Male Club Telegram AI Assistant

1. Vision and Scope

• Provide empathetic, evidence-informed psychological support tailored for male users in Russian- and English-speaking regions.
• Offer guided self-help exercises, psychoeducation, and escalation paths to licensed professionals when needed.
• Maintain strict adherence to ethical, legal, and data protection requirements across jurisdictions.

2. Core Objectives

• Build a Telegram chatbot branded as "Male Club" that delivers context-aware conversations while respecting safety boundaries.
• Enable bilingual interactions (English and Russian) through dynamic language detection and localized content delivery.
• Supply crisis response workflows that promote immediate human support and verified hotlines.
• Implement analytics pipelines to monitor engagement, satisfaction, and model performance for continuous improvement.

3. Product Features (MVP → Growth)

• Onboarding questionnaire to personalize tone, topics, and language preference.
• Modular conversation flows for stress management, emotional regulation, relationships, and career challenges.
• Knowledge base retrieval to inject vetted psychological insights into AI responses.
• Crisis detection system with auto-escalation to human professionals and curated resources.
• Admin portal for psychologists to curate content, review flagged sessions, and tune prompts.
• Optional premium tier for live expert consultations and extended content bundles.

4. Architecture Overview

• Telegram Interface: Bot API via webhook (preferred) with fallback to long polling.
• Conversation Orchestrator: FastAPI (Python) service coordinating dialog state, safety checks, and model calls.
• NLP/LLM Layer: Hosted large language model (OpenAI GPT-4.1 or local Llama 3 variant) with prompt templates, guardrails, and retrieval augments.
• Knowledge Store: PostgreSQL for structured data, Redis for session caching, object storage (S3-compatible) for media, and vector DB (Qdrant/PGVector) for embeddings.
• Safety & Compliance: Content moderation, crisis classifier, legal disclaimers, GDPR/CCPA-ready consent flows.
• Analytics & Monitoring: Event tracking (Segment or self-hosted), dashboards (Metabase/Grafana), alerting (PagerDuty/Telegram ops channel).
• DevOps: Dockerized services, CI/CD (GitHub Actions), IaC (Terraform) targeting AWS/GCP/Selectel depending on data residency needs.

5. Implementation Roadmap

1. Foundations (Week 1-2)
   • Finalize requirements, legal review, and risk matrix.
   • Set up repository, environments, CI/CD skeleton, and coding standards.
2. MVP Core (Week 3-6)
   • Implement Telegram bot shell with bilingual onboarding flows.
   • Integrate LLM gateway with prompt guardrails and logging.
   • Build crisis detection heuristics and hotline directory.
   • Establish persistence layer for user profiles and conversation history (pseudonymized).
3. Enhancements (Week 7-10)
   • Add knowledge base retrieval, weekly content updates, and progress tracking.
   • Launch admin dashboard v1 with content management and session review tools.
   • Introduce premium subscription workflow (Stripe/YooMoney depending on market).
4. Stabilization & Launch (Week 11-12)
   • Conduct localization QA, security tests, and load testing.
   • Run closed beta, gather feedback, iterate on AI prompts and flows.
   • Prepare marketing assets, partnership outreach, and go-live plan.

6. Project Phases

• Phase 0 — Discovery & Compliance: Audience research, requirement elicitation, risk assessment, and legal review for both English- and Russian-speaking regions.
• Recommended follow-up: schedule quarterly user and legal revalidation workshops; refresh personas with new research signals.
• Phase 1 — Technical Foundations: Repository setup, infrastructure provisioning, CI/CD pipelines, coding standards, and telemetry baseline.
• Recommended follow-up: add automated dependency scanning and chaos tests for core infrastructure services.
• Phase 2 — MVP Build: Core Telegram bot flows, bilingual onboarding, LLM integration with safety guardrails, crisis response module, and basic persistence.
• Recommended follow-up: run shadow evaluations with psychology experts and expand knowledge base coverage per priority topics.
• Phase 3 — Beta Expansion: Knowledge base retrieval, personalization logic, admin console v1, premium feature scaffolding, and iterative prompt tuning.
• Recommended follow-up: implement cohort-based experiments, iterate on pricing tests, and harden admin audit logging.
• Phase 4 — Launch Readiness: Localization QA, penetration and load testing, content audits, stakeholder training, and marketing rollout.
• Recommended follow-up: conduct go-live postmortem within two weeks, finalize runbooks for support scaling, and refresh marketing assets quarterly.
• Phase 5 — Operations & Growth: Post-launch monitoring, analytics-driven improvements, periodic model updates, new content campaigns, and partnership integrations.
• Recommended follow-up: review KPI stack monthly, plan semi-annual LLM provider benchmarking, and expand ecosystem partnerships.

7. Team and Responsibilities

• Product Lead: Align roadmap, manage stakeholder expectations, and coordinate releases.
• Psychology Lead: Curate content, validate interventions, oversee ethical standards.
• Engineering Lead: Own backend, integrations, infrastructure, and technical quality.
• AI/NLP Engineer: Design prompt strategies, evaluate model outputs, build safety filters.
• Content Operations: Maintain bilingual knowledge base and campaigns.
• Compliance Officer: Ensure legal adherence, manage privacy policies, and handle user data requests.

8. Risk Management

• Safety: Continuous monitoring of AI outputs, human-in-the-loop review for flagged sessions.
• Legal: Regular audits of consent flows, third-party contracts, and jurisdictional requirements.
• Operational: Disaster recovery, redundancy, and documented incident response procedures.
• Reputational: Transparent communication, clear disclaimers about AI limitations, and responsive support channels.

9. Next Steps

• Define detailed technical specifications for each module.
• Establish data schemas, API contracts, and prompt templates.
• Draft user journeys and UX copy in both languages for the onboarding and first-session experiences.

Repository Map

• docs/ — phase playbooks, data models, safety plans, and generated OpenAPI specs.
• src/ — Python codebase with bot gateway, conversation service, LLM abstractions, and shared utilities.
• pyproject.toml — Poetry configuration with runtime and development dependencies.