# AI HELP — Project Description

Version: 0.1
Date: 2026-04-21
Owner: Hasan Tareq / CoderGens

## Overview

AI HELP is a national-scale, multi-level assistance platform that uses AI to triage, route, assist, and resolve citizen and organizational requests across government and large enterprises. It supports complex hierarchical roles, strict data governance, and high availability.

## Vision & Goals

- Enable fast, consistent, and auditable assistance for citizens and internal users using AI-assisted workflows.
- Scale to national-level workloads with geographic data residency and multi-tenant isolation.
- Provide transparent AI recommendations with human-in-the-loop controls and full audit trails.

## Scope

- Intake channels: web, mobile, phone routing (via staff), API integrations.
- Services: case management, AI triage & RAG-enabled knowledge retrieval, notifications, analytics, and reporting.
- Integrations: identity providers, legacy systems, national registries, payment gateways (optional).

## Stakeholders

- Central team members
- City/State Administrators
- Thana Operators
- Ward Operators
- Unit Operators
- Field officers and case workers
- Citizens / Service consumers
- Auditors and Compliance teams
- Data scientists and AI ops

## Users & Role Hierarchy (example)

- **Central Admin:** full cross-country visibility, policy control, system config.
- **City/State Admin:** manages city/state settings, user provisioning, and reports.
- **Thana Admin:** operational oversight for thana-level activities.
- **Ward Admin:** manages ward-level users, configs, and resources.
- **Unit Admin:** manages unit-level users and resources.
- **Team Lead / Approver:** approves escalations, monitors KPIs.
- **Field Officer / Case Worker:** executes cases, records outcomes.
- **Support Agent / Dispatcher:** triage and reassign cases.
- **Analyst / Auditor:** read-only with data export and audit capabilities.
- **Citizen / End User:** submit requests, track status, receive resolutions.

Scope-based access: roles should be constrained by geographic scope (Central → City/State → Thana → Ward → Unit) and by functional scope.

## Example Role Matrix (high-level)

- **Role:** Central Admin — **Permissions:** manage-all, view-all, configure
- **Role:** City/State Admin — **Permissions:** view-city/state, manage-city/state-users
- **Role:** Thana Admin — **Permissions:** view-thana, manage-thana-users
- **Role:** Ward Admin — **Permissions:** view-ward, manage-ward-users
- **Role:** Unit Admin — **Permissions:** view-unit, manage-unit-users
- **Role:** Case Worker — **Permissions:** view-assigned, update-case, escalate
- **Role:** Citizen — **Permissions:** create-case, view-own-case

(Implement RBAC + ABAC for attributes like `city`, `thana`, `ward`, `unit`, `case_type`, and `sensitivity`.)

## Key Use Cases

- Citizen submits request → AI classifies & suggests categories → auto-route to appropriate unit → human review → resolution and feedback.
- Agent composes response with AI draft suggestions; supervisor approves final.
- Analytics dashboards for backlog, SLA breaches, AI suggestion accuracy, fairness metrics.
- Mass-notifications for regional announcements or emergency alerts.

## Functional Requirements (high-level)

- FR1: Multi-level RBAC & scoped authorization (RBAC + ABAC).
- FR2: Multi-tenant support with logical isolation per region/org.
- FR3: Case lifecycle management, SLA tracking, escalation rules.
- FR4: AI-powered triage: classification, intent detection, RAG-based answers.
- FR5: Audit logging for all actions and AI decisions with provenance.
- FR6: Secure APIs for system-to-system integrations.
- FR7: Offline-capable mobile workflows for field agents.
- FR8: Role-specific dashboards and exportable reports.

## Non-Functional Requirements

- NFR1: Scale to millions of requests/year; horizontal scaling.
- NFR2: High availability (target 99.95%+), multi-AZ and multi-region failover.
- NFR3: Latency: API p95 < 300ms (core endpoints), AI responses acceptable per use-case.
- NFR4: Data residency per legal/regional constraints.
- NFR5: Strong security: encryption-at-rest/in-transit, KMS, least-privilege.
- NFR6: Observability: metrics, logging, traces, SLOs/SLA dashboards.

## Data & Privacy

- Classify data: public / internal / confidential / highly confidential (PII).
- Apply pseudonymization/anonymization for analytics.
- Data retention policy per data class and legal requirements.
- Maintain consent and opt-out mechanisms for citizens.

