Pfes-011 Jun 2026

| In‑Scope | Out‑of‑Scope | |----------|--------------| | • Real‑time ingestion of feedback from all existing PFES channels. • Streaming sentiment scoring (English + 5 additional languages). • Live dashboard with trend graphs, heat‑maps and drill‑down tables. • Configurable alert rules (threshold, time‑window, channel, location). • Integration with existing incident‑management system (e.g., ServiceNow). • Auditing & logging for GDPR/PHI compliance. | • Building a new feedback collection UI (existing PFES UI remains unchanged). • Deep‑learning model training pipeline (use pre‑trained model + fine‑tune offline). • Voice‑to‑text transcription (will be covered in PFES‑012). • Predictive “next‑issue” forecasting (future roadmap). |

In the vast and multifaceted landscape of Japanese adult video (AV) entertainment, certain release codes transcend their status as mere catalog numbers to become touchstones for fans and collectors. The code is one such identifier. For enthusiasts of the genre, and specifically for fans of the studio Premium and the legendary actress Tsubasa Amami, this alphanumeric string represents a specific moment in time—a showcase of performance, aesthetic, and production quality that defines the golden age of AV. PFES-011

| FR ID | Description | Priority | |-------|-------------|----------| | | Real‑time ingestion : All feedback events from existing sources (Kiosk, Web, Mobile, Email, SMS, Social) must be published to a Kafka topic within ≤ 2 seconds of receipt. | Must | | FR‑2 | Streaming sentiment scoring : Each event must be enriched with a sentiment score (range –1.0 to +1.0) using the hybrid model (rule‑based + transformer). Latency per event ≤ 150 ms. | Must | | FR‑3 | Language detection & routing : Auto‑detect language (English, Spanish, Mandarin, Arabic, French, German) and apply the appropriate language‑specific model. | Should | | FR‑4 | Dashboard UI : Provide a single‑page web dashboard with: • Global sentiment line chart (rolling 24 h). • Heat‑map by clinic location & service line. • Table of “Top 10” most negative feedback items (with raw text). • Configurable time window selector (5 min, 15 min, 1 h, 24 h). | Must | | FR‑5 | Alert engine : Allow users to define threshold‑based alerts (percentage negative, absolute count, rate of change). Alerts must be sent via: • Email • Slack webhook • ServiceNow incident creation. | Must | | FR‑6 | Config UI : A “Rules & Settings” page where users can: • Add/edit/delete alerts. • Choose model version per language. • Set retention period for raw feedback (default 30 days). | Should | | FR‑7 | Audit logging : Every scoring event must be recorded in an append‑only immutable log (e.g., AWS QLDB or Azure Confidential Ledger) with fields: event‑id, timestamp, model‑version, language, sentiment‑score, raw‑text hash, user‑id (if applicable). | Must | | FR‑8 | Data retention & purge : Raw feedback text must be automatically deleted after the configured retention period, while aggregated sentiment statistics remain. | Must | | FR‑9 | Model versioning : The system must expose an API endpoint to query the active model version for each language and support hot‑swap without downtime. | Should | | FR‑10 | Metrics exposure : Export Prometheus metrics for: • Ingestion lag (Kafka offset lag). • Scoring latency (p95). • Alert firing count. | Must | | FR‑11 | Fail‑over handling : If the sentiment service is unavailable, events must be persisted to a dead‑letter queue and re‑processed once the service recovers. | Must | | FR‑12 | Accessibility : All UI components must meet WCAG 2.2 AA standards (color contrast, keyboard navigation, ARIA labels). | Must | | FR‑13 | Internationalisation : UI strings must be externalised; initial localisation for English, Spanish, French. | Should | | FR‑14 | Performance : Dashboard page load ≤ 3 seconds on a 3G connection, ≤ 1 second on broadband. | Should | | FR‑15 | Security : All traffic must be TLS 1.3, JWT‑based authentication with role‑based access control (RBAC). PHI must never be logged in plaintext. | Must | | • Building a new feedback collection UI