Mapping the No-Code AI landscape

Introduction

While building our own platform, we have been keeping a close eye on the no-code AI space. We realized how difficult it was for non-technical people to build custom AI solutions and AI-powered process automation. That is why we wanted to share this knowledge with you.

While the no-code market is maturing as a whole (Dreamweaver and MS Frontpage, the first WYSIWYG (what you see is what you get) solutions, both launched in 1997), certain sub-segments are just emerging, making this space more powerful. No-code AI is one of them. As we are constantly observing the field, we thought that sharing these insights would be useful for you as well.

We are mapping out the intersection of no-code, SaaS, and AI: AI tools that don't require any coding or infrastructure to be set up for building powerful applications that can make decisions that previously required human judgment.

Buckle up!

No-code in a nutshell

For as long as there have been computers to program, there have been attempts to make programming easier, faster, less technical, and available to a much broader audience. Essentially, any end-user programming signals that even though most computer users lack coding skills, they would welcome the application potential of various tools – as long as the effort to obtain these skills is low.

No-code stands for a family of tools that allow people to build applications and systems without having to program them in a conventional way. Instead, the core functionality is accessible through visual interfaces and guided user actions, as well as pre-built integrations with other tools to exchange information as needed.

While these self-imposed restrictions can lead to issues for very large or complex applications, the whole family of no-code tools is handing a big chunk of power to their users. As Alex Nichols from Alphabet's growth fund CapitalG said:

"No code is empowering business users to take over functionality previously owned by technical users by abstracting complexity and centering around a visual workflow. This profound generational shift has the power to touch every software market and every user across the enterprise."

To give you a few examples, here are some common things that can be built entirely with said no-code tools (check out Nocodelist for more examples):

• Websites and landing pages with Webflow. (ours is built with it!)
• Web or mobile applications with Bubble, Adalo, Mendix or Thunkable.
• Chatbots or virtual assistants through Octane AI, Kore.ai, Landbot or Mindsay.
• Databases through Airtable.
• Connecting your tool stack with Zapier, tray.io, Integromat, Parabola, or Paragon.
• E-commerce through Shopify or Weebly.
• Manage memberships with Memberstack.

It is fair to believe that the no-code space is here to stay. AI tools built on these principles are showing that the field not only grows in width but also depth when it comes to the job to be done and technology in place.

Before we move to no-code AI, we will quickly touch on one fundamental question first: When does it even make sense to use AI?

When to use Artificial Intelligence

Note that AI can be used for a variety of applications but we intentionally limit our discussion to business applications.

Broadly speaking, AI is particularly helpful when there is some sort of intelligent judgment to be made by humans and when there are many of these on an ongoing basis. We often use the phrase "AI starts where rule-based automation ends" – which makes sense from our viewpoint but should not be generalized (there are tools that go beyond pure automation, e.g. Obviously AI for analyzing tabular data at scale).

More practically, whether AI should be used or not 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 generally better suited to do the job. AI is (still) inherently fuzzy due to not being explicitly programmed to do x.

At the same time, explicit programming often leads to problems when there are simply too many rules or exceptions to be considered. In that case, AI often works better. For example, it is certainly possible to set up rule-based automation for processing text by using a long chain of words and phrases but in many situations, this wouldn't be efficient due to high costs or poor performance.

The promise of no-code AI

A vast amount of AI and Machine Learning companies claim that they democratize AI and this is probably true for their respective target users, which oftentimes are still regular engineers. Out of all these companies, those that are building no-code tools get the closest to the ideal of "any person without prior training".

This increased level of democratization seems overdue: It has been proven time and time again, that the majority of businesses struggle to implement AI to its full potential and scale, making the ease of this trade-off even more crucial.

Easy-to-use ML platforms leverage the time/value/knowledge trade-off in a genuinely attractive way and allow users with no AI coding skills to optimize day-to-day operations and solve business issues.

Visual, often drag-and-drop, no-code AI tools make AI less intimidating and more comprehensible to non-technical people or those who lack the time or resources to build such systems from the ground up.

