Building an app is not an easy process, each step corresponds to a feature by itself. For you and for the users.
From the website creation to the payment system you’ll use when converting users you can now outsource most of these features by integrating an API to your application.
If you’re not familiar with this term, see an API as a bridge between what you need to build and a pre-build version of it made by another application. It allows you to reuse an existing system for your use case without the need of building it from scratch.
However, when doing this you rely on a third party which is why when an application starts to grow and see real traction for the product it provides, founders starts to think about internalizing all the process they initially based on an API.
With the rapid evolution of no-code, automation, and AI it has never been that simple to build an application in a record time and for a few hundred bucks. Still, a question remains, how flexible are we in regard to building your own app features?
Relying on existing API is great, but building your own feature in no code using a flexible application and streamlining it is even better.
That’s exactly what’s happening with the rapid development of LLMs, you can streamline their capabilities with their API and build whatever you want on top.
Each process you’re building becomes a feature on its own. AI API is a new paradigm, one that’s completely reshaping the way apps are being built. Let’s see the role that Argil is playing in this new ecosystem and how you can make the most out of it.
The goal of every company is to be cost-effective. All major trade-offs are made in that regard, optimize all aspects of building a product to keep the output quality consistent but also decrease the marginal cost of production.
Using API has been a real game-changer in that regard, let’s say that to build one specific feature for your application you need another specific one. Instead of coding it you may use an existing API and streamline it to your internal app.
That’s how interconnected systems saw the day, you no longer need to build from scratch and you no longer need to think about scaling the infrastructure of your application early on in your development process.
You can just let the market push you through and decide further down the road whether or not scaling is required, and if it is, you won’t have to change your application straight away, using the API for a while is more than enough as you’ll pay more but that means you’re also paid more.
Product builders relying on APIs can now stand against giants of their market fist to fist and build something meaningful and useful that people are ready to pay for before stressing about the next steps.
Building an MVP has never been that easy with all the APIs ready to be used. Now AI API has pushed things further.
Being built on top of LLMs, ai applications have much more striking strength and flexibility than apps we used so far. It is now possible to modulate infinitely the capacities of the LLMs to fit the needs of a specific client, all of that form the same application.
This change is massive, when streamlining with AI API you open a completely new realm of possibilities for product building. Applications will outsource vertical feature development to ai applications, and then centralize those using AI API.
We’ve reached a breaking point in which cost-effective product development will benefit in all kinds of ways: speed, scalability, and innovation.
No, the recent development of AI is not simply due to hype. It got a lot to do with how OpenAI has invited end-user to try their product for free.
That move was massive and confirmed the potential of LLMs for interactive research usage by people from all industries, backgrounds, and professional roles.
This not only shows the potential of AI but also gives a little preview of how people could make the most out of it.
Having gathered data and consumer insights for the different use cases made on chatGPT, openAI released its AI API to let end-user integrate and optimize the LLM model to their application.
One can easily see the limitations by using a general model, AI API has accelerated AI deployment because it gave product builders the ability to put their hands in the roots of the model and optimize it accordingly to the feature they want to provide on top of it.
This is massive as you can outsource without even being aware of the development of a specific feature needing an AI model and then integrate the AI API into your application and streamline a working product for your audience.
The use of AI API in product development processes is transformative, the common way to develop products involved linear, and often time-consuming processes.
Now each step can be outsourced with AI API. You can automate tasks with multimodal ai workflows that were traditionally manual or required an internal code script, which made it challenging and inefficient.
AI API offers a unique opportunity for customization. By integrating various APIs into a product, you can customize the product according to the needs of your end-user which was not possible before.
API allowed integration of finite features, but AI API left room for personalization as they’re based on LLMs.
It’s now possible to provide a highly personalized experience at scale. This new approach not only enhances user satisfaction but also enables the development of more diverse and innovative products.
Gaining a competitive edge is crucial for businesses. AI API provides a new way to differentiate your product by enabling personalized customer service, optimization of operations, and giving a tool for data-driven decisions.
AI API is the enabler of technological innovations for people with different understandings and skills regarding AI. Developers can add advanced AI capabilities to their products without having a deep understanding of machine learning or AI.
This allows even small businesses or startups to create cutting-edge products.
If you don’t know yet what Argil is, let us give you some context. At Argil we’re building the first No-Code platform for multimodal ai automations.
Our goal is to be the agnostic tool that can be used in all industries to simplify, faster, and automate daunting processes.
This vision can be pushed a bit further as we also want to enable product builders, creative agencies, and consultants to build their own multimodal workflows and streamline them.
Let’s get through a full example of AI API in action to build a specific feature vertical:
Let’s say you’re currently building an app for community management and gamification.
The goal of your app is to let brands upload pictures of their products, design a specific customer Journey and make them interactively imagine tomorrow's product.
One of the features you need is the following:
Training of an LLM model to recognize the product and let the community generate images with it as a base.
On Argil, you can upload images of your model (Product, Person, Style, etc.) and train the AI over it. Every time you’ll use it, or call it, you’ll be able to generate it near pixel-perfect.
Having this feature natively will need months of work, but with our aAI API, you can train the ai on our platform and call integrate into your application the feature.
Personalization will become by-design a feature in every app. If you didn’t try argil yet, do it now, and don’t hesitate to give us a feedback
