Short message service (SMS) might not make headlines in 2025, but it continues to outperform flashier channels where it matters most — reach and response. With 90% of texts read within three minutes and returns hitting $8.11 per message, it remains one of the most dependable tools for customer communication.
What’s changed is the intelligence behind the message.
AI-powered SMS bots can now understand intent, personalize responses, and hold real conversations. Whether it’s appointment reminders or sales follow-ups, these bots handle it with speed, context, and clarity.
In this blog post, you’ll learn how to build a powerful SMS chatbot using the latest artificial intelligence (AI) tools and messaging platforms.
What is an SMS chatbot?
An SMS chatbot is an automated software application that simulates human conversation over SMS using text-based interactions. Instead of requiring human agents to respond manually to every message, an SMS chatbot uses pre-programmed rules or AI to interpret and respond to incoming text messages in real time.
These chatbots are commonly used in customer service, marketing, appointment scheduling, lead generation, and other business functions where quick, scalable, and consistent communication is needed.
How does an SMS chatbot work?
At its core, an SMS text bot follows a structured workflow that enables two-way communication between users and businesses. Here's a breakdown of the process:
The user sends a message
The interaction begins when a user sends a text message to a designated phone number, typically a short code or a virtual number. For example, a customer might text “Check alance” or “Book Appointment.”
The SMS gateway forwards the message to the chatbot
The incoming SMS is routed through a gateway or messaging platform (e.g., Plivo), which bridges mobile networks and the chatbot’s backend. The gateway receives the user’s text and passes it to the chatbot server or engine for processing.
The chatbot processes the input
Once the chatbot receives the message, it evaluates the content using one of the following methods:
- Keyword recognition: Looks for specific predefined keywords or phrases. For example, “balance,” “schedule,” or “cancel.” This is a rule-based system suitable for straightforward use cases.
- Predefined conversation flows: Follows a set of scripted responses and branching logic based on the user’s input. These flows simulate a conversation and can include multiple decision points and replies.
- Natural language processing (NLP): Uses NLP and machine learning (ML) to understand user intent beyond keywords. This allows bots to interpret variations in sentence structure, spelling, or phrasing (e.g., "I'd like to check my account balance" vs. "balance").
The processing logic maps the input to an appropriate intent (i.e., what the user wants) and retrieves information from a database, customer relationship management (CRM) system, or knowledge base to form a relevant response.
The bot sends a response
Once the input is processed, the chatbot composes a response, either static (predefined text) or dynamic (based on real-time data, such as account status or appointment slots).
It sends it back through the SMS gateway to the user’s mobile phone. This entire interaction typically occurs within a few seconds, providing a seamless user experience.
Step-by-step guide to creating an SMS bot
Here’s a straightforward guide to help you build an SMS bot from scratch.
Step #1: Choose an SMS platform
Start by picking a platform that can reliably send and receive SMS. It’s what enables your bot to respond in real time, manage message delivery, and scale conversations without manual effort.
Plivo provides a scalable infrastructure, global reach, and features such as message delivery reports, two-way messaging, and more. To get started with Plivo:
- Create an account: Head to Plivo's website and sign up for an account.
- Get your API credentials: After logging in, access the "Dashboard" to retrieve your Auth ID and Auth Token. These credentials will be necessary for authenticating your API requests.
- Purchase a phone number: You will need a phone number capable of sending and receiving SMS messages. Plivo offers a variety of numbers to choose from based on your region.
- Configure your number: Finally, set up the number for SMS functionality in the Plivo Console to ensure it's ready to receive incoming messages.
With Plivo set up, you’re ready to begin designing the backend of your SMS bot.
Step #2: Set up your development environment
Next, you must set up your development environment to integrate the Plivo SMS API and build the bot. Here’s how:
- Choose a programming language: Select a programming language that you're comfortable with that also supports Plivo's SDK. Common choices include Python, Node.js, or Ruby.
- Install the necessary dependencies:
- For Python: Install Python if it has not already been installed. Use pip to install Plivo's SDK by running pip install plivo.
- For Node.js: Install Node.js from the official website, then Plivo’s SDK using npm: npm install plivo.
- Set up your IDE: Choose your integrated development environment (IDE) for writing code. Popular options include VSCode, PyCharm, or any editor of your choice. Ensure that you have version control to manage your codebase.
- Create a basic API request: Before diving into actual bot development, test to make sure you can send an SMS via Plivo’s API.
Step #3: Design the bot flow
A well-structured bot flow helps your SMS bot respond clearly and stay on track during a conversation. Below is a simple way to plan:
- Understand the text bot's purpose: Determine what the bot is supposed to do. Is it for customer support, order tracking, or information retrieval? Clearly define the use case.
- Map out user interactions: Create a flowchart or wireframe of the conversation. Identify key touchpoints, such as greetings, user queries, responses, and potential follow-up actions.
For example, a simple order tracking bot might start with a welcome message, ask for an order number, and then return the order status.
Define response logic: Think through how the bot will respond at each stage. Consider the different types of responses: simple text, questions, or actions (like triggering a function or fetching data from a database).
The bot flow will serve as a blueprint for development, ensuring a smooth user experience.
Step #4: Develop the text bot
Now, it's time to write the code that brings your AI SMS chatbot to life. Let’s see how:
1. Authenticate with Plivo: At the start of your bot script, import the Plivo library and authenticate using your API credentials:
2. Create messaging logic: Write the logic for sending and receiving messages. For example, to send a message, use the following code:
3. Handle incoming messages: Set up a web server (with Flask or Django, for example) to receive and respond to SMS messages. You'll need to design an endpoint that Plivo can call to notify you of incoming SMS messages.
