## **The way you’ve built integrations is no longer enough**

Before AI, building integrations was a manual, brittle process.

Teams relied on a patchwork of iPaaS tools, custom-coded connectors, and outsourced agencies — each integration scoped and built one app at a time. The process was slow, repetitive, and operationally expensive:

- Read the docs
- Write API wrappers
- Map fields
- Handle auth
- Fix breakages across customer environments

That worked — barely — when you only needed a few integrations. But today, customers expect your product to connect to *whatever stack they already use*. Not just five tools. Not just major CRMs. Every tool in their workflow.

And as usage scales, so does variation: different schemas, edge cases, and customer-specific logic. The old model — one integration at a time — just can’t keep up.

To solve this in the last 5 years, many teams adopted **embedded iPaaS platforms** or **unified APIs**. While they abstracted away some of the grunt work, they introduced limitations of their own:

- Embedded iPaaS platforms still rely on rigid workflows and visual builders that struggle with edge cases and versioning.
- Unified APIs oversimplify real-world differences — they reduce surface area, but also reduce flexibility, locking you into the lowest common denominator.
- Neither gives you control over logic reuse, dynamic per-customer variations, or runtime orchestration.
- And neither was built to handle a world where **AI agents can be part of the integration building process**

---

## **AI is changing the integration process — not just accelerating it**

AI isn’t just a productivity tool for engineers. It changes the underlying assumptions about how integrations are created and maintained:

- It can help generate app- and customer-specific variants of core use cases
- It can adapt existing logic to new schemas or APIs
- It can debug issues or propose updates when things break


![Table (1).png](https://static.integration.app/Table_1_93205ad72e.png)

But to unlock this, your system needs to be structured in a way that **AI can participate meaningfully** in the integration lifecycle — not just generate snippets of code.

You need to move from handcrafted connectors to **reusable, composable logic** that AI can help you scale.

---

## **What you need: a system for scalable integrations**

If you want to support dozens (or hundreds) of integrations without growing your team linearly, you need a new kind of architecture. One built on the following principles:

### **1. Separate business logic from app-specific implementation**

Model the *intent* behind an integration (e.g., “create task”) separately from how it’s executed in Asana, Jira, or Linear.

### **2. Create reusable interfaces for common use cases**

Design for patterns like “sync contacts,” “log events,” or “export leads” — not for specific tools.

### **3. Dynamically generate per-app and per-customer implementations**

Let AI assist in adapting your logic to specific customer environments: auth methods, schemas, edge cases.

### **4. Support runtime execution, observability, and version control**

Integrations are not static assets — they need to be debuggable, testable, and upgradable in real time.

### **5. Make logic accessible to both humans and AI agents**

Expose integrations via APIs and standards (like MCP) so they can be triggered by people or autonomous systems alike.

This is what modern, scalable integration architecture looks like: not a collection of connectors, but an intelligent system for managing intent, variation, and execution at scale.

---

## **How Membrane enables this shift — so you don’t have to build it yourself**

Membrane is a universal layer designed for this new model of building integrations. It helps teams go from managing a few handcrafted integrations to orchestrating hundreds — without exploding complexity or headcount while incorporating AI into the way of shipping, maintaining and scaling integrations.

### **With Membrane, you can ->**

![Screenshot 2025-07-22 at 11.31.40.png](https://static.integration.app/Screenshot_2025_07_22_at_11_31_40_09dbe508ef.png) 

- **Define logic once** — model the core use case in code
- **Use AI to generate app- and customer-specific variants** — with full control over mapping, auth, and edge cases
- **Expose all logic via APIs** — everything is runtime-executable and observable
- **Operate at scale** — test, deploy, debug, and revise integrations quickly
- **Get your AI agent product to integrate with apps** — Membrane is MCP-compatible, so AI tools can trigger and adapt integrations directly

---

## **The next generation of integration teams will stop building connectors — and start designing systems**

Moving to an AI-first integration system isn’t just about adopting new tools — it’s about unlocking a fundamentally better way to scale. 

