From prompts to production cultural intelligence.
Monday: 3 core prompts for sentiment analysis, engagement scoring, and culture mapping. Tuesday: automated agents processing Slack, surveys, and 1:1 notes. Wednesday: workflows for HR, managers, and executives. Thursday: complete technical architecture for enterprise-scale cultural analytics with compliance, multi-agent orchestration, and ML pipelines.
Key Assumptions
System Requirements
Functional
- Ingest employee signals from 5+ sources (Slack, surveys, 1:1s, HRIS, reviews)
- Real-time sentiment analysis with context-aware scoring
- Engagement trend detection across teams, departments, and company
- Culture mapping with dimension scoring (psychological safety, inclusion, innovation)
- Anomaly detection for burnout, flight risk, and team health issues
- Role-based dashboards (HR, managers, executives) with drill-down
- Automated alerts for critical cultural shifts or individual risks
Non-Functional (SLOs)
💰 Cost Targets: {"per_employee_per_month_usd":2.5,"per_signal_processed_usd":0.001,"ml_inference_per_1k_usd":0.5}
Agent Layer
planner
L3Decomposes incoming signals into tasks, routes to specialized agents
🔧 Signal classifier, Priority scorer, Agent registry
⚡ Recovery: If classification fails: route to manual queue, If agent unavailable: queue task with retry backoff
executor
L4Runs primary analysis workflows (sentiment, engagement, culture)
🔧 LLM inference (GPT-4, Claude), Feature Store queries, Vector similarity search
⚡ Recovery: If LLM timeout: retry with exponential backoff (3x), If low confidence (<0.7): flag for human review, If feature unavailable: use cached baseline
evaluator
L3Validates output quality, detects hallucinations, ensures accuracy
🔧 Consistency checker, Cross-reference validator, Anomaly detector
⚡ Recovery: If quality check fails: route to manual review queue, If inconsistent results: re-run with different model, If anomaly detected: alert data science team
guardrail
L2Enforces policies, redacts PII, prevents unsafe outputs
🔧 PII detection (AWS Comprehend, Presidio), Policy engine, Toxicity classifier
⚡ Recovery: If PII detected: block processing until redacted, If policy violation: escalate to compliance team, If guardrail service down: fail-safe block all processing
sentiment
L4Analyzes emotional tone, context, and trends in employee signals
🔧 LLM sentiment analysis, Historical trend comparison, Contextual embedding search
⚡ Recovery: If LLM fails: fallback to rule-based sentiment, If context missing: use team-level baseline, If confidence low: flag for human validation
engagement
L4Scores employee engagement based on multi-signal fusion
🔧 Multi-modal fusion model, Weighted scoring algorithm, Trend detection
⚡ Recovery: If signal missing: impute from historical average, If score anomalous: validate with manager input, If trend unclear: extend lookback window
culture
L4Maps organizational culture across dimensions (safety, inclusion, innovation)
🔧 Dimension classifier, Network analysis (collaboration graphs), Comparative benchmarking
⚡ Recovery: If team too small (<5): aggregate to department level, If data sparse: use industry benchmarks, If conflicting signals: weight by recency and source reliability
ML Layer
Feature Store
Update: Real-time for activity metrics, daily batch for aggregates
- • employee_30d_sentiment_avg
- • team_sentiment_baseline
- • slack_activity_frequency
- • 1on1_meeting_count
- • survey_response_rate
- • peer_collaboration_score
- • manager_feedback_frequency
- • promotion_velocity
- • tenure_months
- • department_size
Model Registry
Strategy: Semantic versioning with A/B testing for major versions
- • sentiment_classifier_v3
- • engagement_scorer_v2
- • culture_dimension_mapper
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ Employee privacy concerns (surveillance perception)
High✓ Mitigation: Transparent communication: anonymized insights only, opt-out available, no individual tracking for managers. HR-only access to raw data. Regular privacy audits.
⚠️ Biased sentiment analysis (demographic disparities)
Medium✓ Mitigation: Bias testing across gender, race, age. Quarterly fairness audits. Diverse training data. Human review for edge cases.