Example using Flask:
4. Add bot logic: Based on the user’s incoming message, define a series of if-else conditions or use more advanced techniques like ML or AI to interpret and respond to the user's queries. For instance:
Step #5: Test your SMS bot
Once the bot is built, thorough testing is key to making sure it works as intended. It helps catch bugs, validate message formatting, and confirm that conversations play out smoothly across different scenarios. You must test:
- Response accuracy: Simulate various user inputs to see how the bot responds. Test both expected and unexpected messages. For example, test valid order numbers, invalid inputs, and edge cases, such as what happens if the user sends an empty message.
- Edge cases: Ensure the bot can handle edge cases, such as special characters, long messages, or incorrect inputs, in a graceful manner. You may need to implement input validation and error handling to manage these cases.
- Different scenarios: Run through all possible conversation paths based on your bot flow. Ensure the bot can handle various user scenarios and provide helpful feedback.
Step #6: Deploy and monitor
Once your SMS bot is working as intended, it’s time to deploy it and start using it in a live environment. Deployment involves making the bot available to real users and monitoring its performance to ensure it functions correctly.
Below are a few key areas to focus on during and after deployment to keep things running smoothly:
- Host the web server that processes incoming messages on a cloud platform like Amazon Web Services (AWS), Google Cloud, or Heroku. Ensure your server is publicly accessible so Plivo can reach it. Set the webhook URL in Plivo to point to your server’s endpoint (e.g., https://yourdomain.com/receive_sms/).
- Use Plivo’s built-in analytics to monitor your bot’s performance. Keep an eye on crucial metrics like message delivery rates, response times, and error rates.
- After deployment, continually analyze user interactions to identify opportunities for enhancing the bot’s performance. Collect user feedback to refine your chatbot’s SMS responses and add new features over time.
How AI enhances SMS bot performance
AI significantly enhances SMS bot performance by making conversations more intelligent, personalized, and efficient.
Traditional SMS bots rely on predefined scripts, but AI-powered bots apply NLP to understand user intent, even with typos or slang. This results in faster, more accurate responses and increased customer satisfaction.
Let’s understand this better.
In fact, AI SMS text bots are expected to save businesses over $11 billion annually through reduced costs and improved customer service efficiency.
The technology also lets bots analyze past interactions and personalize messages, improving engagement rates. For example, an AI SMS chatbot can recommend products based on previous purchases or browsing behavior. Furthermore, machine learning empowers continuous improvement as the bot learns from every interaction.
Challenges when building an AI SMS bot
AI makes SMS bots smarter, but it also raises the bar. Here’s what can trip you up and what to plan for.
Ensuring data privacy and regulatory compliance
When your SMS bot handles user data — even something as simple as a name and phone number — you're entering regulated territory.
Frameworks like the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA) apply strict rules on how that data is collected, stored, and used. SMS interactions are no exception.
Compliance shapes how your bot is designed. That means building in consent prompts, limiting data collection to what’s absolutely necessary, and giving users a clear way to opt out or request data deletion. It also means being thoughtful about integrations — if your bot connects to third-party CRMs, analytics tools, or cloud storage, you're still accountable for how that data is handled downstream.
Don’t treat privacy as an afterthought because regulators won’t either.
Balancing automation with human support
Automation can improve efficiency, but relying too heavily on it can alienate users, especially when the conversation requires empathy or complex reasoning.
Program your bot to recognize its limitations and offer seamless escalation to human agents when needed. Be transparent with users about when they’re chatting with a bot and when a human is taking over.
It’s also important to define clear handoff points within your bot flow. For example, if a user repeats the same question twice, expresses frustration, or types something your NLP model can’t categorize, that’s a signal to escalate. You don’t need complex sentiment analysis to make this work — just a few well-placed triggers can keep conversations from going off the rails.
On the backend, make sure your support team has context when they step in. Passing along the full conversation history, user metadata, or selected intents can help human agents respond faster and more accurately.
Training AI with low-quality message data
An AI SMS bot is only as good as what it’s trained on, and most teams get this part wrong. They either feed the model with perfect, internally written scripts (“What are your store hours?”), or synthetic training data generated by people who don’t text like actual users.
The result? A bot that looks smart in testing but crumbles when it meets real messages like:
“u open today?”
“store open sat??”
“hey r u guys taking returns rn”
SMS language is messy. It’s informal, typo-heavy, and full of shorthand or implied meaning. If your AI model hasn't seen this kind of input before, it won't know what to do with it, or worse, it will respond with confidence to the wrong intent.
To avoid this, train on anonymized real user messages whenever possible. Include edge cases, abbreviations, slang, and even common customer frustrations. Augment your dataset with diverse tones, sentence structures, and intents.
And if you're fine-tuning large models, don’t overtrain: it’s better to build fallback logic for unclear queries than to have a bot that responds confidently to something it clearly didn’t understand.
How Plivo helps build an AI-powered SMS bot
Plivo is a leading Communications Platform as a Service (CPaaS) that enables businesses to build intelligent SMS bots easily. Its robust APIs and SDKs allow seamless integration with AI models, making it simple to automate conversations and streamline customer interactions.
The platform supports advanced features, including message scheduling, interactive SMS, and multi-language capabilities. Businesses can utilize AI-driven conversation management tools to deliver more intelligent responses, while built-in analytics and reporting tools provide in-depth insights into message performance.
Plivo tool also prioritizes security and compliance, providing encryption and data privacy controls to protect customer information.
Talk to us about launching an AI-powered SMS bot today.