With AI assisting in code generation, schema mapping, and edge case handling, your team can support far more integrations without growing headcount. You go beyond the top 5–10 tools and cover the long tail of customer needs — faster and with less effort. AI-first systems adapt to customer-specific variations dynamically, reducing manual overhead and making it easier to iterate. More importantly, they prepare your infrastructure for what’s next: AI agents triggering workflows, adapting integrations at runtime, and operating against standards like MCP. 

By rethinking your integration system around AI from the ground up, you turn what used to be a bottleneck into scalable infrastructure — faster to ship, easier to maintain, and more powerful for both your team and your users.

The good news? you don’t need to reinvent the wheel. With Membrane, you can adopt the architectural shift now — moving from simple connectors to a flexible, intelligent system designed to scale with your team, your customers, and your AI roadmap.

Check out more about Membrane and how it can be used: https://integration.app/membrane

Before AI, building integrations was a manual process. We're diving in how you should think about a future-proof system for building, managing and scaling 100s of customer-facing integrations in the AI age.

The new system for building customer-facing integrations in the age of AI

Over the last few months, we’ve seen that the biggest barrier to realizing AI’s potential often isn’t the model itself—it’s how you integrate that model with your existing stack. 

Connecting AI models to the tools and systems businesses rely on can feel like solving the same problem over and over again. It’s frustrating, time-consuming, and it limits what these models can achieve. That’s why Anthropic’s Model Context Protocol (MCP) caught our attention.

## What is MCP by Anthropic?

MCP is an open protocol that creates a consistent framework for AI models to interact securely with both local and remote resources. 

It eliminates the need for scattered APIs or custom scripts by streamlining tasks like accessing files, connecting to databases, or integrating with external APIs. MCP is built to be scalable, reliable, and—most importantly—efficient, paving the way for AI models to leverage diverse resources in a unified manner.

## What is an MCP Server?

An MCP server is a specialized server that implements the Model Context Protocol. It serves as a bridge between AI models and the vast ecosystem of tools and data sources available to businesses.

In simple terms, the MCP server translates requests from AI models into standardized actions—such as accessing files, querying databases, or triggering API calls—ensuring that integrations are secure, consistent, and efficient.

By doing so, it removes the traditional challenges of custom integration work, enabling developers to connect AI models to numerous systems without reinventing the wheel each time.

## The Integration App MCP Server

![3.png](https://static.integration.app/3_f48a485776.png)
Integration App is built on the same open technologies as MCP so they are fully compatible.

We made a simple MCP server that gives LLM access to the full power of our whole database of universal connectors and integrations you build on top.

As a result, LLMs can connect to tens of of thousands of tools across hundreds of apps and to hundreds of sources of knowledge in a consistent way

For developers, this means building and testing MCP servers is no longer a challenge. 

Whether it’s linking to a single database or managing integrations across multiple apps, Integration App transforms what was once a bottleneck into a straightforward process.

### How It Works


- **Unified Access to Resources**:
The MCP server acts as a gateway, allowing LLMs to connect to tens of thousands of tools across hundreds of apps and numerous sources of knowledge—all in a consistent way. For developers, this means that whether you’re linking to a single database or managing integrations across multiple applications, the complexities of integration become a streamlined process.

- **Simplifying Development**:
Traditional approaches often require custom scripts or scattered APIs to connect AI models with your existing stack. With our MCP server, these challenges disappear. Developers can build and test MCP integrations with ease, reducing what was once a significant bottleneck into a straightforward workflow. Check out our documentation on[ model context protocol usage](https://console.integration.app/docs/use-cases/ai/mcp) and using [tools](https://console.integration.app/docs/use-cases/ai/use-tools) for more details.

