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Natural language processing is revolutionizing the way humans interact with software tools. Not only that, the increasing prevalence of AI is opening up the door to a huge range of transformative use cases that would be almost impossible with traditional automation tools.

Of course, this isn’t news. We constantly hear how AI is reshaping just about all aspects of how businesses operate.

Today, we’re thinking about this in more practical terms by checking out some of the most prevalent NLP applications that you can build and deploy right now.

Along the way, we’ll also see how ¹û¶³ÊÓƵ is uniquely positioned to empower teams to build secure, AI-powered internal tools.

Specifically, we’ll be covering:

• What are NLP applications?
• How are tools that leverage NLP built?
• How do we select a model?
• 5 most prominent NLP applications in 2025

Let’s start with the basics.

What are NLP applications?

Natural language processing is a subfield of AI that enables machines to understand, interpret, and generate human language. NLP applications comprise any use case that leverages this technology to handle natural language inputs, outputs, or tasks.

This works by transforming unstructured text into a structured format that machines can reason about.

To achieve this, NLP relies on the following techniques:

• Tokenization - Splitting text into words or structures.
• Parsing - Analyzing grammatical structures.
• Embedding - Mapping words or phrases into numerical vector spaces.

This allows LLMs to represent the statistical relationships between units of language, utilizing these patterns to both understand inputs, perform language-based reasoning, and generate outputs.

As we’ll see in a moment, the majority of NLP applications are built using existing LLMs. This enables developers to implement what would otherwise be highly complex functions without needing to hardcode the underlying rules, logic, or processes.

As such, we’re seeing a sharp rise in the number of tools leveraging NLP across both internal and customer-facing use cases.

How are tools that leverage NLP built?

As we said a second ago, most tools that rely on natural language processing don’t use proprietary, purpose-built models.

Rather, NLP applications typically integrate with existing LLMs, either via APIs or MCP servers, or using a local deployment.

Essentially, this works by sending an end user’s input, such as commands, queries, or other data and text to an LLM, along with a predefined prompt that determines what the model should do with this.

The model then interprets this and provides a response, which the end user application can then interpret, act on, or simply display.

So, whereas in traditional development projects, we must hard-code most logic, the challenge when building NLP applications is largely creating appropriate prompts that will enable our model of choice to output the desired response.

While we have the option of coding our apps from scratch, today, a range of tools exists to expedite this process. Take a look at our guide to the top no-code AI agent builders to learn more.

How do we select a model?

As you may have gathered, choosing a model is a critical decision when building an NLP application. So, it pays to understand the key considerations that will inform our choice.

This includes a range of technical, practical, and financial decision points.

Naturally, costs are a particular priority. Broadly speaking, models are offered on two kinds of bases. First, there are commercial models. Almost all of these charge on a usage basis. That is, we use up tokens to make requests to the model’s API.

Some models will also have tiered pricing, with premium or enterprise licenses introducing additional functionality.

Alternatively, some models are open-source or, at least, open-weight. These can be used for free, but we’ll need to manage our own deployment and hosting. Check out our round-up of the top open-source LLMs .

The other obvious thing we’ll need to keep in mind is how effective different models are for certain tasks. Comparing models in this way can be tricky. On the one hand, there’s just such a broad scope of tasks you may wish to perform with NLP.

On the other, rapid technological advancement means new models are constantly being released, often claiming revolutionary improvements.

To cut through this and select the model that’s most suitable to our specific needs, it’s helpful to pay attention to task-specific benchmarking. These are independent measures of how effectively models can do certain things, like generate code or summarize text.

We may also want to pay attention to other variables when comparing models, such as the length of their context windows or their parameter counts. These influence things like how much knowledge the model can retain within and across sessions.

5 most prominent NLP applications in 2025

With a good grasp of what NLP applications are, how they work, and how they’re built, we can move on to thinking about some more concrete, real-world examples.

We’ve chosen five of the most common use cases that offer general applicability across a range of different scenarios. We’ll also be seeing how they can be built without a single line of code, using the AI column in ¹û¶³ÊÓƵDB.

By default, this leverages ChatGPT as its model, but we also have the option of connecting any OpenAI-compatible LLM, using ¹û¶³ÊÓƵ’s custom AI configs.

In most cases, we don’t even need to write our own prompts, instead relying on ¹û¶³ÊÓƵ AI’s native operations.

Let’s jump right in.

1. Automating categorization

Within ticketing workflows, categorizing submissions is a vital first step toward resolution. The goal here is to use the user’s inputs to determine which team or colleague the ticket should be routed to.

For example, in an IT helpdesk setting, different agents will typically have specific competencies, enabling them to resolve particular kinds of issues.

In a traditional ticketing flow, we’d usually require end users to self-select a category. Service agents must then verify that this is correct.

At best, this creates avoidable admin workloads for our agents, but it may also introduce extra scope for human error. By contrast, NLP enables us to automate this entire process, without requiring complex, hard-coded logic.

To demonstrate this, here’s a simple database table for a ticketing system in ¹û¶³ÊÓƵ.

This contains three columns - Title, Description, and Date. The goal is to enable users to submit their requests using only natural language.

We’re going to use ¹û¶³ÊÓƵ’s AI column by adding an attribute, selecting the AI data type, and choosing Categorize Text as our Operation.

We can then use the Categories text box to input our options as comma-separated values. For a simple demonstration, we’re going to use Hardware, Software, Network, Security, and Other.

