⭐ ERP + Generative AI + BI — Project Case Study
URL: erp.LinhTruong.com
🚀 Overview
This project is a unified ERP + Generative AI + Business Intelligence platform that centralizes core business operations (orders, inventory, customers, finance), augments workflows with AI copilots, and exposes real‑time analytics through interactive dashboards.
It’s designed as a modular, API‑driven system that can support small to mid‑size businesses needing serious operational visibility without a full SAP/Oracle deployment—while showcasing how LLMs can sit on top of structured ERP data to power natural‑language insights and automation.
👨💻 My Role
- Architecture:
Overall platform design (ERP modules, AI layer, BI layer, data warehouse) - Backend:
Services for orders, inventory, customers, invoices, and reporting - Data & BI:
ETL/ELT pipelines, warehouse schema, metrics modeling, dashboards - AI/ML:
Generative AI layer for natural‑language queries, summaries, and copilots - Frontend:
Admin UI, operational dashboards, analytics views - DevOps:
Containerization, deployment, environment configuration, monitoring
🧩 Problem & Constraints
The platform aims to solve a common mid‑market problem:
- Data is fragmented across tools (spreadsheets, SaaS apps, ad‑hoc reports)
- Leaders can’t get real‑time, trustworthy views of operations
- Existing ERPs are either too heavy, too rigid, or too expensive
- BI is often an afterthought, not integrated into daily workflows
- AI is bolted on as a gimmick instead of being grounded in ERP data
Constraints:
- Must model core ERP domains (orders, inventory, customers, invoices)
- Must provide clean, queryable data for BI and AI
- Must support natural‑language questions like “What were last month’s top customers by revenue?”
- Must be modular enough to extend with new modules and reports
🏗️ 1. System Architecture Diagram
┌───────────────────────────────┬───────────────────────────────┬───────────────────────────────┐
│ Frontend │ API Gateway Layer │ BI Dashboards │
│ (ERP UI + Analytics UI) │ (Auth, Routing, Throttles) │ (KPIs, Charts, Tables) │
└───────────────┬───────────────┴───────────────┬───────────────┴───────────────┬───────────────┘
│ │ │
▼ ▼ ▼
┌──────────────────────────┐ ┌──────────────────────────┐ ┌──────────────────────────┐
│ ERP Services │ │ Data Pipeline / ETL │ │ Data Warehouse / DB │
│ (Orders, Inventory, │ │ (Ingest, Transform) │ │ (Star/Snowflake Schemas) │
│ Customers, Invoices) │ └───────────────┬──────────┘ └───────────────┬──────────┘
└───────────────┬──────────┘ │ │
│ ▼ │
└───────────────┬──────────┐ ┌──────────────────────────┐ │
▼ │ │ Metrics / Semantic Layer│ │
┌──────────────────┐ │ │ (Business Metrics, Models)│ │
│ AI Query Layer │─┘ └──────────────────────────┘ │
│ (NL → SQL/Logic) │ │
└─────────┬────────┘ │
▼ ▼
┌──────────────────────────┐ ┌──────────────────────────┐
│ LLM Layer │ │ BI Engine │
│ (Summaries, Copilots) │ │ (Dashboards, Reports) │
└──────────────────────────┘ └──────────────────────────┘
🔄 2. Data Flow Diagram
┌──────────────────────────┐
│ 1. ERP Operations │
│ (Orders, Inventory, etc.)│
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 2. ERP Services Persist │
│ to Operational DB │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 3. ETL/ELT Pipelines │
│ Extract & Transform │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 4. Data Warehouse │
│ (Modeled Tables) │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 5. Metrics / Semantic │
│ Layer (KPIs, Logic) │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 6. BI Engine │
│ (Dashboards, Reports) │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 7. AI Query Layer │
│ (NL → Queries) │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 8. LLM Summaries & │
│ Explanations │
└──────────────────────────┘
🔁 3. Workflow Diagram (AI‑Augmented Analytics)
┌──────────────────────────┐
│ 1. User Opens ERP/BI UI │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 2. User Asks Question │
│ (e.g., “Top customers…”) │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 3. AI Query Layer Parses │
│ Intent & Entities │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 4. Generate Query Against│
│ Metrics/Warehouse │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 5. BI Engine Executes │
│ & Returns Data │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 6. LLM Layer Summarizes │
│ & Explains Results │
└───────────────┬──────────┘
▼
┌──────────────────────────┐
│ 7. UI Shows Charts + │
│ Narrative Insight │
└──────────────────────────┘
🤖 4. “ERP + AI + BI” Coordination Diagram
┌──────────────────────────┐
│ ERP Core Services │
│ (Orders, Inventory, etc.)│
└───────────────┬──────────┘
│
▼
┌──────────────────────────┐
│ Data Warehouse │
│ (Clean, Modeled Data) │
└───────────────┬──────────┘
│
┌───────┴───────────────────────────────┐
▼ ▼
┌──────────────────────────┐ ┌──────────────────────────┐
│ Metrics / Semantic Layer │ │ AI Query Layer │
│ (Business KPIs, Logic) │ │ (NL → Metric Queries) │
└───────────────┬──────────┘ └───────────────┬──────────┘
│ │
▼ ▼
┌──────────────────────────┐ ┌──────────────────────────┐
│ BI Engine │ │ LLM Layer │
│ (Dashboards, Reports) │ │ (Summaries, Copilots) │
└───────────────┬──────────┘ └───────────────┬──────────┘
│ │
└───────────────┬──────────────────────┘
▼
┌──────────────────────────┐
│ Unified ERP+AI UI │
│ (Charts + Narratives) │
└──────────────────────────┘
🎨 UX & Design Rationale
- Single pane of glass
: operations + analytics + AI in one interface - Metric‑first design
: KPIs and trends are primary, tables secondary - Natural‑language entry point
: users can type questions instead of hunting for reports - Side‑by‑side charts + narrative
: visual plus explanation for decision‑makers - Consistent information hierarchy
: company‑level → segment‑level → record‑level drill‑downs
🔬 Technical Deep Dive
ERP Services
- Modular services for:
- Orders (status, amounts, customers)
- Inventory (stock levels, locations)
- Customers (segments, lifetime value)
- Invoices/Payments (AR, aging)
- REST/JSON APIs with authentication and validation
Data Pipeline & Warehouse
- Periodic or near‑real‑time sync from ERP DB to warehouse
- Modeled schemas (fact tables + dimensions) for:
- Orders, customers, products, inventory, invoices
- Designed for BI friendliness (star/snowflake patterns)
Metrics / Semantic Layer
- Defines business metrics like:
- MRR, revenue, AR aging, inventory turnover, top customers
- Encodes logic so both BI and AI layers use the same definitions
AI Query Layer
- Maps natural‑language questions to:
- Metrics
- Dimensions (time, region, customer segment)
- Generates safe, constrained queries against the metrics layer
- Prevents free‑form SQL injection or hallucinated tables
LLM Layer
- Takes structured query results and:
- Summarizes trends
- Highlights anomalies
- Suggests follow‑up questions
- Always grounded in actual BI data, not synthetic numbers
📈 Results & Impact
- Turns ERP from a data entry system into a decision‑support platform
- Gives leaders real‑time visibility into revenue, operations, and risk
- Reduces dependency on ad‑hoc spreadsheet reporting
- Demonstrates how Generative AI + BI + ERP can work together coherently
- Provides a strong, enterprise‑grade anchor project in your portfolio
🔗 Live Demo
erp.LinhTruong.com