- **Proven and Trusted**:
Our server is recognized as part of Anthropic’s official third-party integrations, as seen on the Anthropic MCP integrations page. 

![Screenshot 2025-02-12 at 19.22.11.png](https://static.integration.app/Screenshot_2025_02_12_at_19_22_11_bd67c37a7b.png)

Companies like **Clay, Apollo**, and others have already started building on top of Anthropic’s MCP, harnessing the power of this new paradigm to expand their AI’s capabilities.



## Why This Matters

Integrating AI into existing systems should not be a perpetual struggle. With Anthropic’s MCP and our MCP server, you have the tools to bridge the gap between advanced AI models and the practical applications businesses rely on every day. This technology isn’t just a new way to connect; it’s a revolution in how we think about digital integrations. Quite exciting, no? 

## Anthropic’s Event in Paris: 3 Key Takeaways

![IMG_8351.jpg](https://static.integration.app/IMG_8351_0b8622368c.jpg)

Our **Head of Growth, Horia** had the opportunity to witness Anthropic’s builder event in Paris—a gathering that underscored the significance of AI Agents, future Anthropic models and how MCP plays out its potential impact on the future of digital integration shared by their **co-founder & CEO, Dario Amodei.**

Here are the three main takeaways:

- **The TCP/IP of the AI world**:
Just as TCP/IP revolutionized how computers communicate, MCP is set to transform the way AI models interact with disparate systems, creating a universal language for integration.

- **A Growing Community**:
The ecosystem around MCP is rapidly expanding. More developers, businesses, and innovators are joining forces to build upon this protocol, enhancing its robustness and versatility every day. The set of Github repos https://github.com/modelcontextprotocol/servers has already hit 8.6.k stars and 1k forks in relatively short amount of time & it's only set to expand from here.

- **We’re All In**:
At Integration App, we’re proud to be part of this movement. Our commitment to open, scalable, and efficient integration solutions aligns perfectly with the ethos of MCP, and we’re excited to help lead the charge into this new era.

![IMG_8356.jpg](https://static.integration.app/IMG_8356_bbad18d0e0.jpg)

## Conclusion

Realizing the full potential of AI means overcoming integration challenges. 

By leveraging Anthropic’s Model Context Protocol and the Integration App MCP server, developers and businesses can simplify connections to hundreds of systems and unlock a unified, efficient approach to AI integration. If you’re considering using this transformative technology to connect your SaaS apps and internal tools, Integration App is ready to help you get there.

For more details on our MCP server, check out our **[GitHub repository](https://github.com/integration-app/mcp-server)** and explore the comprehensive documentation available on our [console](https://console.integration.app/docs/use-cases/ai/mcp) and [tools guide](https://console.integration.app/docs/use-cases/ai/use-tools).

How AI interacts with other tools is rapidly changing. We are building on top of Anthropic's MCP to allow more connectivity to AI Agents. Find out more here.

Anthropic MCP and Integration App

Integrating AI’s Future: Anthropic's MCP and Integration App

This blog is not like “In this blog, we’ll help you to understand differences between Unified API vs embedded iPaaS” but instead:

We’ll explain it like never explained before..

## What is a unified API?

Probably you think unified API is easy to understand “ One API to build all APIs”. But actually, it is one aggregated API to connect to multiple 3rd-party providers **in the same software category**. So it can’t connect your data for CRM and Accounting tools at once.

:::zoom[https://static.integration.app/Group_1171275143_2_08b479a290.png]

![Unified API vs embedded iPaaS](https://static.integration.app/Group_1171275143_2_08b479a290.png)
:::

## What is Embedded IPaaS

Embedded integration platforms are built for product teams that need to offer their customers integrations with business tools. So what? Alright, let's break down the concept of "Embedded iPaaS”

Imagine you're building a complex Lego set. Instead of searching for individual pieces in different boxes (which can be quite a hassle and time-consuming), you have a special box that already contains exactly the pieces you need, and it even comes with a guide on how to fit those pieces into your current set seamlessly. This special box is like an Embedded iPaaS in the world of software applications.

