velocity. Flags at-risk employees 60β90 days before resignation.
Running Β· 8 flags
ReAct + SignalsPerformance Intelligence
Calibrates performance ratings across managers to remove grade inflation and harshness bias. Identifies high performers at flight risk, underperformers needing support, and succession candidates.
Running Β· Q2 cycle
Reflection + CalibrationDEI Analytics Agent
Tracks representation across hiring funnel, promotion rates, pay equity, and engagement scores by demographic. Flags statistical disparities before they become systemic. EEOC-defensible reporting.
Running Β· all clear
ReAct + StatisticsEmployee Lifecycle Agents
AI Onboarding
Personalised 30/60/90 day onboarding plans. Matches new hires to buddy mentors, surfaces relevant documentation, and nudges managers on key touchpoints. 40% faster ramp time.
Running Β· 4 new joiners
Planning + PersonalisationOffer Intelligence
Benchmarks offers against real-time market data, internal equity, and comp bands. Predicts offer acceptance probability. Suggests negotiation strategy when candidate signals hesitation.
Running Β· 4 offers
Reflection + Market DataLearning Path AI
Maps skill gaps from performance data to curated learning paths. Surfaces internal mobility opportunities before external job searches begin. Reduces attrition via career development visibility.
Idle Β· 847 employees
Planning + Skills GraphScreened Today
247
Strong Fits
34
Score >0.80
Bias Events
0
Screening Time
4min
vs 45min manual
π AI Screening β ROLE-0847 Β· Staff Engineer
Structured rubric: 9 criteria Β· Skills, experience, domain, leadership
PARSE β Resume + LinkedIn + portfolio analysed
MATCH β Distributed systems: STRONG β
MATCH β Scale (10M+ users): STRONG β
MATCH β Staff-level scope: STRONG β
PARTIAL β ML infra: background noted, not primary
DEI β Bias check passed Β· rubric-only scoring
SCORE β 0.88 Β· STRONG FIT Β· advance to screen
MATCH β Distributed systems: STRONG β
MATCH β Scale (10M+ users): STRONG β
MATCH β Staff-level scope: STRONG β
PARTIAL β ML infra: background noted, not primary
DEI β Bias check passed Β· rubric-only scoring
SCORE β 0.88 Β· STRONG FIT Β· advance to screen
AI Recommendation: Advance to 45-min technical screen. Focus areas: system design (distributed state management), and staff-level influence β portfolio shows strong individual contribution but limited cross-org evidence.
π DEI Bias Monitoring
Every screening decision audited for protected-class bias
β Rubric-only scoring: Candidates scored on 9 structured criteria only β name, school, and demographic signals excluded from model inputs. Decisions defensible under EEOC Uniform Guidelines.
β Pipeline diversity check: Current Staff Eng pipeline is 42% women, 38% URG β above market baseline (28% and 22%). No funnel dropout bias detected at any stage.
β» JD language scan: Original JD flagged "rockstar" and "ninja" (gendered language). Auto-replaced with "exceptional" and "skilled". Predicted +18% women applicants from revised language.
β Interview note flag: Interviewer 3 used "culture fit" 4Γ without evidence. Flagged for calibration discussion β replace with specific behavioural observation.
JDs Generated
47
Bias Flags Removed
284
Conversion Lift
+34%
Applications per post
Over-spec Flags
12
Requirements simplified
βοΈ JD Optimisation β ROLE-0847 Β· Staff Engineer
AI-generated Β· Inclusive Β· Conversion-optimised
Staff Engineer β Platform Infrastructure
What you'll do:
Lead technical direction for our distributed platform serving 15M users. Partner with engineering teams across 4 product areas to define and deliver infrastructure that scales. Mentor engineers at all levels and influence our engineering culture.
What we're looking for:
β Deep experience with distributed systems at scale (not "10+ years required")
β Track record of technical leadership that influenced beyond your team
β Systems thinking: comfort making architecture decisions with incomplete information
β Collaborative approach to unblocking teams and growing talent
What we offer:
$195Kβ$230K Β· Equity Β· Flexible remote Β· Engineering-led culture
Lead technical direction for our distributed platform serving 15M users. Partner with engineering teams across 4 product areas to define and deliver infrastructure that scales. Mentor engineers at all levels and influence our engineering culture.
What we're looking for:
β Deep experience with distributed systems at scale (not "10+ years required")
β Track record of technical leadership that influenced beyond your team
β Systems thinking: comfort making architecture decisions with incomplete information
β Collaborative approach to unblocking teams and growing talent
What we offer:
$195Kβ$230K Β· Equity Β· Flexible remote Β· Engineering-led culture
AI changes: Removed "CS degree required" (dropped to preferred) Β· "rockstar" β "exceptional" Β· 10yr min removed Β· Added flexible remote signal Β· Predicted +34% application volume, +18% women applicants
π¬ JD Analysis Process
How the JD Agent improves every posting
Step 1 β Bias scan: Flag gendered language, ageist phrasing, and exclusionary signals (e.g. "recent grad" implying age preference)
Step 2 β Over-spec check: Compare requirements to actual role needs and market norms. Flag "nice to have" masquerading as "must have"
Step 3 β Conversion prediction: Score JD against our highest-converting past posts for similar roles. Rewrite to match
Step 4 β Inclusive framing: Reframe "requirements" as "what we're looking for". Add belonging signals. Test reading grade level (target: Grade 10)
Flight Risk (>0.7)
8
60β90 day warning
Retained (90d)
34
After AI intervention
Regrettable Exits
2
This quarter
Signal Accuracy
78%
vs 12% annual survey
Flight Risk β Top Flags
S. Park Β· Senior Engineer L5 Β· Platform Team
Tenure: 3.1 years Β· Manager: T. Williams
Comp vs Band
12% below median
Last Promo
14 months ago
LinkedIn Activity
3Γ usual (7 days)
AI signals: Comp 12% below band median Β· No promotion discussion logged in 14 months Β· LinkedIn profile views up 3Γ Β· 2 of last 3 scheduled 1:1s skipped Β· Below-average engagement survey score (3.2/5). Recommended intervention: manager conversation within 7 days + comp review + explicit career path discussion.
