How to Create an AI platform like Jasper or ChatGPT

Today, generative artificial intelligence (AI), represented by solutions like ChatGPT and Jasper, is becoming the new Uber. Engineers get easier access to the technology stack. Thus, businesses can create cutting-edge products in marketing, education, and healthcare.

According to Statista, the AI market will reach $126 billion by 2025. Companies from different industries increasingly use AI/ML technology to optimize their business processes and provide better customer service. Do you want to become an AI solution provider? With nine years of experience in creating tech products, Softermii’s team prepared a detailed guide on how to create a content generation software based on artificial intelligence.

Stay tuned to learn what features to add to your AI, what technology stack to use, what stages to consider, and how much your development will cost.

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How to create an AI content-generation platform like Jasper or ChatGPT — 15 features to borrow, the tech stack, and the build steps.

The AI market will reach $126 billion by 2025 (Statista); both ChatGPT and Jasper use the GPT-3 model with 175 billion parameters.

ChatGPT focuses on research and conversational understanding; Jasper Chat focuses on marketing and sales content.

Chatting features to borrow: pre-training, fine-tuning, deep understanding (sarcasm/irony), context handling, OpenAI's API, batching and scaling.

Content features from Jasper: content generation, editing, rewriting, plagiarism checker, Grammarly integration, lookback analysis, voice assistant and tone of voice.

Use cases span e-commerce descriptions, manufacturing SEO, entertainment naming, FMCG ad copy and healthcare support.

Tech stack covers data management (OpenML, TensorFlow, Pandas), model management (PyCharm, Neptune) and deployment (Cortex, Kubernetes, Azure).

Based on Softermii's nine years building tech products.

15 Features to Take from Jasper and ChatGPT

If you want to build an AI platform that will generate content, look at the features of ChatGPT and Jasper. Both solutions use the GPT 3 language model with whooping 175 billion parameters.

What is the fundamental difference between these AI tools? ChatGPT is more focused on research and conversational understanding, while Jasper Chat helps create content for marketing and sales.

Chatting features

To build an AI platform like ChatGPT, include the following features:

1. Pre-Training

For pre-training, ChatGPT uses arrays of conversational text, learning to understand the conversation context and create natural and appropriate responses.

2. Fine-Tuning

Setting ChatGPT to perform specific tasks, such as speech understanding and text generating, makes it more efficient.

3. Deep Understanding

ChatGPT handles text of any length and form, understanding such communication tones as sarcasm and irony. It allows the generation of human-like speech.

4. Handling context

ChatGPT understands and maintains dialogue, so it perfectly captures the conversation's essence and adapts to changing topics and contexts.

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5. OpenAI’s API

ChatGPT is available to developers through OpenAI's API, allowing them to use it when building their applications.

6. Batching

Jasper handles batch input and output, which boosts efficiency while processing many requests at the same time.

7. Scaling

Distributed systems help ChatGPT work with large databases and perform complex operations.

Content generation features

If you want to make an AI software for content generation, don't forget to consider these features from Jasper:

1. Content Generation

Jasper requires input such as titles or a couple of sentences to generate text of a given length — a social media post, blog post, or long-read.

2. Editing

Jasper's website allows users to work with a Google Doc-like interface to edit text — bold it, add headings, hyperlinks, and images.

3. Rewriting

Jasper not only writes texts from scratch but also rewrites existing ones, leaving the meaning and increasing the uniqueness.

4. Plagiarism Checker

With Jasper's plagiarism checker, users can see if the text matches other sources to rewrite parts if necessary.

5. Grammarly Integration

Grammar check with rules explanation is a helpful feature, but it is optional to develop it personally. For example, Jasper partners with Grammarly, and all generated texts get automatically checked.

6. Lookback Analysis

Another interesting Jasper feature is considering the meaning of previous sentences and paragraphs when writing new ones. It prevents repetition and preserves the overall sense of the text.

7. Voice Assistant

Voice commands help remotely manage Jasper's activities when it is impossible to do them on a computer.

8. Tone of Voice

Jasper even allows users to set the text's tone, although without the ability to select multiple options.

Examples of Using ChatGPT and Jasper

Jasper and ChatGPT are helpful to all business owners and marketers who need to generate content:

• E-Commerce — product description improvements
• Manufacturing — fascinating content creation and SEO optimization
• Entertainment — creating catchy naming for shows, podcasts, and sections, as well as headlines for blog posts
• FMCG — advertising texts, video scripts, posts for social media, responses to comments
• Healthcare — customer support

The advantage of modern AI systems is that other companies can use them. For example, ChatGPT tools improve Jasper with its image creator, assist Copy.ai with marketing, and partner with Koko, which uses its chatbot to provide emotional support. Some experts even see possibilities of employing ChatGPT for fraud detection in banking.

What tools and technologies to use to create AI software

To create an AI content generation platform, you need a comprehensive technology stack. Here's what an exemplary AI development tech stack might look like:

The modern AI stack covers several key tasks that require different tools.

