From prompts to production feedback intelligence.
Monday: 3 core prompts for feedback analysis. Tuesday: automated agent code. Wednesday: team workflows. Thursday: complete technical architecture. Today we show the full system: agent orchestration, ML pipelines, multi-tenant scaling, security layers, and evolution from startup to enterprise deployment.
Key Assumptions
System Requirements
Functional
- Ingest feedback from 15+ sources with unified schema
- Extract features, sentiment, themes, and user intent via ML
- Deduplicate similar feedback across sources and time
- Prioritize features based on impact, frequency, and strategic fit
- Generate weekly insights reports with trend analysis
- Support custom taxonomies per tenant (enterprise)
- Real-time alerts for critical feedback (P0/P1 bugs, churn signals)
Non-Functional (SLOs)
💰 Cost Targets: {"per_feedback_item_usd":0.02,"per_tenant_monthly_usd":150,"ml_cost_percent_of_revenue":8}
Agent Layer
planner
L4Decomposes feedback processing into subtasks and selects appropriate tools
🔧 task_decomposer, tool_selector, dependency_resolver
⚡ Recovery: Retry with simplified plan (skip optional steps), Route to manual review queue if decomposition fails, Log planning failures for model retraining
executor
L3Executes the planned workflow: feature extraction, deduplication, prioritization
🔧 llm_extractor, vector_search, priority_model, feature_mapper
⚡ Recovery: Retry failed steps up to 3x with exponential backoff, Use cached embeddings if vector search fails, Fallback to rule-based prioritization if ML model unavailable, Queue for manual review if extraction confidence < 0.7
evaluator
L3Validates output quality, checks for hallucinations, ensures schema compliance
🔧 schema_validator, hallucination_detector, quality_scorer, consistency_checker
⚡ Recovery: Flag low-quality outputs for human review, Request re-execution with stricter parameters, Log evaluation failures for threshold tuning
guardrail
L4Enforces safety policies: PII redaction, profanity filtering, content moderation
🔧 pii_detector, profanity_filter, content_moderator, compliance_checker
⚡ Recovery: Block processing if PII detection fails (fail-safe), Use conservative redaction if uncertain, Alert security team for high-risk content, Quarantine feedback if moderation service unavailable
deduplication
L3Identifies and clusters duplicate or similar feedback across sources and time
🔧 vector_search, clustering_algorithm, semantic_similarity_model, merge_logic
⚡ Recovery: Use exact text matching if embedding search fails, Create new cluster if no matches above threshold, Log clustering failures for model retraining
prioritization
L3Scores and ranks features based on impact, frequency, strategic fit, and urgency
🔧 priority_model, impact_estimator, roadmap_aligner, explanation_generator
⚡ Recovery: Fallback to rule-based scoring if ML model unavailable, Use historical priority data for cold-start features, Flag for PM review if conflicting signals
ML Layer
Feature Store
Update: Real-time for new feedback, batch daily for aggregates
- • feedback_sentiment (pos/neg/neutral)
- • theme_category (bug/feature/ux/performance)
- • user_intent (request/complaint/praise/question)
- • urgency_score (0-100)
- • user_segment (enterprise/smb/trial)
- • source_type (in-app/email/slack/ticket)
- • text_embedding (1536d)
- • historical_frequency (30d/90d)
- • similar_feedback_count
- • user_churn_risk
Model Registry
Strategy: Semantic versioning with A/B testing for major updates
- • sentiment_classifier
- • theme_extractor
- • priority_model
- • deduplication_model
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ LLM cost explosion (unpredictable token usage)
High✓ Mitigation: Token budgets per request (max 4K tokens). Cache embeddings. Use cheaper models for non-critical tasks (GPT-4o-mini for theme extraction). Monitor cost per feedback item, alert if > $0.03. Quarterly cost optimization reviews.
⚠️ Model drift (accuracy degrades as product evolves)
Medium✓ Mitigation: Weekly model performance monitoring. Automated retraining triggered when accuracy drops > 5%. Monthly human review of 100 errors. RAG knowledge base updated daily with new product docs.
⚠️ PII leakage (customer data sent to LLM)
Low✓ Mitigation: Guardrail Agent blocks processing if PII detection fails (fail-safe). All feedback redacted before LLM call. Audit logs for all PII access. Quarterly security audits. Encrypted storage (KMS).
⚠️ Vendor lock-in (OpenAI/Anthropic dependency)
Medium✓ Mitigation: Multi-LLM strategy (GPT + Claude + fine-tuned models). Abstract LLM calls behind interface. Test failover quarterly. Maintain fine-tuned models as backup (can run on-prem).
⚠️ Scaling bottlenecks (vector DB, database)
Medium✓ Mitigation: Load testing at 2x expected volume. Auto-scaling for compute. Read replicas for database. Vector DB sharding. Caching layer (Redis). Quarterly capacity planning reviews.
⚠️ Data quality issues (garbage in, garbage out)
High✓ Mitigation: Input validation (schema checks, length limits). Confidence thresholds (< 0.7 = flag). Human review queue for low-confidence outputs. Feedback on feedback (users rate AI quality). Monthly data quality audits.
⚠️ Hallucinations (LLM invents features, quotes)
Medium✓ Mitigation: 5-layer hallucination detection (confidence, cross-reference, fact-checking, statistical validation, human review). Evaluator Agent validates all outputs. Monthly hallucination rate audits (target < 1%).
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 presentation to security
Presentation
4 components
API Gateway
4 components
Agent Layer
6 components
ML Layer
6 components
Integration
5 components
Data
4 components
External
5 components
Observability
5 components
Security
5 components
Sequence Diagram - Feedback Processing Flow
Automated data flow every hour
Data Flow
Feedback submission → insights in 2.6 seconds
Key Integrations
Intercom / Zendesk
Slack
Jira
In-App Widget
Email Parser
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI/Anthropic outage) | Switch to backup LLM (Claude ↔ GPT), queue if all down | Degraded (slower processing), not broken | 99.5% |
| Feature extraction low confidence (<0.7) | Flag for human review, use rule-based fallback for critical fields | Quality maintained, slight delay | 99.9% |
| Vector DB unavailable (Pinecone/Weaviate outage) | Skip deduplication, use exact text matching, create new clusters | More duplicates created, eventual consistency | 99.0% |
| PII detection service fails | Block processing (fail-safe), queue for manual redaction | Processing halted for safety | 100% (no PII leaks) |
| Database write timeout | Retry 3x with exponential backoff, write to S3 as backup | Eventual consistency, slight delay | 99.9% |
| Tenant quota exceeded (rate limit) | Queue excess requests, notify tenant admin | Delayed processing for tenant | Per-tenant SLA |
| Model serving latency spike (>5s p95) | Route to faster model (GPT-4 → GPT-4o-mini), scale workers | Slightly lower accuracy, maintained latency | 99.5% |
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Model Drift Detection
Tech Stack Summary
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