### What is iPaaS? 

Firstly, iPaaS is a cloud-based platform that helps different software applications "talk" to each other. It's like a translator or a middleman that allows two apps to share data and functions, even if they were not originally designed to do so. This is crucial for businesses that use multiple software tools and want them to work together harmonically.

### The "Embedded" Part

Now, when we say "Embedded iPaaS," we're talking about integrating this iPaaS functionality directly inside another application. Instead of going to a separate platform to manage these connections and data flows between apps, you can do it from within one of the apps itself. It's embedded, meaning it's tucked into the software just like embedding a video directly onto a webpage instead of linking to it externally.

## unified API vs embedded iPaaS

Unified APIs standardize data and functionality of distinct systems into one API but are limited to common features across apps and may not handle non-standard operations well. They do not offer a fully built integration stack and often require users to build most of the integration themselves.

On the other hand, embedded iPaaS like Tray Embedded and Paragon offer a low-code integration builder for developers/support teams with some abilities to embed it inside your app and add some configuration UI. However, with these systems, you typically need to build every integration flow for every external app, which becomes untenable quite fast. They are not a part of your application, just iframes embedded in it, which does not feel like a part of your app.

### iPaaS: A New Era Beyond Workato, Make, Zapier

Traditional integration platforms like Workato, Make, and Zapier have set the stage for the first wave of integration solutions. Yet, they carry certain drawbacks. Mainly, they demand that users start their integration projects from zero on a separate platform. This disrupts the overall user experience and introduces extra complexity.

On the other hand, Integration.app brings a more intuitive method that integrates directly within your app. Our approach is straightforward: why push your users out of their familiar environment when you can deliver integrations right to where they are? By embedding our integrations within your application, we remove the need to switch to external platforms, providing a smoother and more unified user experience.

### Embedded iPaaS: Moving Beyond Tray.io, Paragon, Prismatic

Embedded iPaaS options, such as Tray.io, Paragon, and Prismatic, have tried to overcome the challenges posed by traditional iPaaS by integrating directly within applications. Yet, they tend to use iframes, which can disrupt the app's natural look and feel. Furthermore, when you need to integrate with several apps, the process doesn’t scale well since each integration demands its own setup and maintenance.

Integration.app goes beyond these barriers by delivering an integration experience that truly feels like part of your app, both in appearance and in code. With our API-first mindset and cutting-edge AI technology, we can automatically create similar integrations, drastically cutting down the time and effort needed to expand across many applications. We envision a future where integration is not just an add-on but a natural extension of your product.

:::zoom[https://static.integration.app/Copy_of_reddit_ads_Blog_Banner_3_1e26e7ef05.png]

![Unified API and embedded iPaaS ](https://static.integration.app/Copy_of_reddit_ads_Blog_Banner_3_1e26e7ef05.png)

:::

### Unified APIs: Merge, Apideck

Unified API services like Nango, Merge, and Apideck offer a consolidated API to handle the basic functions of various apps. But they struggle with more complex, unique tasks. For example, if you want to use custom fields created by your users in external application, unified APIs can’t help you. Their general-purpose API often misses out on leveraging each application's distinctive features.

In contrast, Integration.app is much more than just an API. We offer a comprehensive set of integration tools and infrastructure customized for your specific integration needs. 

From managing everyday tasks to tackling the special features of each app, Integration.app covers it all without a hitch. 

We enable you to create and manage complex integration workflows, customize settings for your customers, and solve problems efficiently. Unlike many unified API providers based in the U.S., facing time zone challenges and limited working overlap, Integration.app's global team offers 7/24 support. Moreover, we address another common issue with unified API services. Most unified API companies are located in USA and then don’t offer data hosting in Europe, which we fully support.

