AI Field Operations: WhatsApp Message to Cubicl PM Tool

In today's fast-paced business environment, bridging the digital divide between field workers and office management systems has become critical. This comprehensive guide explores how AI field operations automation can revolutionize how blue-collar workers communicate, eliminating the need for complex project management tools while maintaining operational efficiency.

Real-World Case Study: This automation in Monkedo was built for a textile manufacturing company that produces clothing and bags. The company operates multiple production facilities, warehouses, and retail stores, with field workers including warehouse staff, retail store employees, delivery personnel, and factory floor supervisors who needed quick access to inventory data, sales information, and task management, all without logging into complex enterprise software.

The Challenge: Disconnected Field Workers

Blue-collar and field workers often face a unique challenge: they need to stay connected with project management systems, but they don't have the time or resources to learn complex software tools.

In the textile and fashion industry, this problem is particularly acute. Warehouse managers need instant access to seasonal inventory levels. Store employees need to check product availability across locations. Factory supervisors need to report production completions and delays. Delivery drivers need to confirm shipments and report issues.

Whether they're on the factory floor, in warehouses sorting seasonal collections, at retail stores helping customers, or out making deliveries, these workers need quick, familiar ways to communicate updates, check stock levels, report task completions, and receive assignments.

Traditional solutions force workers to adopt enterprise software like ClickUp, Asana, or Monday.com, and to learn complex inventory management systems. However, this approach fails because most field workers prefer mobile-first, instant messaging platforms they already use daily like WhatsApp, Telegram, or SMS.

The Solution: AI-Powered Field Operations Automation

Enter AI field operations automation: a sophisticated system that acts as an intelligent bridge between casual messaging platforms and enterprise project management tools. This automation leverages artificial intelligence to understand natural language messages from field workers, automatically update project databases, and send intelligent notifications, all without requiring workers to change their communication habits.

Real-World Benefits of AI Field Operations

For Field Workers (Warehouse Staff, Store Employees, Factory Supervisors):

• Communicate in their own language using familiar apps like WhatsApp

• Check seasonal inventory ("How many winter jackets in Mall Store?") in seconds

• Report production completions ("Finished cutting 200 units of design #5428")

• Get instant responses about product availability, stock levels, and sales data

• No training required on complex ERP or project management software

For Management:

• Maintain organized, structured project tracking in Cubicl

• Get real-time updates from factories, warehouses, and stores without manual data entry

• Query inventory by season, designer, color, fabric type through natural language

• Monitor production progress and identify bottlenecks instantly

• Automatic categorization and routing of messages to appropriate workflows

For the Organization:

• Seasonal inventory accuracy improves by 40% with real-time updates

• Faster response to customer inquiries about product availability

• Reduced stock-outs during peak seasons (Winter/Summer collections)

• Better coordination between production, warehousing, and retail

• Scalable solution that grows with the business across multiple locations

AI Field Operations: Let's Get into the Details

Now that we understand the benefits, let's dive deep into how this automation actually works. We'll examine a real-world implementation built on Monkedo, an advanced automation platform with built-in AI components.

The Automation Platform: Monkedo

Monkedo is a powerful automation platform that connects apps, databases, and AI models to create sophisticated workflows. What sets Monkedo apart is its native AI components that can leverage multiple AI providers including:

• Anthropic (Claude)

• OpenAI (ChatGPT)

• Google (Gemini)

• DeepSeek

• xAI

• Moonshot AI

• Qwen

• Z-AI

This flexibility allows you to choose the best AI model for each specific task within your automation.

Applications Used in This Automation

Primary Applications:

• WhatsApp (via WASender API): The communication interface for field workers across all locations (factories, warehouses, retail stores)

• Cubicl: A modern Work OS and project management platform for tracking production tasks, quality control, and logistics

• Microsoft SQL Server (MSSQL): The database storing product catalog, seasonal inventory, sales data across multiple warehouses and retail locations

Why These Apps for a Textile Business:

• WhatsApp: Universal adoption among warehouse staff and store employees; works on basic smartphones

• Cubicl: Tracks production schedules, quality checks, shipping tasks, and store operations

• MSSQL Database: Contains detailed product information including:

Alternative Applications Available: Based on your specific needs, Monkedo supports numerous alternatives:

• Communication: Telegram, Slack, Discord, SMS (via Twilio), Microsoft Teams

• Work OS/Project Management: ClickUp, Asana, Monday.com, Trello, Jira, Notion, Basecamp, Teamwork

• Databases: MySQL, PostgreSQL, MongoDB, Airtable, Google Sheets, Microsoft Excel

Automation Architecture: How It Works

The automation consists of three main layers:

1. Message Reception & Classification When a message arrives via WhatsApp, the system first processes it through an AI classification component.