Besides this, there are some additional advantages to no-code AI:

• Accessibility: No-code AI enables organizations to make use of AI in the first place and can act as the stepping-stone towards intensified use of Data Science or AI in the future. The comparably low investment paired with people building up hands-on knowledge of AI tools mitigates the biggest obstacles to AI adoption at small and mid-sized companies.
• Usability: Plug-and-play allows anyone in the organization to find an AI solution to a problem, and more often than not, in a budget-friendly way. These tools are built with non-technical users and non-developers in mind.
• Speed: The best no-code AI platforms allow users to iterate through the whole value chain of Machine Learning quickly. This allows for more rapid experimentation to see what can be done using one's own data – and getting back to business right afterward. There is no better way to convince someone than to show them the process in a simple, intuitive way.
• Quality: No-code tools are built for people who may not possess a technical degree or even deep expertise in the subject, to begin with. This requires extensive work going into the product as sane defaults and safety measures need to be carefully chosen on behalf of the user. To further mitigate such risks, some AI platforms have human review functions built-in and ask for input when needed. This combination reduces human error when setting up such systems in the first place and allows direct interaction with the platform during daily operations.
• Scalability: AI itself doesn't care whether it performs a task for a single or a hundred users and neither do servers that are automatically scaled up or down, depending on the load.

Mapping the no-code AI landscape

There are some great tools already out there (and plenty of resources – check out MakerPad,  Zeroqode, and NoCode) – and we thought that it would be a good idea to map them out.

Besides providing a current snapshot of the industry, it might also help better understand subtle differences between seemingly similar tools. For seasoned ML practitioners, this may be obvious but no-code tools are addressing a less technical audience by definition, so there's that.

While observing the field, we noticed that two dimensions stood out:

1. Use-case specific versus agnostic generalists: Companies either build their business models around a specific industry and use case (e.g. Accern) or leverage the fact that companies across industries have a similar problem and lack similar AI development resources (e.g. MonkeyLearn, Levity).
2. What data types can be processed: AI isn't to be confused with stew – just throwing a bunch of data into it won't give you what you want. Therefore, a key question is what data a company focuses on in the first place – the most important types being images, text, documents, or structured (tabular) data.

No-code AI is still a rather growing market – and most companies who operate in this space tend to have positioned themselves in technologies (NLP, Voice Recognition, Computer Vision) vs specific use case management (classification problems, CRM, web-builders, business apps). It is often hard to draw the line where one application ends and the other starts – especially when we look at AI applications. To get a clearer picture, we decided to take a deeper look into no-code AI players, and what they offer. The list below is in no way exhaustive, nor in any particular order (well... alphabetical), and we will keep on adding new players as they come – but bringing some structure into the landscape was a necessity.

What made the most sense to us was grouping based on core value proposition – we know that many of these companies are active in more than one scene. Leveraging the no-code movement to become a maker is fantastic – but we need to know what we want to create in the first place.

In a nutshell, we took the following criteria to qualify as no-code AI to heart:

• Tools that enable users to build solutions from scratch and integrate these into their processes - which would have previously required one or more (ML) engineers to build.
• Creates value on its own for users and companies of all sizes – and is not just an enterprise-level developer tool (think Uber's Ludwig).
• Usable by non-technical people – this is essentially the core of the no-code movement. More importantly, this is one of the criteria we had the longest debate on. The level of knowledge plays a key role - and while there are tools like MS Azure, C3 AI Suite, or even deepCognition - they are not built for the average knowledge worker, but for people who already know what they are doing in the developer stage.
• Finally, we considered the horizontal and vertical approaches of these tools: if you want to be well informed and updated on the no-code AI ecosystem, these are the tools you should probably keep an eye on.

When running this exercise, it made sense to group the tools by the technology they use. Here's a quick round of definitions:

• Computer Vision: Allows machines to obtain information from digital images, videos, pdfs, and other visual data, and take actions based on their learnings.
• NLP: This allows machines to understand and process language both spoken and written, for example, text messages.
• Predictive Analytics: This refers to predictive modeling based on structured (i.e tabular data), e.g. predicting churn rate, forecasting, and stock prices.

