6 No-Code AI Platforms That Are Accessible

Introduction

When building our own platform, we have been paying close attention to the field of code-free AI. We realize how difficult it is for non-technical personnel to build customized artificial intelligence solutions and artificial intelligence-driven process automation.

Although the no-code market as a whole is maturing (Dreamweaver and MS Frontpage, the earliest WYSIWYG [WYSIWYG] solutions, were both launched in 1997), certain market segments have just emerged, making this field even stronger.Code-free artificial intelligence is one of them. Since we have been observing the field, we think it is also useful for you to share these insights.

We are drawing the intersection of code-free, SaaS, and AI: without setting up any coding or infrastructure AI tools, we can build powerful applications that can make decisions that previously required manual judgment.

No code

As long as there are computers that can program, they have been trying to make programming easier, faster, less technical, and available to a wider audience.In essence, any end-user programming shows that even if most computer users lack coding skills, they will welcome the application potential of various tools (as long as it is not too strenuous to acquire these skills).

No code represents a series of tools that allow people to build applications and systems without having to program them in the traditional way. Instead, core functions can be accessed through visual interfaces and guided user actions, as well as pre-built integrations with other tools to exchange information as needed.

Although these self-imposed restrictions may cause problems with very large or complex applications, the entire family of code-free tools is handing a large part of the power to their users.As Alex Nichols of Alphabet growth fund CapitalG said：

No code can abstract complexity and center on visual workflows to allow business users to take over functions previously owned by technical users.This huge generational shift has the ability to reach every user in every software market and enterprise.

To give a few examples, here are some common things that can be built completely using the above code-free tools (check out Nocodelist for more examples)：

• Websites and login pages that use Webflow (our website is built with it!）
• Web or mobile applications with Bubble, Adalo, Mendix or Thunkable
• Through Octane AI, Kore.ai , Landbot or mindsay build a chatbot or virtual assistant
• Establish a database through Airtable
• Combine your tool stack with Zapier, tray.io , Integromat, Parabola or Paragon are connected
• E-commerce through Shopify or Weebly
• Use Memberstack to manage the number of members

It is reasonable to believe that the code-free field will continue to exist.The artificial intelligence tools built on these principles show that when it comes to the work to be done and the technology in place, the field has grown not only in width, but also in depth.
Before we move to code-free AI, we will first quickly touch on a basic question: when does it make sense to use AI?

When to use artificial intelligence

Please note that artificial intelligence can be used in various applications, but we deliberately limit the discussion to business applications.
Broadly speaking, artificial intelligence is particularly useful when humans need to make certain intelligent judgments and many of them are ongoing.We often use the phrase ”artificial intelligence starts where rule-based automation ends”-this makes sense from our point of view, but it shouldn’t be generalized (some tools go beyond pure automation, for example, Obviously AI is used to analyze tabular data on a large scale).
More realistically, whether artificial intelligence should be used is a question of whether there are other solutions that can do the job at the same (or higher) level of quality, cost, or speed.If so, they are usually more suitable for this job.Since it is not explicitly programmed to execute x, AI is (still) vague in nature.

At the same time, when there are too many rules or exceptions to consider, explicit programming usually causes problems.In this case, artificial intelligence usually works better.For example, it is of course possible to set up rule-based automated processing of text by using long chains of words and phrases, but in many cases, this will not be effective due to high cost or poor performance.

Prospects for code-free artificial intelligence

A large number of artificial intelligence and machine learning companies claim that they have popularized artificial intelligence, which may be correct for their respective target users, who are usually still ordinary engineers.Among all these companies, those that are building code-free tools are the closest to the idea of “any untrained person”.
This increase in democratization seems to be long overdue: it has been proven time and time again that most companies are working hard to realize the full potential and scale of artificial intelligence, which makes this trade-off even more important.

The easy-to-use ML platform leverages the time/value/knowledge trade-off in a truly attractive way and allows users without AI coding skills to optimize daily operations and solve business problems.
Visual, usually drag-and-drop, code-free AI tools make it easier for non-technicians or people who lack the time or resources to build such systems from scratch to understand AI.

In addition, code-free artificial intelligence has some additional advantages：

Accessibility: Code-free artificial intelligence enables organizations to use artificial intelligence first, and can be used as a stepping stone to strengthen the use of data science or artificial intelligence in the future. Relatively low investment, coupled with people’s accumulation of practical knowledge of artificial intelligence tools, has alleviated the biggest obstacle to the adoption of artificial intelligence by small and medium-sized companies.
Availability: Plug and play allows anyone in the organization to find AI solutions to problems, usually in a budget-friendly way.These tools are built with non-technical users and non-developers in mind.

Speed: The best code-free AI platform allows users to quickly iterate over the entire value chain of machine learning.This allows for faster experiments to understand what can be done with your own data, and then resume business immediately.There is no better way to convince someone than to show them the process in a simple and intuitive way.