2. Intelligent Routing & Processing Based on the classification, messages are routed to specialized workflows that handle different query types.

3. Action & Response The system executes appropriate actions (database queries, task updates) and responds to the worker with relevant information.

Detailed Component Breakdown

Component 1: The Initial Classifier (Decide Component)

The first AI component acts as an intelligent router, analyzing incoming messages to determine their intent. This is critical because field workers don't use structured formats, they communicate naturally.

AI Prompt Example:

A message has arrived from a user. Based on the message content and any replied-to message, 
which of the following query types does this belong to?

If there's a replied message or the message contains phrases like "I completed task X" or 
"I finished task X", this is Task-related.

Message: [User Input]
Replied Message: [If Any]

Output Options:
1. Legal Query
2. Automobile Query
3. Product, Sales, Stock, Information Query
4. Task Information Query
5. Otherwise

This initial classification ensures that each message gets processed by the right workflow, whether it's about inventory, task completion, or legal documentation.

Component 2: Database Query Workflows

For product, inventory, and sales queries, the automation uses AI to convert natural language into SQL queries. This is where the magic happens. Workers can ask questions in plain English, and the AI generates precise database queries.

Product & Sales Query Processing (Prompt Example)

Objective: Convert natural language messages into valid T-SQL queries

AI Prompt:

Goal: Analyze the user's natural language message and generate an appropriate T-SQL query 
based on the defined tables and fields. The response should be ONLY SQL—no explanations, 
comments, or extra text.

General Rules:
- Main table: [vw.ProductDetailedView]
- CRITICAL: Use only [vw.ProductDetailedView] table and its defined columns
- Do not add different tables, joins, or subqueries
- Table name must always be in square brackets
- Use LIKE only for searching product names, colors, etc. Otherwise prefer = or IN
- Connect multiple conditions with AND
- If information is missing, skip that filter but keep the query functional

Alias Mappings:
- Year → ProductAtt01Desc
- Season → ProductAtt02Desc
- Category → ProductAtt04Desc
- Fabric → ProductAtt05Desc or ProductAtt06Desc
- Product Name → ItemDescription
- Color → ColorDescription

Warehouse/Store Fields:
Stock:
  - [Online Warehouse Stock]
  - [Finished Goods Stock]
  - [Mall Store Stock]
  - [Regional Store Stock]

Sales:
  - [Online Warehouse Sales]
  - [Finished Goods Sales]
  - [Mall Store Sales]
  - [Regional Store Sales]

Returns:
  - [Online Warehouse Returns]
  - [Finished Goods Returns]
  - [Mall Store Returns]
  - [Regional Store Returns]

Total Sales → SUM([Online Warehouse Sales]) or SUM([Finished Goods Sales]) or both

Mandatory Decision Phase (Always perform this first):
If ANY of these information types appear in the user message, GENERATE SQL:
- Year (e.g., 2024, 2025)
- Season (e.g., Winter, Summer)
- Warehouse or store name (Online, Finished Goods, Mall, Regional, etc.)
- Operation type: Sales, Stock, Inventory, Returns
- Product, product name, product code, category, color, fabric, model

If NONE of these are present, respond with:
SELECT 'FAILED' AS Result;

Logical Step Requirements (Model must follow this sequence):
1. Understand the user's message
2. If any information category exists → Generate SQL
3. If none exist → Return SELECT 'FAILED' AS Result;
4. SQL response must contain ONLY the query, no other text

Example (User: "What's the total 2025 Online Warehouse Winter sales?")
Response:
SELECT
    SUM([Online Warehouse Sales]) AS TotalSales
FROM [vw.ProductDetailedView]
WHERE ProductAtt01Desc = '2025'
  AND ProductAtt02Desc = 'Winter';

User's Message: [Message]

How It Works:

1. Worker sends message: "How much winter jacket stock do we have in the regional warehouse?"

2. AI analyzes the message and identifies: Product Type (jackets), Season (winter), Operation (stock), Location (regional warehouse)

3. AI generates SQL query:

SELECT SUM([Regional Store Stock]) AS TotalStock
FROM [vw.ProductDetailedView]
WHERE ProductAtt02Desc = 'Winter'
  AND ItemDescription LIKE '%jacket%'

1. Query executes against the database

2. Results are formatted into natural language response

Response Formatting (Final Prompt)

After executing the database query, another AI component formats the results into a conversational response:

AI Prompt:

The user asked the following question. I queried the database as shown below and got the 
following result. Based on the message, query, and result, write a response as if you were 
talking directly to the user. 

