# Enterprise ERP Specification - Phased Roadmap & AI Integrations

This document defines the implementation phases, infrastructure scalability plans, and the rollout of artificial intelligence (AI) models.

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## 1. Implementation Roadmap & Scalability Phasing

To ensure operational stability and mitigate risks, the system rollout is structured across three distinct organizational growth phases.

```mermaid
gantt
    title ERP System Implementation Timeline
    dateFormat  YYYY-MM
    section Phase 1 (10-100 staff)
    Core Database & Schema Setups       :active, p1_1, 2026-06, 2026-08
    HRMS, basic PMO & Finance modules   :active, p1_2, 2026-07, 2026-10
    Monolithic Deploy & Validation      :p1_3, 2026-09, 2026-12
    section Phase 2 (100-1000 staff)
    Microservice Decoupling             :p2_1, 2027-01, 2027-04
    CRM, Sprints & Procurement Integrations :p2_2, 2027-03, 2027-08
    Read Replicas & CDN configs         :p2_3, 2027-06, 2027-10
    section Phase 3 (1000-10000 staff)
    Multi-Region Kubernetes Deployments  :p3_1, 2027-11, 2028-03
    Geofencing & Biometric Clock-ins     :p3_2, 2028-02, 2028-06
    AI Forecasting & OCR Engines        :p3_3, 2028-04, 2028-10
```

### Phase 1: Foundation (10 - 100 Employees)
*Goal: Provide immediate operational structure and central data repository.*
- **Architecture**: Monolithic deployment of services (packaged in separate folders, run on a single Docker node). Single PostgreSQL database instance with automated daily S3 snapshots.
- **Modules**: Employee Profiles, Leaves, Manual Attendance, Core Task Management, Client directory, and manual Invoicing.
- **Rollout**: Direct deployment to hosting. Data seeded from legacy spreadsheets.

### Phase 2: Scale (100 - 1,000 Employees)
*Goal: Decouple operational workloads and support rapid head-count expansion.*
- **Architecture**: Move from monolith to decoupled microservices on Kubernetes (EKS). Introduce **Kong API Gateway** and **Apache Kafka** for event-driven async messaging. PostgreSQL master-slave configuration with automated read-replicas.
- **Modules**: Agile Sprints, CRM Sales funnel, Automations, Procurement PR/PO chains, Asset rosters.
- **Rollout**: Pilot program in the Engineering department (3 months), followed by finance and HR migrations.

### Phase 3: Enterprise (1,000 - 10,000+ Employees)
*Goal: Secure MNC-level redundancy, compliance, and automated monitoring.*
- **Architecture**: Multi-region Kubernetes clusters with latency-based DNS routing. ClickHouse database integration for real-time OLAP dashboards.
- **Modules**: Geofenced biometric check-ins, automated cash flow forecasting, multi-currency invoicing, compliance audit trails.
- **Rollout**: Parallel execution of monolithic failover checks, automated validation routines, and SRE team handoff.

---

## 2. Future Artificial Intelligence (AI) Integrations

The system's database schema includes fields designed to support AI automation, matching our microservices boundaries.

### A. Intelligent Candidate Screening (Recruitment Domain)
- **Feature**: Automatically extract skills, experience levels, and qualifications from uploaded PDF resumes.
- **Mechanism**: LLM embeddings parse resumes against the job scorecard details. It scores candidates from 1-100, ranking them in the "Applied" column to reduce HR screening workloads.
- **Schema Field**: `candidates.resume_text` (stores parsed plain text and embeddings vector JSON).

### B. Automated Invoice OCR & Expense Auditing (Finance Domain)
- **Feature**: Employees take photos of expense receipts; the system extracts the vendor, amount, tax, and date automatically.
- **Mechanism**: Vision LLMs or OCR networks extract line items and auto-populate the expense form. It compares values with the company spending policy, flag discrepancies, and auto-checks for potential duplicate submissions.
- **Schema Field**: `expenses.receipt_url` (monitored by OCR workers).

### C. Sprint Delay Prediction Engine (PMO Domain)
- **Feature**: Predicts if active sprints will miss committed milestone dates.
- **Mechanism**: Recurrent neural networks (or regression trees) evaluate historic developer velocity, task complexity, daily standup check-in compliance, and task timer values.
- **Action**: Raises a red dashboard flag to the Project Manager if completion likelihood drops below 80%.

### D. Automated Standup & Roadblock Matcher (Operations Domain)
- **Feature**: Analyze daily morning roadmaps vs evening completion logs.
- **Mechanism**: NLP models compare daily standup notes to identify blocks (e.g. "blocked by API issue"). It automatically groups team dependencies and notifies the Team Lead to resolve blocks.