Quality: In the beginning, code-free tools were built for people who may not have a technical degree or even have expertise in the subject.This requires a lot of work on the product, because it is necessary to carefully select reasonable default settings and security measures on behalf of the user.In order to further reduce such risks, some artificial intelligence platforms have built-in manual review functions and solicit opinions when needed.This combination first reduces human error when setting up such systems and allows direct interaction with the platform during daily operations.

Extensibility: Artificial intelligence itself does not care whether it performs tasks for a single user or a hundred users, nor does it care about services that automatically scale up or down according to the load.

Draw a code-free AI panorama

There are already some great tools (and a lot of resources, please check out MakerPad, Zeroqode, and NoCode)-we think it’s a good idea to draw them out.
In addition to providing a current snapshot of the industry, it may also help to better understand the nuances between seemingly similar tools.This may be obvious to experienced ML practitioners, but by definition, code-free tools are targeting a less technical audience, so that’s it.
When observing this field, we noticed that two dimensions are prominent：

Comparison of specific user scenarios with agnostic generalists: companies either build business models around specific industries and use cases (e.g. Accern), or take advantage of the fact that cross-industry companies have similar problems and lack similar AI development resources (e.g. MonkeyLearn, Levity).

What types of data can be processed: Don’t confuse artificial intelligence with stew-just throwing a bunch of data into it won’t get what you want. Therefore, a key issue is which data the company focuses on first, and the most important types are images, text, documents, or structured (tabular) data.

Code-free artificial intelligence is still a growing market-most companies operating in this field tend to position themselves in technology (NLP, speech recognition, computer vision) and specific user scenario management (classification issues, CRM, network builders, business applications).It is usually difficult to draw the line between the end of one application and the beginning of another-especially when we look at AI applications. In order to understand the situation more clearly, we decided to delve into uncoded AI players and what they offer. The list below is by no means exhaustive, and there is no specific order (hmm…In alphabetical order), we will continue to add new players-but it is necessary to introduce some structure in the panorama.

What makes the most sense to us is to group based on core value propositions-we know that many of these companies are active in more than one scenario. It’s great to use the no-code movement to become a maker-but we first need to know what we want to create.
In short, we regard the following standards as code-free artificial intelligence：

Tools that enable users to build solutions from scratch and integrate them into their processes-previously one or more (ML) engineers were required to build.

Create value for users and companies of all sizes-not just an enterprise-level development tool (think Uber’s Ludwig).

Available for non-technical personnel to use-this is essentially the core of the code-free movement. More importantly, this is one of the criteria we have been arguing about for the longest time. Knowledge level plays a key role-although there are tools like MS Azure, C3 AI Suite and even deep Cognition-they are not built for ordinary knowledge workers, but for those who already know what they are doing during the development phase.

Finally, we considered the horizontal and vertical approaches of these tools: if you want to fully understand and update the code-free AI ecosystem, then you should probably pay attention to these tools.

Description：

SME: Small and medium-sized enterprise
Narrow: Narrow
Broad: Extensive
Tabular: Table

Last but not least, we considered the vertical and horizontal focus of the tool.Some tools perform well in very specific user scenarios-because they are built for this (for example, Lobe is great if you try to use machine learning for personal use, or if you are mainly looking for it, please check out Rossum for document processing).If you are looking for tools for a specific task, process, or team, please stay on the left side of the center of this map.If you want to build AI into multiple processes or an entire organization-tools with a broad use case focus may be more suitable.
One last consideration: Just like using any other software, you will find code-free tools that are more suitable for enterprise implementation: whether it is due to billing, launch work, or the need for cross-team collaboration with your analysis/data science department.If you want to consider the time to realize the value, then the tools built for SMEs provide flexibility and there are fewer technical settings.
We will briefly introduce the selection of these tools.

Aito
Aito is a deployer of predictive analysis and NLP automation. It is aimed at RPA developers with a simple UI and API that integrates with many automation platforms. Aito focuses on tabular data sets (and some textual data), but its core product is its automatic retraining system. Indicators such as automation rate, prediction error, and monitoring accuracy are some of their built-in functions.

Clarifai
Clarifai is an NLP and computer vision tool, founded in 2013, that provides end-to-end solutions for unstructured data modeling throughout the AI life cycle. Image, video, and text recognition solutions are built on an advanced machine learning platform and can be easily accessed through APIs, device SDKs, and on-premises deployment. They have accurate and detailed results with fast APIs, and provide some concise pre-training models (personnel, vehicles, and general detectors).

Crowd AI
Crowd AI is a code-free AI tool based on computer vision, focusing on images and videos.For technical and non-technical users, their use cases are mainly focused on the industrial field (such as vegetation management or disaster response).

Dataiku
Dataiku is an AI analysis tool designed to help data scientists build business applications, focusing on ML Ops and AI Ops.If you are satisfied with the data, then it is quite simple to use-and it has a very concise list of plug-ins.

DataRobot
The DataRobot enterprise AI platform democratizes data science and automates the end-to-end process of building, deploying, and maintaining AI. Founded in 2012, its core focus is on predictive models, supported by open source algorithms, which can be deployed in the cloud, on-premises, or as a fully managed AI service.