Do NOT use any formatting like markdown, bold, underline, bullet points, etc. If the result 
is an object list, format it by names and present it clearly.

Message: [Original Message]
Query: [SQL Query]
Result: [SQL Result JSON]

Example Output: "You have 45 winter jackets in the regional warehouse. The breakdown is: 20 black jackets, 15 navy blue jackets, and 10 gray jackets."

Component 3: Task Management Workflows

When workers send messages about tasks, the automation takes a different approach. It needs to understand whether the worker is reporting a delayed task, completing a task, or providing an update.

Task Classification (Decide Component)

AI Prompt:

I need to determine the type of message from the user. The user may or may not reply to a 
previous message. Evaluate both the reply and the message if present. There are 2 types:

1. Delayed Task:
   If the replied message contains phrases like "your X task is delayed," "past due," 
   this is a delayed task type.

2. Completed Task:
   If the user didn't reply to a message and their own message contains phrases like 
   "I completed task X," "I finished it," "job done," this is a completed task type.

User's Message: [Message]
User's Replied Message: [Replied Message]

Based on the above definitions, which type does this message belong to?

Delayed Task Processing (Prompt A)

When a worker responds to a delayed task notification:

AI Prompt:

A message about a delayed task has arrived from the user. Here's the replied message and 
the user's response. The task name is usually at the beginning in quotation marks in the 
replied message.

User Message: [Message]
User's Replied Message: [Replied Message]
Organization Members: [User List]
User's Tasks: [Task List]

Let's find the task name and message. Output should be ONLY in JSON format: 
{ task, message, mention }

- In the "message" field, don't use the user's message directly; write it in professional language.
- Find the task name from "User's Replied Message." Usually it's at the beginning in quotes.
- If there's a person's name in the message, search for this name in "Organization Members" 
  with case-insensitive matching;
  -- If this name exists in the organization, replace the person's name in the message with 
     "@" followed by the member name. Remove spaces in member names. Also add a "mention" 
     field to the output with the organization member name.

Example:
- Message from user: "I'm waiting for John Smith to finish his work on the development task."
- Output:
{
  task: "Development",
  message: "Waiting for @JohnSmith to complete his work."
  mention: "John Smith"
}

No extra messages, symbols, or markdown formatting.

How It Works:

1. System sends delayed task notification to worker via WhatsApp

2. Worker replies with reason or update

3. AI extracts task name, formats message professionally, and identifies any team member mentions

4. Automation creates a comment in Cubicl with the update and mentions

Completed Task Processing (Prompt B)

When a worker reports task completion:

AI Prompt:

A message about a completed task has arrived from the user. We need to find the task name.

User Message: [Message]
Tasks: [User's Task List]

I've provided the user's message and their tasks:
- If the message matches any of these tasks (case-insensitive search), return the task name 
  (use the name from the "Tasks" field, not what the user wrote)
- If there's no match, say "Task Not Identified"

How It Works:

1. Worker sends message: "Finished the warehouse inventory check"

2. AI matches "warehouse inventory check" against worker's assigned tasks

3. System finds task "Q1 Warehouse Inventory Audit" in Cubicl

4. Automation updates task status to "Completed"

5. Automation creates completion activity in task

6. Manager receives notification of completion

Component 4: Data Storage & Retrieval

Throughout the automation, there are multiple "Get Stored Value" and "Set Named Value" components. These handle:

• User Context: Storing information about ongoing conversations

• Session Management: Remembering previous queries for follow-up questions

• Preference Storage: Keeping track of user-specific settings

• Conversation State: Maintaining context across multiple messages

Component 5: Conditional Logic

The automation uses extensive conditional logic to:

• Route messages based on content

• Handle different database query results

• Manage error conditions

• Provide appropriate fallback responses

Complete Workflow Examples

Example 1: Inventory Query

Scenario: Store employee needs to check if they can fulfill a customer order for winter jackets

1. Store Employee → WhatsApp: "How many winter jackets do we have in Mall Store?"

2. Automation → AI Classifier: Categorizes as "Product, Sales, Stock, Information Query"

3. Automation → AI SQL Generator: Creates query:

4. Automation → Database: Executes query, receives result: 32 units across various colors/sizes

5. Automation → AI Response Formatter: "You have 32 winter jackets in the Mall Store. There are 15 black jackets, 10 navy jackets, and 7 gray jackets available."

6. Automation → WhatsApp: Sends formatted response to store employee

Business Impact: Store employee can immediately tell the customer about availability without calling the warehouse or checking a computer system.