## Combining flexibility of iPaaS with simplicity of Unified APIs

While Unified API solutions provide a solid foundation for cross-platform integration, Integration.app builds on this by offering a more adaptable, developer-centric, and scalable approach. Its emphasis on customization, AI-powered efficiency, and global support makes it an attractive option for businesses looking to harness the full potential of their integrations. In essence, Integration.app is not just an alternative to unified APIs; it's a comprehensive solution designed to meet the evolving needs of modern businesses in a digitally interconnected landscape.

### Conclusion

At Integration.app, we're not just participating in the evolution of SaaS integrations; we're leading it. Learn how we help organizations — like Airmeet, Glyphic, Potion and many more to build integrations, save lots of resources, maintain API lifecycle and more by [scheduling a demo](https://integration.app/book-a-demo?utm_source=inbound&utm_medium=embedded-vs-ipaas-article) with one of our integration experts. You can also test our platform by [creating a free account](https://console.integration.app/login).

Probably you think unified API is easy to understand “ One API to build all APIs”. But actually, it can’t connect your data for CRM and Accounting tools at once.

Unified API vs embedded iPaaS

Today’s SaaS solutions are built around the API economy, an ecosystem of business models and practices rooted in APIs from organizations across various verticals. Thanks to the API economy, it’s never been easier to integrate your app with third-party apps.

If you need to add functionality to your app, **there is an app for that**. Today, you can enable your users – both developers and non-technical – to choose how they want to integrate your product with other apps they might be using.

It’s easy to take these integration capabilities for granted. However, to truly appreciate how we got here and where integrations are headed – hint: it involves AI – it’s essential to understand how and why things evolved the way they did.

## History of REST API

Let's rewind to the late 1990s when the Internet was emerging and the API landscape was in its infancy. Inter-app communication frameworks like [SOAP](https://en.wikipedia.org/wiki/SOAP) and [CORBA](https://en.wikipedia.org/wiki/Common_Object_Request_Broker_Architecture) exchanged data but were complex, lengthy, and often challenging to grasp.

The API landscape changed around 2000 when computer scientist [Roy Fielding](https://en.wikipedia.org/wiki/Roy_Fielding) and his colleagues realized they could build a more straightforward and standardized solution on the Internet’s primary protocol, HTTP. Thus, the [Representational State Transfer](https://en.wikipedia.org/wiki/REST) (REST) architecture was born. REST APIs revolutionized things by distilling operations to a core set of [CRUD](https://en.wikipedia.org/wiki/Create,_read,_update_and_delete) requests, including POST, PUT, GET, and DELETE. Companies like eBay quickly jumped on the technology as they embraced the new e-commerce frontier. Thus, the era of app integrations had truly begun.

From the mid-2000s until approximately 2014, REST APIs facilitated diverse interconnections. From online payment processing to the generation of support tickets and everything in between, developers harnessed this newfound technology to consume and share services with third-party entities conveniently.  But just as the early pre-REST frameworks had their challenges, so did these integrations. Each integration was a DIY affair. Developers had to learn each third-party app’s API and create and maintain app-specific integration. And with the growth of SaaS, new integrations had to be made for new customers. It was all becoming much more challenging work for developers, but with new problems come new opportunities.

## iPaaS or Integration Platform as a Service

By around 2014, companies were integrating with multiple apps, often building separate integrations for the same app to handle different scenarios.

**Integration as a platform as a service** (iPaaS) providers soon sprang up after seeing an opportunity to take over the burden of integrations and offer them as a service. This meant companies could now pay for a subscription to pre-built integrations rather than invest vast amounts into DIY integrations. iPaaS providers not only took over the mechanics of integrations but also provided additional functionality (e.g., workflow design, enforcing data standards, support for different protocols, customizations, etc.).