A simpler way for our non-technical folks to find the right fit is to think about what data you're looking to process. CV, NLP, and structured data analysis come with their own territory, but it's a safe bet that there is a tool out there that can cover most, if not all your process needs.

We also made a distinction between no-code tools and low-code tools. The no-code tools follow basic criteria: They are end-to-end tools that are usable without coding knowledge. In that sense, low-code tools are better suited if you have someone on your team who speaks data.

Last, but surely not least, we considered the tools' vertical vs horizontal focus. There are tools that are superior in very specific use cases - as that was what they were built for (e.g. Lobe is great if you're trying to play around with Machine Learning for personal use, or check out Rossum if you're mainly looking for document processing). If you're looking for a tool for one specific task, process, or team, stay left of the center on this map. If you're looking to build AI into multiple processes, or across your organization - tools with a broad use case focus are likely a better fit.

One last consideration: just like with any other software, you'll find no-code tools that are better suited for enterprise implementation: be it due to billing, roll-out effort, or requiring cross-team collaboration with your analytics/Data Science department. If time to value is the consideration, tools built for SMEs offer flexibility with less technical setup.

We'll take a brief look into a selection of these tools.

Aito

Aito is predictive analytics and NLP automation deployer. It is targeted toward RPA developers with a simple UI and APIs that integrate with many automation platforms. Aito focuses on tabular datasets (and some text data), but its core offering is its automated re-training system. Metrics such as automation rate, prediction errors, and monitoring accuracy are some of their built-in features.

Clarifai

Clarifai is an NLP and Computer Vision tool founded in 2013 that offers an end-to-end solution for modeling unstructured data for the entire AI lifecycle. Image, video, and text recognition solutions are built on an advanced ML platform and made easily accessible via API, device SDK, and on-premise. Boasts with accurate and detailed results with a fast API, they have some neat pre-trained models on offer (people, vehicles, and general detectors).

Crowd AI

Crowd AI is a no-code AI tool built on Computer Vision, with a focus on both images and video. Aimed for both technical and non-technical users, their use case focus lies mainly in the industrial space (e.g. vegetation management, or disaster responses).

Dataiku

Dataiku is an AI analytics tool aimed at data scientists to build business applications, with a focus on ML Ops & AI Ops. It's fairly simple to use if you're comfortable with data - and it has a pretty neat 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 predictive models and is powered by open-source algorithms and available in the cloud, on-premise, or as a fully-managed AI service.

Google AutoML

AutoML is the Google package star, and the tool works much the same way as CreateML – just on the cloud. The model package currently includes Sight (Vision and Video Intelligence, the latter in beta) and Language (NLP & Translation) as well as structured data (Tables) functions. AutoML overall manages to cover a lot of ground already in no-code – but once again, if you’re not a developer it’s hard to operationalize.

Levity

Levity focuses on image, text, and document classification and enables users to train custom models on their use-case-specific data – and is meant for businesses of any size. Custom models and flows also include a Human-in-the-Loop option, so users have full control, as the model asks for input where it is unsure – and will automatically learn from interactions. Levity focuses on providing an end-to-end solution that integrates with all the tools people use on a daily basis.

Lobe

Lobe, a product by Microsoft, offers image classification, with Object Detection, and data classification coming soon. Lobe is a free, private desktop application with a fair amount of pre-trained solutions (e.g. Emotional Reactions which allow your app to react to different expressions allowing people to send emoji reactions using just their faces).

MonkeyLearn

MonkeyLearn offers an all-in-one Text Analysis and data visualization studio, for unstructured text-based data to get topic, sentiment, intent, keywords, etc. Features include automatically tagging business data, visualizing actionable insights and trends, and simplification processes for both text classification and extraction. Integrates with Zendesk, RapidMinder, and Google products, with a whole bunch more coming soon. Also - in our humble opinion - one of the best blog resources out there when it comes to text analysis.

Nanonets

Nanonets fall into the Computer Vision domain - they have ready-to-use solutions for most common document types but offer a setup for custom models as well. One of their cooler solutions offers to build an ID card verification model for any country, format, or language – including perspective transformation, meaning models that can work with tilted or angled images.