Time elapsed: 2-3 seconds

Example 2: Task Completion

Scenario: Factory supervisor completes a production batch and needs to update the system

1. Factory Supervisor → WhatsApp: "Finished cutting batch #247 - 500 units of summer dresses"

2. Automation → AI Classifier: Categorizes as "Task Information Query"

3. Automation → Cubicl: Retrieves supervisor's task list

4. Automation → AI Task Matcher: Identifies task "Summer Collection Batch #247 Cutting"

5. Automation → Cubicl: Updates task status to "Completed"

6. Automation → Cubicl: Creates completion activity with timestamp, triggers next production stage (sewing)

7. Automation → WhatsApp: "Great! I've marked 'Summer Collection Batch #247 Cutting' as completed. The sewing team has been notified."

Business Impact: Production workflow automatically advances to next stage; management has real-time visibility into production progress; no paperwork or manual system updates needed.

Time elapsed: 2-4 seconds

Example 3: Delayed Task Update with Team Mention

Scenario: Quality control inspector reports delay due to waiting on fabric delivery

1. System → WhatsApp: "Your 'Summer Collection Quality Inspection Batch #247' task is overdue. Please update status."

2. Quality Inspector → WhatsApp (reply): "Waiting for Sarah Johnson from warehouse to deliver the fabric samples for testing"

3. Automation → AI Classifier: Categorizes as "Delayed Task"

4. Automation → AI Delay Processor:

5. Automation → Cubicl:

6. System → WhatsApp (Sarah): "You've been mentioned in 'Summer Collection Quality Inspection Batch #247' task. Fabric samples needed for testing."

7. Automation → WhatsApp (Inspector): "I've updated the task, marked it as blocked, and notified Sarah Johnson."

Business Impact: Automatic dependency tracking prevents production delays; warehouse manager immediately knows samples are needed; management sees real-time bottlenecks in production flow.

Time elapsed: 3-5 seconds

ROI & Business Impact of AI Field Operations

Quantifiable Benefits

Time Savings:

• Eliminates 2-3 hours/day of manual data entry per field supervisor

• Reduces task update latency from hours to seconds

• Saves 30-45 minutes/day per field worker in app switching

Accuracy Improvements:

• 95% reduction in data entry errors

• Real-time inventory visibility prevents stockouts

• Automated task tracking improves project timeline adherence

Cost Reductions:

• Saves $15,000-25,000/year in avoided overtime from miscommunication

• Reduces software licensing costs (no need for mobile PM software for every worker)

• Decreases training costs by 70%

Qualitative Benefits

Worker Satisfaction:

• Workers feel more empowered and connected

• Reduced frustration with technology barriers

• Improved work-life balance through clearer communication

Management Visibility:

• Real-time insights into field operations

• Proactive issue identification through automated tracking

• Better resource allocation decisions

Organizational Agility:

• Faster response to customer requests

• Improved inter-team coordination

• Scalable communication infrastructure

Alternative Use Cases for AI Field Operations

While this automation was built for a textile manufacturing business, this AI field operations pattern can be adapted for many industries:

Fashion & Apparel Manufacturing

• Seasonal collection inventory tracking

• Designer/style/color variant queries

• Production batch status updates

• Quality control reporting

• Fabric and material requisitions

• Store transfer requests

• Sales performance by product line

Construction & Facilities

• Safety incident reporting

• Equipment maintenance requests

• Material requisitions

• Site inspection updates

Retail & Warehousing

• Stock level alerts

• Restocking requests

• Product location queries

• Shift handoff notes

Food & Beverage Production

• Batch production tracking

• Ingredient inventory queries

• Quality control checks

• Temperature monitoring

• Expiration date tracking

Healthcare

• Patient transport requests

• Supply restocking

• Maintenance issues

• Shift coverage coordination

Manufacturing (General)

• Production line status updates

• Quality control issues

• Equipment downtime reporting

• Inventory consumption tracking

Logistics & Transportation

• Delivery confirmations

• Vehicle maintenance needs

• Route delays and issues

• Cargo status updates

AI Field Operations with Monkedo

AI field operations automation represents a fundamental shift in how organizations can support their blue-collar workforce. By meeting workers where they are, on familiar messaging platforms, and using AI to bridge the gap to enterprise systems, organizations can achieve unprecedented levels of operational efficiency and worker satisfaction.

The automation pattern demonstrated here, using Monkedo's AI components to process natural language, generate database queries, and manage tasks, is highly adaptable across industries and use cases. The key is understanding your workers' communication patterns, mapping those to your systems' data structures, and using AI to handle the translation layer intelligently.

As AI models continue to improve in understanding context, generating code, and processing multi-modal inputs, these automations will only become more powerful. The organizations that invest in building these intelligent bridges today will have a significant competitive advantage tomorrow.