AI/ML is a technology capable of replacing humans for multiple job profiles. And hence, this has been the topic of many recent discussions and discourses. However, on the other hand, businesses are fearing the over-exploitation of this technology at workplaces, by profiles that do not require it. This, along with some other reasons, is the push behind the introduction of an AI detection tool. This blog discusses the basics of this tool and the AI content detection tool development cost.
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What is an AI Content Detection Tool?
An AI content detection tool is a program that makes use of artificial intelligence (AI) to tell the AI program-generated content apart from human-curated content.
Using ML algorithms, the content is compared to a known AI-generated content database. This allows the tool to identify content that has not been written by a human and therefore, flags it for review for manual editing.
The tool looks at 4 parameters against which the content is checked:
It refers to the probability of a word appearing in a sequence of words.
This refers to the frequency distribution of words in the content being investigated. AI-generated content generally has a higher level of business.
3. Non-Human Linguistics
This refers to the language that appears very non-human or unusual to read.
Absurd Syntactic and Semantic Patterns
Syntactic pattern refers to the words and phrases used as per the grammar rules that lead to grammatically correct and coherent content.
Semantic patterns, on the other hand, refer to the sense a sentence makes or the words in a sentence make.
Some of the best AI content detection tools and apps are Originality.ai, AI Detector Pro, Winston AI, Writer, GLTR, etc.
Let’s now look at the AI content detection tool development cost.
Development Costs of an AI Content Detection Tool in 2023
The development cost of an AI content detection tool can cost between $50,000 to $200,000.
One reaches this cost by factoring in multiple variables, such as some project-based requirements, the complexity of the AI content detection tool, as well as the technologies implemented.
We would now look at some of these factors:
1. Tool Complexity
Any tool’s cost depends upon how accurate it is at its job. Therefore, the more complex, accurate, and feature-rich an AI content detection tool is, the higher it would cost to develop it.
2. Data Requirements
An NLP model needs massive amounts of data for its training. And therefore, an AI content detection tool also requires similar amounts of data for its training purposes. The larger the volume of data is, the more tool accuracy would be gained.
Open-source data libraries could be used for data training, at reduced costs.
3. Development Team
AI content detection tool development requires knowledge and skill, like any other software development. It requires a highly skilled and dedicated development team of data scientists, software developers, as well as ML engineers with extensive exposure and experience.
If a business decides to hire and retain such a team, it is a highly expensive and daunting affair. Instead, outsourcing AI content detection tools is a better option. It saves a lot of time and costs, whilst offering the much-required expertise and experience, in addition.
Additional features could be added to the tool as per business needs, such as proofreading or plagiarism check. Naturally, these features would come at additional costs.
5. Infrastructure Requirements
An AI content detection tool would require storage and computational infrastructure and the costs of the same must be factored in while finalizing the development project.
6. Testing and Validation
Tests and validation are crucial for any software development. Similarly, the testing for an AI content detection tool might require a significant investment of time, teams, and hence, costs. Failure to do the same, hence might lead to future development and maintenance costs.
7. Integration with Existing Software
Like any other newly developed software, an AI content detection tool must integrate seamlessly with the existing systems and software. In the case of an AI content detection tool, smooth integration with the CMS is important, but this require businesses to incur additional costs.
While these were the many factors that influence the cost of an AI content detection tool development in 2023, let’s now have a look at the development life-cycle of this tool.
Development Life-cycle of an AI Content Detection Tool
Following are the steps that form the life-cycle of an AI content detection tool development:
1. Market Research and Competitive Analysis
The first and foremost step in any software development is research. This research involves market, or user research and competitive analysis. It is important to offer users the product or features that the marketplace lacks, even though users need them.
Furthermore, competitive analysis is carried out for the same, to study the products already existing in the market. This helps a business to identify and fill the potential product gaps in the market as well as avoid the repetition of already existing features and functionalities.
Simultaneously, pricing strategy and marketing approaches are also analyzed along with customer reviews and feedback on the given product. All this data is eventually used for product planning, designing, and engineering.
2. Choosing a Suitable Engagement Model for the Development
The next step and a rather crucial one is choosing the most appropriate engagement model amongst the various options available as per the business needs.
A business could go for in-house hiring; however, it is relatively costlier and demands a significant amount of effort. The remaining options are the two engagement models namely staff augmentation and project outsourcing.
Staff augmentation and project outsourcing, both have their own pros, cons, and use cases. While the former is suitable if the business just needs to fill in some expertise gaps, the latter is a better option for big, ambitious projects that require the expertise, and experience of a third-party service provider.
In both cases, however, a business needs to research and review a lot of factors and then choose the right software development company with extensive exposure to emerging technologies like AI/ML.
3. Data Gathering And Training
The gathering of datasets is the next step in an AI content detection tool development life-cycle. It involves collecting data from multiple sources such as websites, social media platforms, and public sources. Then the data is organized in a way that all of it is labeled correctly, and multiple data types are used for the algorithms to accurately detect a wide array of content. This data is then used by ML algorithms to train their learning algorithms.
Then through the transfer learning NLP technique, the AI tool, which is trained on one task, makes use of the existing knowledge to train itself for a similar kind but a different task.
4. Testing And Validation
Once the models have been trained, it is crucial to run them against test data to check their responsiveness and accuracy.
Furthermore, undertake user testing with real users. This would get you their real feedback and ensure that it caters to their needs and has ease of usage.
5. Set Up And Integration
After testing and validation, comes deployment and integration with existing workflows, software, and systems. This may require creating APIs or some integration points for the smooth interaction of the tool with other systems.
This step must be undertaken in collaboration with the development team.
6. Post-Launch Maintenance
After deployment, a business needs to constantly monitor the performance of the AI content detection tool. This may involve tracking metrics such as accuracy, speed, user satisfaction, etc. This is done to ensure that the tool manages to meet user expectations and gives them a hassle-free experience.
As AI in the content creation market is being increasingly used, a tool to regulate its usage for certain job profiles is no surprise. AI content detection tool is developed to differentiate human-written content from AI -generated content and the future of this tool is certainly bright as AI technology takes over the world.
AI content detection tool development cost is an important topic of discussion if a business is planning to build such tools. It depends upon various factors such as complexity, additional features, development tool, etc., and a business seeking this tool needs to evaluate every variable for fruitful development and usage.
Furthermore, its development life-cycle involves various crucial steps that would decide the future working and utility to users, of a particular AI content detection tool.
You could ensure that the AI content detection tool life-cycle is carried out in the most efficient and cost-effective manner with BigOhTech’s AI/ML development services.
From ML and AI-driven app/web development to NLP consulting and implementation to smart chatbots, BigOh specialize in AI/ML-powered products and tools. We innovate so that our clients can thrive in a marketplace that evolves at a rapid pace.
Visit our website and read through our portfolio to see how BigOh has helped multiple clients achieve their business and tech goals.
Q1. How can we detect AI content?
AI content detection is usually done through training the AI content detection model on how to differentiate or categorize AI-generated content and human-written content. This is usually done by training the tool to identify certain writing styles and factors that are specific to machines.
Q2. How accurate are AI content detection tools?
Since AI content detection tools are relatively newer, it is difficult to comment on their accuracy. While the tool development companies claim that these tools are 99% accurate in the detection of content, AI-generated content might not always be as easy to tell apart, especially when it is highly manipulated.
Q3. What Are Some Challenges in Developing an Ai Content Detection Tool?
Certain challenges in the development of AI content detection tools are: 1. Quality of the data collected 2. Algorithm bias 3. Privacy concerns with personal data 4. Other technical complications that come with algorithm complexity and the hardware required