Google AutoML
AutoML is the star of the Google software package, and the tool works very similar to CreadeML-only in the cloud.The model package currently includes Sight (visual and video intelligence, the latter is in the testing phase) and language (NLP and translation), as well as structured data (tables) functions. AutoML generally manages to cover a lot of areas without code, but again, if you are not a developer, it is difficult to operate.

Levity
Levity focuses on image, text, and document classification, enabling users to train custom models based on their specific use case data-suitable for enterprises of any size. Custom models and processes also include a person-in-loop option, so the user has full control, because the model will ask for input in uncertain places-and will automatically learn from the interaction. Levity focuses on providing end-to-end solutions that integrate with all the tools people use every day.

Lobe
Lobe (a Microsoft product) provides image classification, object detection, and data classification functions. Lobe is a free private desktop application with a large number of pre-trained solutions (for example, emotional response allows your application to react to different emoticons, allowing people to send emoji reactions using only their faces).

MonkeyLearn
MonkeyLearn provides an integrated text analysis and data visualization studio for obtaining topics, emotions, intentions, keywords, etc. based on unstructured text data. Features include automatic labeling of business data, visualization of actionable insights and trends, and simplified process text classification and extraction. Integrate with Zendesk, RapidMinder and Google products, and more products will be launched soon. In addition, in our eyes, it has one of the best blog resources in text analysis.

Nanonets
Nanonets belong to the field of computer vision-they provide ready-made solutions for most common document types, but they also provide settings for custom models. One of their cooler solutions provides to build an ID card verification model for any country, format, or language-including perspective transformation, that is, a model that can handle tilted or tilted images.

Noogata
Established in 2019, Noogata is another predictive analysis tool worth a look. Fast and easy to set up, it is a good solution to customize the model and make your decisions more data-oriented.

ObviouslyAI
Obviously AI, founded in 2019, uses NLP processing to perform tasks on user-specific text data. Drag and drop your data into CSV or integrate with HubSpot, Salesforce, or MySQL (and others), select your forecast column, it will automatically build a custom ML algorithm, and you will eventually get a forecast report. The platform is particularly useful for small and medium-sized enterprises, who are looking for a tool to choose an algorithm that suits their needs.

Pecan AI
Pecan AI is another predictive analysis tool that allows you to gain insights and turn them into important indicators. Many data scientists use it, and you can get a feasible forecast within 14 days.

Primer
Primer is an out-of-the-box NLP model builder with powerful integration and many pre-trained models that can be used. If you want to visualize the performance of your model at once, it’s worth taking a closer look.

Robo flow
Robo flow is a computer vision-driven tool that allows you to train and deploy models of images, annotations, and videos. They support multiple annotation formats, so the retraining process is very smooth.

User scenarios of code-free AI

“What can I do with it?” It can be said to be the most common problem in this field, and for good reason: by definition, the main user base of uncoded AI is composed of non-technical personnel. They may know one or two things about artificial intelligence, but they certainly don’t deal with this topic every day, let alone writing neural networks for a living.
It turns out that the fastest way to master the usefulness of artificial intelligence as part of business operations lies in studying some user scenarios.

Please note that some tools hint at user scenarios through settings (for example, for specific industries or processes), while others are designed to be trained by users for their specific purposes. Some platforms are available at the same time. Naturally, there are different application layers at work, classification, labeling, detection, data extraction… The list goes on and on.
Nevertheless, there are still some things to consider…
One of the myths of the no-code field is that if you want to enter the stage of implementation of any solution, you must lower your expectations.The days when we have to choose between fast/cheap/good are numbered, but we must manage expectations.
The current field of code-free AI shows that every solution is intrinsically linked to the design of tools.Some practitioners point out that in some cases, it is important to remember that once you develop an application on the platform, you will always be linked to the platform as long as the application is running.In the context of PoC, this is not a problem, but in the context of applications that are expected to last, the situation may be different.
Although a code-free platform reduces the complexity of engineering and coding, it is not a magic tool for everything.Instead, you should (as a process owner) consider the following issues：

What problem do I want to solve?
Which tasks make up for this problem?
What level of project management do we need?
What is the role of tools/platforms in the company structure?
Does the platform meet the needs of the problem?
In the long run, is using code-free AI tools a strategic choice?

What will the future of code-free AI bring?

For a variety of reasons, companies are steadily turning to code-free platforms.Partly due to the chain reaction to labor management, exposure to developers and software engineers slows down project delivery-and this is where technology can add real value.Not only enabling your employees to provide solutions, but also maintaining relevance and competitiveness in the current environment is a unicorn that we all want to seize.
Research shows that it is estimated that by 2024, nearly 65% of application development will be completed through low-code and no-code platforms, and no-code AI will play an important role in it. When it is possible to interrupt the current process management and it is widely available to everyone, it is difficult to see the logic of doing things in the traditional way.