## Architecture Overview (high-level)

- API Gateway + WAF
- Auth & IAM layer (SSO: SAML/OIDC, MFA, delegated admin)
- Microservices:
  - User & Org Service
  - Case Management Service
  - AI Service (Embeddings, RAG, LLM connector)
  - Notification Service
  - Audit & Compliance Service
  - Reporting & Analytics
- Data stores:
  - Relational DB (Postgres) for transactional data
  - Vector DB (Pinecone / Milvus / Weaviate) for embeddings
  - Search engine (OpenSearch) for full-text
  - Time-series DB (Prometheus) for metrics
  - Object store (S3) for attachments
- Async layer: Kafka or RabbitMQ for events and background jobs
- UI: `Next.js` for admin and citizen portals; mobile clients (React Native / native)
- Infra: Kubernetes, Terraform, CI/CD pipelines

## AI Design & Model Strategy

- Use embeddings + vector search + RAG for knowledge retrieval.
- Keep LLMs behind an orchestration layer (prompt templates, retrieval augmentation, caching).
- Implement model evaluation: accuracy, safety, hallucination-rate, fairness tests.
- Human-in-loop: require explicit confirm/approval for any action with legal or high-impact outcomes.
- Logging of prompt + response + retrieval trace for provenance.

## Integrations & APIs

- Standard REST / GraphQL endpoints for internal apps.
- Event-based integration (Kafka) for external listeners.
- Connectors for SSO, national registries, payment, and telephony systems.

## Security & Compliance

- Centralized IAM, scoped API tokens, short-lived credentials.
- WAF, rate-limiting, anomaly detection.
- Encryption using KMS.
- Regular security audits, pen-tests, and compliance mapping (GDPR, local laws).
- Data access approvals and automated data access reviews.

## Deployment & Scalability Patterns

- Multi-region active-passive or active-active depending on data residency.
- Database partitioning by region/org and read-replicas for scale.
- Autoscaling for stateless services; use HPA and cluster autoscaler.
- Blue/Green or Canary deploys with traffic shaping for safe rollouts.

## Monitoring & Observability

- Metrics: throughput, latency, error rates, AI suggestion acceptance rates.
- Traces: distributed tracing for end-to-end flow.
- Logs: centralized, indexed logs with retention policies.
- Alerts and on-call rotations with runbooks for common incidents.

## Governance & Ethics

- Create an AI Governance Board for policy, approval, and audit.
- Bias and fairness testing on models; periodic audits.
- Transparent explanations of AI recommendations for users.
- Escalation paths for contested automated decisions.

## Roadmap & Milestones (suggested)

- Phase 0: Requirements, stakeholder workshops, pilot use-case selection (3–6 weeks)
- Phase 1 (MVP): Core intake, case management, basic AI triage, RBAC, pilot region (3 months)
- Phase 2: Expand integrations, vector search, advanced analytics (3 months)
- Phase 3: Multi-region rollout, full governance, national scale (6–12 months)

## Success Metrics

- Time-to-first-response (goal: < 24 hours for given case types)
- Case resolution rate and backlog reduction
- AI triage precision & recall (target > 85% on prioritized categories)
- Uptime and SLO adherence
- Citizen satisfaction (CSAT) scores

## Risks & Mitigations

- Risk: AI hallucination → Mitigation: human-in-loop + provenance + confidence thresholds.
- Risk: Data residency breach → Mitigation: strict region-aware data partitioning, audits.
- Risk: Low adoption by agents → Mitigation: integrate AI helpers into agent workflows, training.

## Next Steps

- Run stakeholder workshop to prioritize 2–3 pilot use-cases.
- Produce detailed data model and full role/permission matrix.
- Prototype AI triage for one use-case and validate with real data.
- Draft implementation plan and CI/CD + infra IaC design.

## Appendix: Example Citizen Workflow

1. Citizen submits request (web/mobile).
2. Ingest service normalizes input and enriches with user profile.
3. AI Triage suggests category, urgency, and SLA.
4. Case created and routed to appropriate region/org queue.
5. Agent receives AI-suggested draft response and either approves or edits.
6. Resolution recorded; citizen notified; case closed; metrics logged.

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Document created as a template. Update sections to reflect policy/regulatory specifics and prioritized use-cases.