Noogata

Noogata was founded in 2019 and is another predictive analytics tool worth taking a look at. Quick and easy to set up, it’s a good solution to customize your model and turn your decisions more data-driven.

ObviouslyAI

Obviously AI, founded in 2019 uses NLP processing to perform tasks on user-specific text data. Drag and drop your data as CSV or integrate with HubSpot, Salesforce, or MySQL (among others), pick your prediction column, and it'll auto-build a custom ML algorithm and you’ll end up with a prediction report. The platform is especially useful for SMEs, who are looking for a tool that chooses the right algorithm for their needs.

Pecan AI

Pecan AI is another predictive analytics tool that allows you to gain foresight and turn them into important metrics. Used by many data scientists, you can get actionable predictions in 14 days.

Primer

Primer is an out-of-the-box NLP model builder with powerful integrations and many pre-trained models ready to be used. If you're looking to visualize your model performance in one go, it's worth taking a closer look.

Roboflow

Roboflow is a Computer Vision-powered tool that allows you to train and deploy models for images, annotations, and videos. They support a wide variety of annotation formats, so the retraining process is very smooth.

Use cases for no-code AI

“What can I do with it?” is arguably the most common question in this space and there is a good reason for it: By definition, the primary user group of no-code AI consists of non-technical people. They may know a thing or two about AI but they are certainly not dealing with the subject on a daily basis, let alone code Neural Networks for a living.

As it turns out, the quickest way to grasp the usefulness of AI as part of business operations lies in studying a few use cases. That’s when the “aha” moment usually happens.

Note that some tools imply the use case by way of how they have been set up (e.g. for a specific industry or process) while others are meant to be trained by the users with their specific purposes. A few platforms offer both. And naturally, there are different application layers at play –  classification, tagging, detection, Data Extraction... the list goes on and on – and so do the possibilities.

Nevertheless, there are things to consider...

One of the myths in the no-code space is that if you want to get to the stage of any solution implementation, you have to lower your expectations. The days when we had to choose two out of three between fast/cheap/good are numbered, but expectations do have to be managed.

The current no-code AI space shows that each solution is intrinsically bounded to the design of the tool. Some practitioners point out that in some cases, it is important to remember that once you have developed an application on a platform, you are linked to that platform for as long as the application is running. In the context of a PoC, this is not a problem, but in the context of an application that is expected to last, things can be different.

And even though no-code platforms mitigate engineering and coding complexities, it is not a magic tool that can be used for everything. Instead, you should consider (as a process owner) some of the following questions:

• What problem am I trying to solve?
• What tasks make up for this problem?
• What is the level of project management that we need?
• What is the role of the tool/platform in the company architecture?
• Does the platform fit the problem needs?
• Is using a no-code AI tool a strategic choice that will drive value in the long run?

What will the future bring?

Businesses are steadily moving towards no-code platforms for a number of reasons. Partially due to the ripple effect on workforce management, access to developers and software engineers slows down project delivery – and this is where technology can add real value. Not only enabling your workforce to deliver solutions but also staying relevant and competitive in the current landscape is the unicorn we all want to catch.

Research shows that it is estimated that nearly 65% of application development will be done through low-code and no-code platforms as soon as 2024 – and no-code AI will play a significant part in this. It is hard to see the logic of doing things the traditional way when disruption of current process management is possible and widely available to everyone.

Nevertheless, useful AI applications require a good use case, to begin with. Just having an AI model is worth relatively little, regardless of how powerful it is. But just as people have found a new love for databases (thanks, Airtable!) and Wikis (Notion), people are going to pick up on the potential of AI. Just as no-code AI tools will mature, so will their users.

Thank you, Luc Meijer (@NoCodeLuc), Andrew Davison (@AndrewJDavison), Ryan Myher (@ryanmyher), and Bryce Vernon who runs a Slack Community for no-coders for your contributions to this!

This article has been written in collaboration with our amazing team members Arne Wolfewicz, Hanna Kleinings, Karen Rivera & Thilo Hüllmann.

Note: We aim for a complete overview of the field. If you are missing a tool, please let us know and we add it for the next iteration!

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