Key Features

Essential Elements

Generative AI: Effortlessly create comprehensive and coherent paragraphs with our Generative AI Paragraph Generator, ideal for reports, articles, or creative writing tasks.

Paraphraser: Quickly transform text to fit your requirements with our efficient Paraphraser tool.

Translator: Easily translate text into more than 42 languages using our state-of-the-art Text Translator, ensuring seamless communication across global audiences.

Summarizer: Condense extensive texts into clear and easily understandable summaries, simplifying complex information effectively.

Rewritter: Revamp paragraphs to improve their clarity and enhance their style, ensuring they resonate more effectively with readers.

NLP: Harness the power of AI's natural language processing capabilities, leveraging our Small Language Model (SLM) to enhance your applications and processes.

Classify text with precision using our intuitive tools.

Train the SLM effortlessly via a simple form or by uploading an Excel file.

Fenorri's AI seamlessly integrates with the No-Code Development Platform and No-Code Document Generation, allowing you to read and write data to databases, embed AI within your business apps, generate documents, compose emails, send AI outputs to external APIs, and integrate AI results into data forms effortlessly.

Generative AI Example

⭐️ Generative text AI serves as a digital wordsmith, adeptly crafting content at your command. This form of artificial intelligence comprehends human language patterns, generating fresh text based on this comprehension. Imagine prompting it for a short story about a magical journey; it responds with a creative, coherent narrative akin to human authorship. Emulating various writers' styles, tones, and idiosyncrasies, generative text AI produces diverse content – ranging from poetry to news articles to novel dialogues. Beyond mere repetition, it invents novel ideas and phrases, proving invaluable for content creation, creative writing, and aiding tasks such as brainstorming or crafting product descriptions. To illustrate its application, we'll construct a basic scenario using just four blocks in our no-code diagram.

🔧 We'll design a straightforward scenario where one block generates text while another integrates this text into a data input form. Upon running the scenario, we'll observe the data input form pre-filled with the AI-generated content. The text input field will be automatically populated with the AI's creation. To achieve this, we'll craft a simple no-code diagram via the Fenorri editor, comprising Start, AI Text, Form, and End blocks.

✨ For the AI Text block configuration, we'll input the phrase 'what is the meaning of life' directly and select the 'Paragraph Generator' option. This directs the system to generate a paragraph of text centred around the given topic. It's worth mentioning that in practical applications, the topic would usually be dynamic, sourced from sources like internal or external databases, user input, REST APIs, etc. Here, we're setting the text statically.

🚀 To configure our data input block, let's start by clicking on the Form block. Then, select 'PRE-SET DATA IN FIELDS'. Next, click on the cog icon next to our text input field.

💥 To accomplish this, we'll drag and drop the AI Text block into the text area, then click on Apply & Close. Afterward, we'll save and run our no-code app.

✨ Upon opening the data input form, the system should populate the text field with content generated by the AI.

🌟 By employing this technique, we unlock the potential to generate a diverse array of textual content, spanning documents, emails, and beyond. This enables us to automate the creation of written materials, thereby saving considerable time and effort, especially in scenarios involving repetitive tasks. For example, we could effortlessly generate standardized reports, craft personalized emails, or even devise creative marketing content. Essentially, integrating AI generative capabilities into our no-code apps empowers us to streamline workflows and boost productivity, particularly in situations where manual text generation would be cumbersome or inefficient.

Text Classification Example

AI text classification involves teaching computers to categorize chunks of text into different groups based on their topics. It's akin to training an intelligent robotic brain to comprehend written content. Through sophisticated mathematical algorithms and linguistic techniques, these AI systems analyze the words and patterns within the text to determine its appropriate group. Achieving proficiency requires extensive practice with labelled examples, where the text is already associated with its correct category. Once trained, these systems excel at sorting new text snippets into the correct groups. This technology proves immensely useful for various applications, such as identifying spam emails, discerning sentiment from written expressions, and facilitating content discovery tailored to individual interests, be it articles or videos.

⭐️ In this example, we'll delve into a concept akin to the previous one, yet focusing on the potential of AI text classification. Within our Fenorri editor, we'll craft a basic diagram featuring Start, AI NLP (Natural Language Processing), Form, and End blocks.

🔧 In this setup, the AI NLP block will manage text classification tasks, while the Form block will serve as a data input form, specifically featuring a text field designed to receive the output from the AI NLP block. This straightforward example aims to demonstrate the application of text classification within a no-code solution. To initiate editing, let's select the AI NLP block and then click on 'Edit'.

✨ Upon accessing the interface, we can create our text classification training data model. Although we have the option to upload an Excel file, for this example, we'll input all our text classifications manually. Specifically, we'll provide 5 text samples exemplifying a happy mood and another 5 text samples depicting a sad mood:

HAPPY	I'm over the moon!
HAPPY	Today is a great day!
HAPPY	I couldn't be happier!
HAPPY	Life is beautiful!
HAPPY	I'm smiling from ear to ear!
SAD	I feel so down.
SAD	Everything seems hopeless.
SAD	I'm heartbroken.
SAD	I can't stop crying
SAD	It feels like the world is crashing down on me.

🚀 Given that our dataset lacks an adequate number of data samples for each classification, we're primarily assessing the viability of text classification within a no-code environment. Now, let's input the following text into the text area: I'm crying. Typically, in real-world no-code apps, you'd link this text area to dynamic data from another block in your scenario. However, for this demonstration, we're exclusively concentrating on testing text classification capabilities.

💥 Feel free to experiment with other phrases like 'I feel bad' or 'I feel great', but remember that our model's training data is limited (we usually need more data to get different moods detected), so it may not accurately detect the correct emotion. In a real-life situation, we’d tackle this by adding more examples of data for different emotions. This helps our model get better at recognizing and understanding a wider range of feelings.

🌠 For the form configuration, we'll utilize a random text input. Then, we'll link our text data field to the AI block, as depicted below:

🌟 Let's proceed to save and run our no-code app. Upon doing so, we'll observe that the system detects the phrase 'I feel great' as 'HAPPY'.

This example shows how Text Classification can boost no-code apps. It simplifies tasks across different areas. For example, in customer service, it automatically sorts and routes incoming queries, making communication more efficient. In content moderation, it helps detect and remove inappropriate content, keeping online spaces safer. And in sentiment analysis, it lets organizations quickly understand public opinion or customer feedback, allowing for quick responses or making the most of positive feedback. Overall, Text Classification in no-code AI is a powerful tool for handling various challenges efficiently.

Key Features

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Generative AI Example

Text Classification Example

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