1. Data management

• Data gathering: OpenML (collection), ImgLab (labeling), TensorFlow (generation)
• Data transformation: Oracle (ETL — Extract, Transform, Load) / Fivetran (ELT — Extract, Load, Transform), Azure Cloud (data storage)
• Data processing: Pandas (analysis), Tecton (feature management), Pachyderm (versioning & lineage), Censius (monitoring)

2. Model management

• TensorFlow (algorithm building), PyCharm (IDE — Integrated Development Environment), Neptune (experiment tracking), Censius (performance evaluation)

3. Deployment

• Cortex (model serving), Azure (virtual machines), Kubernetes (containers), Functionize (testing), Censius (model monitoring)

Crucial Steps To Build a Platform Like Jasper or ChatGPT

What steps should you take for AI software development? To get started, focus on the following:

Identify the Problem

Determine what user pain you want to cover with your product and how to do it. Think about a unique selling proposition. AI is just a tool that you use to solve a specific issue.

Problem identification helps you create a beneficial technology with valuable features for the consumer. In the future, at the MVP stage, it is crucial to test the product on real users and make sure that it meets all the previous points.

Prepare Quality Data

It is better to get high-quality data initially than to spend time improving the AI model itself later. Therefore, it is essential to pay sufficient attention to this step.

In AI, data is divided into structured and unstructured.

• Structured data is well-defined information that contains patterns and structured formats. It includes dates, names, phone numbers, and addresses.
• Unstructured refers to data without consistency and patterns. These are images, infographics, audio, and emails.

After being collected, the data must be cleaned and processed and only then saved. This stage takes an average of 80% of the time before writing code.

Create the Algorithms

Algorithms are mathematical instructions that help an AI model learn from a data set. There are two ways of learning:

• In supervised learning, a person provides a model with training data and waits for specific results. For example, this is how AI can determine the probability of a loan default or the amount that the bank will lose in this case. Popular supervised learning algorithms include Logistic Regression, SVM (Support Vector Machine), naïve Bayes Classification, Random Forest generation, and others.
• In unsupervised learning, an algorithm decreases the number of variables to reduce noise (dimensionality reduction), groups data (clustering) or finds relationships between objects (association).

Train Your Algorithms

The key to creating effective AI is constant algorithm training and retraining. It is essential to achieve the desired accuracy by setting the minimum acceptable threshold. You may also need additional data.

Choose the Platform

Developers always have two options for choosing a platform:

• In-house platforms using open-source frameworks such as TensorFlow, Scikit Learn, or PyTorch. Remember that they can be implemented with a custom team but are difficult to scale to real workloads.
• Cloud or ML-as-a-Service platforms allow for training and deploy models faster. Popular solutions include the Google Cloud Prediction API and Microsoft Azure Machine Learning.

Select a Programming Language

The variety of programming languages includes classic C++ and Java, as well as more modern Python and R.

• C++ is good for its efficiency and performance.
• Java works well with search engine algorithms, scales easily, and is compatible with most platforms.
• Python has a straightforward syntax, making it an excellent choice for beginner programmers.
• R efficiently works with statistics and predictive analysis in data science.

Deploy and Monitor

Finally, after you’ve developed a sustainable and self-sufficient solution, it’s time to deploy it. By monitoring your models after deployment, you can ensure they’ll keep performing well. Repeat these steps until you get the desired result.

How Much Is to Make an AI Software for Content Generation

Previously, only prominent technology companies could afford to create AI software like Jasper or ChatGPT. But today, there are a lot of available libraries, frameworks, and tools that make AI development more accessible.

The following factors influence the price of developing an AI platform:

AI Type

The cost of a chatbot, virtual assistant, and data analytics system varies greatly, as the software has different complexity, productivity, and purpose.

AI intelligence Level

A narrow AI is programmed to perform a specific task, while a highly intelligent program can perform functions without virtually human instructions.

AI Features

Think over your future functionality and decide if you prioritize several functions or complexity. Ask yourself, what is the data format and structure, the minimum indicator of forecast accuracy, data processing speed, and data visualization?

AI control

AI can be developed, run, and managed in-house or outsourced.

In-house management implies that you have your development team and data scientists. Therefore, it is more expensive due to internal recruitment costs, but also more promising.

Outsourcing management allows you to transfer responsibility for the product to third parties, paying for services monthly or one-time.

Project duration

The more complex the functionality, the more time and cost you allocate to development. At the same time, reducing the optimal period will not lead to savings as you will have to scale the team and allocate more hours. On average, it takes 4 to 6 months to create an MVP.

So, the prototype costs at least $2,500, and the MVP price ranges between $8,000 and $35,000. The budget for a complete solution is usually $20,000–100,000.

Final Thoughts

So, how to develop an AI software like Jasper or ChatGPT? There are many nuances to consider — from desired features to the technology stack. And for everything to go smoothly, you need to enlist the support of an experienced team who can advise and implement any solution.

At Softermii, we provide special MVP development packages for startups. They cover all the stages of technology creation, such as market research, product roadmap, investor pitch, PoC and product development. We will help you create AI software like Jasper or ChatGPT and pick a dedicated team. Contact us to discuss your idea and bring it to life as soon as possible.