π Retention Signal Model
40+ signals across engagement, career, comp, and social
Comp vs market positioningHigh weight
Promotion velocity (vs peers)High weight
Manager 1:1 frequencyMedium
Engagement survey trendMedium
LinkedIn activity (opt-in)Low weight
πΈ Retention ROI
Cost of regrettable attrition vs cost of AI intervention
Cost per regrettable exit: $200Kβ$400K (recruiting, onboarding, productivity, knowledge loss) for senior engineer. 8 flagged risks = $1.6Mβ$3.2M at risk.
Intervention cost: Manager conversation + comp review + career pathing = $8Kβ$15K per employee. AI signal cost: $0.02 per employee per day.
Net ROI (last 90 days): 34 retained employees Γ avg $250K replacement cost = $8.5M retained value. Intervention cost: $420K. ROI: 20Γ.
Interviews Today
12
Consistency Score
0.94
Bias Flags
1
Halo effect detected
Hire Predict Accuracy
74%
π€ Interview Intelligence β How It Works
Interview Intelligence Agent generates role-specific structured interview guides (behavioural + technical). After each interview, it analyses panel notes for: consistency across interviewers, halo effect (over-weighting first impressions), recency bias, and protected-class language. Synthesises all panel feedback into a calibrated hire/no-hire recommendation with confidence score. Final hiring decision always made by hiring manager β AI provides data, humans decide.
Offers Outstanding
4
Acceptance Rate
87%
vs 71% pre-AI
Equity Compliance
100%
Avg Offer Cycle
1.2d
vs 4.5d manual
π Offer Intelligence
Offer Intelligence Agent benchmarks every offer against real-time market data (Levels.fyi, Radford, Mercer), internal comp band, and pay equity analysis before it reaches the candidate. Predicts acceptance probability from candidate signals (urgency, competing offers, hesitation language in recruiter notes). When probability is below 0.7, suggests pre-emptive negotiation strategy. Internal equity check ensures no offer creates a comp inversion with existing employees at the same level. Every offer is EEOC-defensible before it's sent.
New Joiners (30d)
4
Ramp Time
β40%
Day-90 Engagement
4.6/5
Early Attrition (90d)
0%
vs 12% industry
π AI Onboarding β 30/60/90 Day Personalisation
AI Onboarding Agent creates a personalised onboarding plan from the new joiner's background, role, and team context. Day 1: automated equipment provisioning, system access, and buddy match. Week 1: curated documentation path based on role β not a 200-page handbook dump. 30 days: learning milestones calibrated to role complexity. 60 days: first deliverable checkpoint nudges manager. 90 days: sentiment check-in and career path first discussion initiated automatically. 40% faster time-to-productivity measured via manager assessment and peer feedback surveys.
Employees in Cycle
847
Calibration Bias Flags
14
High Performers
124
Succession Candidates
28
π Performance Intelligence
Performance Intelligence Agent calibrates ratings across all managers to identify and remove systematic bias: grade inflation (manager A gives 90% "exceeds"), grade compression (manager B never gives "outstanding"), and recency bias (Q4 performance over-weighted). Surfaces high performers below market comp who are flight risks. Identifies employees whose contribution exceeds their current level β succession planning candidates. All calibration suggestions require CHRO review before any rating changes.
Representation (Eng)
38%
Women Β· target 45%
Pay Equity Score
0.98
Adjusted for role/level
Promotion Parity
0.94
Pipeline Diversity
42%
Women in active pipeline
π DEI Analytics β Systemic View
DEI Analytics Agent tracks representation, advancement, pay, and engagement across every demographic dimension available (with employee consent). Funnel analysis: where does diversity drop in the hiring funnel? Promotion analysis: are promotion rates statistically equal by group after controlling for performance ratings? Pay equity: adjusted pay gap analysis by role and level. All analysis runs quarterly and feeds directly into leadership DEI reporting. Findings are statistical observations β root cause analysis and interventions remain human decisions.
Agents Active
12
Decisions/Day
1,284
Retention Flags
8
Bias Events
0
π‘ Live Agent Trace
All AI decisions logged Β· EEOC Β· GDPR Β· CCPA compliant
π‘ People AI Governance
Why every decision requires human approval
No automated hiring decisions: AI scores and ranks candidates but hiring managers make all final decisions. Compliant with EU AI Act Article 22 (automated decision-making in employment).
EEOC defensibility: Every screening decision is rubric-based and documented. Adverse impact analysis runs on every role. Full audit log for OFCCP compliance.
GDPR/CCPA data minimisation: LinkedIn signals used only with employee opt-in. Retention model uses anonymised signals β no individual surveillance. Data retention: 90 days post-departure.