⚠️ LLM hallucinations (false insights)
Medium✓ Mitigation: Multi-layer validation (confidence, consistency, human review). Shadow mode testing. Gradual rollout with feedback loops.
⚠️ Data breach (PII exposure)
Low✓ Mitigation: Encryption at rest and in transit. PII redaction before LLM. SOC2 compliance. Regular penetration testing. Incident response plan.
⚠️ Integration failures (Slack, HRIS downtime)
Medium✓ Mitigation: Retry logic with exponential backoff. Queue for failed tasks. Fallback to cached data. Multi-source redundancy.
⚠️ Cost overruns (LLM API costs)
Medium✓ Mitigation: Cost monitoring with alerts. Rate limiting. Caching for repeated queries. Multi-LLM with cost optimization.
⚠️ Low adoption (managers don't use insights)
High✓ Mitigation: User training and onboarding. Actionable insights (not just dashboards). Manager coaching on how to act on data. Feedback loops to improve relevance.
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
9-layer architecture from data ingestion to insights delivery
Presentation
5 components
API Gateway
5 components
Agent Layer
7 components
ML Layer
6 components
Integration
5 components
Data
5 components
External
5 components
Observability
5 components
Security
6 components
Request Flow - Employee Sentiment Analysis
Automated data flow every hour
End-to-End Data Flow
From employee signal to dashboard insight in <5 seconds
Key Integrations
Slack Integration
Survey Platform (Qualtrics, SurveyMonkey)
HRIS (Workday, BambooHR)
Calendar (Google Calendar, Outlook)
Identity Provider (Okta, Azure AD)
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI outage) | Failover to Claude or Gemini within 30s | Slight latency increase (+500ms), no data loss | 99.9% uptime maintained |
| Sentiment Agent low confidence (<0.7) | Flag for human review, use team baseline as interim | Degraded insight quality, manual queue grows | 95% auto-processed, 5% manual |
| Feature Store unavailable | Use cached features from Redis (up to 1h stale) | Slightly outdated context, minimal accuracy drop | 99.5% with cache |
| PII detection service fails | Block all processing (fail-safe), alert security team | Processing halted until service restored | 100% PII protection (zero tolerance) |
| Database write timeout | Retry with exponential backoff (3x), then queue to DLQ | Eventual consistency, delayed insights | 99.9% write success |
| Slack API rate limit exceeded | Queue messages, process in batch after rate limit resets | Delayed ingestion (up to 1 hour) | 99% within 1h |
| Agent orchestrator crash | Tasks in queue preserved, new orchestrator instance spins up | Processing paused for 2-3 minutes | 99.9% with auto-restart |
┌──────────────┐
│ Planner │ ← Orchestrates all agents
│ Agent │
└──────┬───────┘
│
┌───┴────┬─────────┬──────────┬─────────┐
│ │ │ │ │
┌──▼──┐ ┌─▼──┐ ┌───▼────┐ ┌──▼───┐ ┌─▼────┐
│Guard│ │Exec│ │Sentiment│ │Engage│ │Culture│
│rail │ │utor│ │ Agent │ │Agent │ │Agent │
└──┬──┘ └─┬──┘ └────┬────┘ └──┬───┘ └───┬───┘
│ │ │ │ │
└────────┴──────────┴──────────┴──────────┘
│
┌────▼────┐
│Evaluator│
│ Agent │
└────┬────┘
│
┌────▼────┐
│Dashboard│
└─────────┘🔄Agent Collaboration Flow
🎭Agent Types
Reactive Agent
LowGuardrail Agent - Responds to input (PII check), returns output (redacted text)
Reflexive Agent
MediumSentiment Agent - Uses rules + context (employee history, team baseline)
Deliberative Agent
HighCulture Agent - Plans analysis based on available data (team size, signal volume)
Orchestrator Agent
HighestPlanner Agent - Makes routing decisions, handles loops, coordinates specialized agents
📈Levels of Autonomy
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Multi-Modal Fusion
Tech Stack Summary
2026 Randeep Bhatia. All Rights Reserved.
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