We’ve also selected Description under columns.

We’ll use the following form to collect submissions.

Here’s how our ticket categorization app looks in action.

2. Summarizing long-form text

Another key use case for NLP applications is providing actionable summaries of more complex text.

This can provide an important efficiency boost within information-heavy tasks. In particular, where the text concerned is unstructured or messy.

This is especially helpful in scenarios that deal heavily with human communications. A good example of this is summarizing meeting transcripts.

Naturally, meetings often result in important action points and decisions, but transcripts may include extraneous detail about how these were arrived at. Summarization helps to isolate the key points from transcripts.

Here’s another database table, storing a Title, Transcript, and Date for our meetings.

This time, we’re adding an AI Column called Summary, choosing the Summarize Text operation, and setting the Title and Transcript attributes as our targets.

To display this, we’ve created a streamlined UI where end users can flick between the raw transcript and the AI-generated summary.

3. Translation

Translation is one NLP application where the benefits are perhaps most obvious. This can largely eliminate the need for manual translation or more complex, hard-coded solutions in many customer-facing workflows.

There is also a clear financial upside to this, especially in cases where we no longer need to account for service agents with proficiency in a particular language.

As such, LLM-powered translation offers a cost-effective, efficient solution for handling many user-facing workflows in a multilingual environment.

The ¹û¶³ÊÓƵ AI Column also has a Translate operation.

To show this in action, we can take the example of an embeddable Contact Us form.

Again, we’ll use a simple data model, featuring only an Email Address and a Message.

Our goal with this one is that users will be able to submit a query in any language, and this will be translated automatically for our English-speaking agents.

Once again, we’ll add our AI Column, this time choosing the Translate option, pointing it at Message, and setting our Language to English.

Here’s what our embedded form looks like as someone enters a query in a foreign language.

Back in our data section, we can see that this has been successfully translated from German to English.

4. Extracting data fields from natural language

For many engineering projects, one key application of natural language processing is extracting structured data from human input.

The idea here is to isolate key, specified attributes and place them in a format whereby they can be used for further processing or logic. For example, if our app accepts natural language inputs but then sends certain information to an external tool or function via an API request.

Most often, we’ll want to format this as JSON. Depending on the specific use case at hand, this could have a fixed or variable schema.

This is particularly helpful in scenarios where we need users to provide very specific information, without necessarily understanding the rationale behind the required format.

A good example of this is bug reporting.

Any support engineer will tell you that getting users to consistently follow report templates can be massively challenging. This creates delays and issues, as we then need to follow up for additional information or manually create tickets based on reported bugs.

The format and schema we want will need to be specified within our LLM prompt.

Here’s a simple data table with a single attribute called Description. This will allow us to accept a basic, natural language bug report.

We’ll call our AI Column JSON and select Prompt as the operation.

The setup for this one is a little bit more complex than the other examples of NLP applications we’ve seen so far. This is because we actually need to write a prompt to tell the model what to do, including how to use the data in the rest of our table.

We’ll hit the lightning bolt beside the Prompt to open the bindings modal.

Here, we’ll enter the following text to tell our LLM to use the content of the Description field to generate a JSON object in our desired format.

 1Take the information provided in {{ Description}} 
 2
 3and use it to create a JSON object with the following format. 
 4
 5Return only the requested JSON object, no additional text or rationale.
 6
 7{
 8
 9 "bug_report": {
10
11  "description": "App crashes when logging in with Google account.",
12
13  "affected_feature": "Login screen",
14
15  "platforms_affected": ["Android", "iOS"],
16
17  "severity": "Critical",
18
19  "reproduction_steps": [
20
21   "Try to log in with a Google account.",
22
23   "Observe app crash on Android and iOS."
24
25  ],
26
27  "reported_at": "2025-05-08"
28
29 }
30
31}

In the preview panel on the right-hand side, we can see the instruction that the LLM receives, followed by the formatted response that it returns.

We can also check the value in our database with the appropriate JSON object, which is ready for whichever downstream actions we wish to take with it.

5. Sentiment analysis on feedback submissions

Lastly, we have sentiment analysis. This is a very common NLP application that involves accepting a text input and determining the overall opinion expressed within it.

In other words, this means categorizing a piece of text as having a positive, negative, neutral, or mixed sentiment.

This might be used as part of a summary of an overall action, to help with filtering submissions, or as a type of aggregation. For example, allowing us to measure the incidence of positive or negative interactions over time within a service workflow.

To demonstrate how this works, here’s a table with a series of customer feedback messages.

¹û¶³ÊÓƵ’s AI Column has a built-in Sentiment Analysis operation.

Here’s how the output looks in our data table.

We can then use this for aggregating data to track the trends in our sentiments. We’ll add a View for this table and call it Aggregation.

Then, we’ll add a calculation to Count the Messages and Group By Sentiment.

Here’s how this should look.

We can then use this data elsewhere in the ¹û¶³ÊÓƵ builder. For instance, within visualizations for our customer feedback.

Turn data into action with ¹û¶³ÊÓƵ

¹û¶³ÊÓƵ is the open-source, low-code platform that empowers IT teams to turn data into action.

We offer exceptional connectivity for all kinds of RDBMSs, NoSQL tools, APIs, and LLMs, alongside autogenerated app UIs, a powerful visual automation editor, custom RBAC, free SSO, and a host of other features.

There’s never been a faster, easier way to build secure, LLM-powered applications on top of any data. Check out our features overview to learn more.