Managing COIs (Certificates of Inspection) inside a factory is usually a slow and manual process. QC staff search for customers, check lot numbers, look up QC results, generate Excel files, and manually send reports.

At Simplico, we built a Hybrid AI Assistant powered by Rasa (structured conversational control) + LangChain (agentic reasoning + SQL tools + RAG) to automate these workflows using a simple chat interface.

In this article, we walk through a real end-to-end use case from our Factory Automation POC and show the full system architecture diagram behind it.

🧠 System Architecture (Hybrid Rasa + LangChain)

Before jumping into the story, here is the architecture that powers the COI AI assistant:

                                       ┌─────────────────────────────┐
                                       │         Factory Staff        │
                                       │ (QC, Engineer, Sales, Admin) │
                                       └──────────────┬───────────────┘
                                                      │
                                                      ▼
                                       ┌─────────────────────────────┐
                                       │        Web Chat UI          │
                                       │ (Browser, LINE, Slack, etc.)│
                                       └──────────────┬───────────────┘
                                                      │
                                                      ▼
                                   ┌────────────────────────────────────┐
                                   │           AI Gateway Layer         │
                                   │ Routes messages to Rasa/LangChain  │
                                   └───────┬────────────────────────────┘
                                           │
                        ┌──────────────────┼───────────────────┐
                        │                  │                   │
                        ▼                  ▼                   ▼
             ┌────────────────┐   ┌───────────────────┐   ┌───────────────────────┐
             │  Rasa NLU +    │   │   LangChain RAG   │   │    LangChain Agent     │
             │ Dialogue Core  │   │ Document Search   │   │  Tools + Reasoning     │
             └────────────────┘   │  (Manual, SOPs)   │   └───────────────────────┘
                        │         └───────────────────┘          │
                        │                                        │
                        ▼                                        ▼
          ┌──────────────────────────┐            ┌────────────────────────────────┐
          │ Rasa Custom Actions      │            │ Agent Toolsets                 │
          │ (Python logic, routing)  │            │ - SQL Query Tool               │
          │ - Entity disambiguation  │            │ - COI XLSX Generator Tool      │
          │ - User selection logic   │            │ - Lot Lookup Tool              │
          └───────────────┬──────────┘            │ - Product Data Tool            │
                          │                       └───────────────┬────────────────┘
                          ▼                                       ▼
           ┌────────────────────────────────┐      ┌──────────────────────────────────┐
           │     COI / QC Database          │      │    Document & Vector Store       │
           │ (Customers, Products, Lots,    │      │  (Manuals, SOPs, Embeddings)     │
           │  Test Results, History, etc.)  │      │  FAISS / PGVector                │
           └────────────────────────────────┘      └──────────────────────────────────┘

This hybrid model combines the safety of structured dialog with the intelligence of agentic AI—making it perfect for real factory environments.

🔍 1. Searching Customer Data with Natural Conversation

Instead of browsing menus, the staff simply types:

User:
“Show me data of Toyota”

The AI instantly looks up the COI database.
Since factories often have multiple similar customers, the assistant clarifies:

AI:
“I found multiple customers named ‘Toyota’.
Please choose one:

1. Toyota Motor
2. Toyota Thailand
3. Toyota Industrial Asia
4. Toyota Logistics Service”

This disambiguation is generated by LangChain SQL tools but controlled safely by Rasa dialogue rules.

The user chooses:

User:
“2”

AI:
“You selected Toyota Thailand.
Here is the customer profile:

• Customer Code: C-TH-112
• Total COIs issued: 243
• Active product codes: 18
• Address: Samut Prakan, Thailand”

🧾 2. Generating a COI File

Now the user requests a COI.

User:
“Create COI for lot 123-456A”

AI performs:

• Lot lookup
• QC result check
• Template loading
• XLSX generation
• Upload to COI storage

AI:
“✔ Lot found
✔ QC: Passed
✔ Using template: COI_Template_v3.xlsx
Generating COI…”

After 2 seconds:

AI:
“Your COI is ready:
📄 Toyota_Thailand_COI_123-456A.xlsx
Download: s3://coi/Toyota_Thailand_COI_123-456A.xlsx”

The entire COI workflow—normally 5–10 minutes—finishes instantly.

📦 3. Listing Lots Under a Product Code

User:
“List lot numbers for product code P-9981”

AI:
“Here are the lots for P-9981:

• 123-456A — Completed
• 123-456B — Awaiting QC
• 123-457A — Completed
• 123-458C — Testing
• 123-460A — Completed (Pending COI)

Would you like to see any lot?”

User:
“Okay”

AI:
“Please choose 1–5.”

🏭 4. Inspecting Lot Data

User:
“1”

The AI returns a full QC + production summary:

Product

• Code: P-9981
• Name: Aluminum Housing

QC Results

• Hardness: 88.5 HRC
• Chemical Composition: OK
• Dimension: Passed
• Visual: No defects

Production

• Machine: CNC Line #2
• Date: 2025-01-14
• Operator Team: B
• Cycle Time: 2.8 minutes

Then the AI asks:

“Would you like to generate the COI, export to Excel, or compare with other lots?”

📘 5. Retrieving SOPs Through RAG

User:
“Show SOP for this product.”

AI (via RAG search):
“Here are the SOP steps for P-9981:

1. Visual inspection
2. Measure dimensions (±0.05 mm)
3. Hardness test (85–90 HRC)
4. XRF analysis
5. COI preparation”

Everything comes directly from the factory’s SOP documents using vector embeddings + LangChain.

📊 6. Summarizing COI History

User:
“Summarize COI history for Toyota Thailand.”

AI:

COI Summary – Last 90 Days

• Total COIs: 62
• QC Pass Rate: 98.3%
• Delayed COIs: 2
• Main product: P-9981

“Would you like this as PDF, Excel, or email?”

🚀 Final Thoughts

This use case shows how a Hybrid Rasa + LangChain AI Assistant can transform COI workflows:

• Natural conversation instead of complex UI
• Automatic COI generation
• Fast lot lookup
• Instant QC insights
• One-click SOP retrieval
• Summary reports on demand

It’s not just a chatbot—it’s an intelligent bridge connecting people, data, QC history, manuals, and COI documents into one seamless experience.

This POC proves that AI can save time, reduce errors, and unlock new productivity for factories of any size.