Automated WhatsApp Messages for Insurance Clients with n8n

✍️ Summary

An automation was implemented in an insurance agency to send personalized WhatsApp messages to clients using their specific information (name, profession, contracted services, sales notes, and contract expiration date). This enables precise, useful, and conversion-oriented communication at the right time, without manual intervention.

🛑 Problem / Pain Point

Companies that manage clients with contracts or recurring services (such as insurance agencies) lose renewal or cross-selling opportunities when they don’t contact customers in time or in a personalized way. Additionally, drafting and sending messages manually consumes time and is difficult to scale.

🎯 Automation Objective

• Send automatic and personalized WhatsApp messages before a contract expires.

• Leverage client data to offer relevant additional products.

• Reduce manual work for sales teams.

• Increase renewal rates and cross-selling opportunities.

⚙️ Solution Description

A daily automation was designed that:

• Extracts client information from a database: name, profession, contracted services, sales notes, phone number, and contract end date.

• Automatically filters clients whose contract expiration date falls within the defined window (e.g., 30 days before).

• Generates a personalized message combining all the data:

  • Contract expiration reminder.

  • Related offer based on profile and past needs.

  • Summary of current services and new recommendations.

• Sends the message directly to the client’s WhatsApp using their phone number.

🔄 Before vs. After Process

• Client Communication: Before, messages were sent manually or not at all. After, sending is automatic, timely, and personalized.

• Use of Client Information: Before, data such as profession or sales notes were not used. After, each message integrates this information to generate context and increase relevance.

• Cross-Selling Offers: Before, additional products were not strategically proposed. After, the system suggests new services based on profile and past needs.

📈 Benefits Achieved

Quantitative:

• Significant time savings for the sales team.

• Reduced risk of missed renewals due to lack of contact.

• Complete automation of the process with daily review.

Qualitative:

• Better client experience thanks to relevant, well-written messages.

• Increased commercial effectiveness: messages are action-oriented and contextually adapted.

• Scalability: the solution can be applied to hundreds of clients without additional effort.

AI-Powered Incident Reporting via Telegram with n8n

✍️ Summary

In an industrial company with more than 150 employees, an AI agent was implemented to automatically register incidents and reports sent by clients through Telegram. The system interprets both text messages and audios (which are automatically transcribed), extracts the relevant information, and saves it in a structured database. Then, the client receives confirmation that their incident was successfully registered.

🛑 Problem / Pain Point

Support teams received incidents through multiple channels in a disorganized way. This caused information loss, lack of tracking, and required manual intervention to process each case. Additionally, client-provided information was not standardized, making registration more difficult.

🎯 Automation Objective

• Capture incidents sent by clients through Telegram.

• Automatically register key data in a structured database.

• Transcribe audio messages into usable text.

• Confirm to clients that their message was received and registered.

⚙️ Solution Description

The solution is based on a workflow developed in n8n, which is triggered when a client sends a message through Telegram. The workflow performs the following actions:

• Detects whether the message is text or audio.

• If audio, it downloads and automatically transcribes it.

• Extracts key information from the message: client name, reported incident, and company name.

• Saves the structured data in a database such as Airtable or similar.

• Sends a confirmation message to the client indicating that the incident was successfully registered.

🔄 Before vs. After Process

• Incident Reception: Before, messages arrived disorganized in chats and were not registered. After, each message is processed, structured, and stored automatically.

• Audio Processing: Before, audios required manual listening for interpretation. After, they are automatically transcribed using AI.

• Client Confirmation: Before, there was no clear confirmation of registration. After, the system automatically responds, ensuring incident receipt.

📈 Benefits Achieved

Quantitative:

• Reduced time required to manually register incidents.

• Increased number of correctly documented incidents.

• Centralized information for tracking and analysis.

Qualitative:

• More professional client experience with immediate confirmation.

• Greater order and traceability in incident management.

• Ability to scale the channel without overloading the support team.

AI Agent (RAG) for Automated Technical Email Responses with n8n

✍️ Summary

In an industrial supply company, an AI agent specialized in industrial trade was implemented to respond to customer emails and technical inquiries. The agent is capable of interpreting emails with technical or product requests, generating structured responses that include models, prices, estimated delivery times, and documentary sources, and adapting to both detailed and generic queries. The solution works as a proof of concept and can be easily integrated into customer service workflows.

🛑 Problem / Pain Point

The sales and technical team received many client emails with technical questions or product requests that had to be manually analyzed and answered. This process was repetitive, slow, and dependent on the individual knowledge of each employee. In addition, many inquiries lacked clear information or specific references, making it difficult to respond efficiently.

🎯 Automation Objective

• Automate the interpretation and response of technical emails.

• Generate structured product proposals: name, description, prices, and timelines.

• Support the sales team in handling inquiries without direct human intervention.

• Provide technical responses even with incomplete or ambiguous information.

⚙️ Solution Description

An agent was designed with expertise in industrial trade, with two main capabilities:

• Respond to technical inquiries: extracts and analyzes questions about products, accessories, or processes, even if references are unclear.

• Generate responses to commercial requests: when pasting an email, the agent generates a comparative table with models, prices, and delivery times. When applicable, it cites the documentary source used (e.g., catalogs or technical documents).

The agent works through a simple chat interface, where real emails can be copied and pasted, or manual questions can be written. All logic was designed to be flexible and adaptable according to the business’s products or catalog.

🔄 Before vs. After Process

• Handling technical emails: Before, emails were reviewed and answered manually one by one. After, the agent generates automatic responses based on the client’s request.

• Interpreting incomplete requests: Before, the team had to contact the client for clarification. After, the agent interprets the need and suggests possible products.

• Generating preliminary quotes: Before, it required human intervention. After, it is automated with models, prices, and estimated times.

📈 Benefits Achieved

Quantitative:

• Saved significant time in handling commercial and technical emails.

• Faster responses, even outside working hours.

• Automatic generation of product comparison tables.

Qualitative:

• Improved client experience with complete and structured responses.

• Reduced workload for the technical and sales team.

• Scalable foundation for integration into more complex or automated processes.