In recognizing the hard work developers were putting in, [MuleSoft](https://www.mulesoft.com/), a middleware and messaging company whose name was based on eliminating the **donkey work**, expanded into the iPaaS market. They became one of the early and prominent players in the iPaaS domain. They furnished app networks with pre-built APIs for diverse systems and a bus-like communication platform to facilitate interconnectivity. They also empowered developers to design APIs, dispensed libraries to share APIs, and introduced a console for integration management.

The API integration history landscape was also changed by [Zapier](https://zapier.com/), who shifted views on how automation could be achieved via integrations. Zapier effectively democratized iPaaS by being a no-code solution. For the first time, non-technical users could connect to apps and build integrations – a task formerly relegated to developers. Zapier built on its success by treating each integration as an official release and highlighting its value to its customers. They also made it easy to create multi-step workflows with two or more app integrations, and developers could start for free.

However, iPaaS offerings weren’t the be-all, end-all solution. They often lacked the functionality to handle complex use cases and still had scalability issues. For example, several integrations for an app were still required for different scenarios, a problem magnified when an app has to  integrate with dozens or even hundreds of other apps. 

At the same time, companies started looking beyond their internal integrations and how they could add integration functionality into their products. Through such embedded integrations, they could enable their customers to choose/configure how the product integrates with the third-party apps they use.

## API Integration and iPaaS products

The concept of embedding iPaaS into products, termed embedded iPaaS, held great promise. This approach enabled companies to extend the power and flexibility of integrations to their clients, not only enhancing the capabilities of their products but potentially prolonging their lifecycle.

One of the challenges in API history was offering an embedded integration solution facilitating huge integrations and dealing with associated complexities like account management and large-scale customizations.

Around 2019, [Tray.io](https://tray.io/), an established iPaaS provider, showed they could leverage their existing integration technology (e.g., their vast collections of connectors, workflow builder UI, etc.) and offer an embeddable solution. The result was [Tray Embedded](https://tray.io/solutions/embedded), and as the company [states](https://tray.io/blog/the-new-tray-embedded), it put **“...the power of integration into the hands of end-users, via in-product, self-service experiences”**. Additionally, their low-code solution offered a configurable white-label user interface, making the integration capabilities appear like native features seamlessly integrated into the application.

But as with iPaaS, embedded iPaaS solutions were generally best suited for connecting to only a few apps. They still needed to catch up regarding scalability, such as integrating with large numbers (e.g., whole categories) of apps, especially for complex integrations. Also, many of the no-code/low-code solutions lacked features favored by developers, including collaboration, version control, and various testing facilities.

## Unified APIs history

Abstracting complexities to create a more user-friendly interface is a longstanding practice and a primary function of APIs. Around 2020, the concept of unified APIs gained traction. Unified APIs take abstraction to the next level by amalgamating the functionalities of distinct systems into a single, unified API.

The key player here was **Merge.dev**, who saw unified APIs as a way for developers to build several integrations of a similar type (e.g., CRM, HR, etc.). Merge emphasized the standardization of data, enabling developers to access it consistently. They also unified the authentication and authorization mechanisms across systems through a common token-exchange system. In a broader sense, Merge integrated numerous advancements from iPaaS and embedded iPaaS solutions into an alternative integration solution geared toward developers.

However, things continued to evolve. New players came onto the scene and questioned if there was a way to build a **universal** approach to integrations, one that could combine the best of iPaaS, embedded iPaaS, and unified APIs.

## Universal Integration platforms - future of API

Built on combining the benefits (while addressing the drawbacks) of iPaaS, embedded iPaaS, and unified APIs, *universal integration platforms* emerged around 2022 and continue to represent state-of-the-art integration technology.

As a universal integration platform, Integration.app eliminates the need to devise and implement separate integrations. Instead, it focuses on integration planning. The concept of abstraction is built on by allowing more granular abstractions. Suppose your integration revolves around tasks, so you can design it around the concept of a task, its requisite fields, and the operations it requires. Subsequently, our AI-powered platform generates application-specific yet customizable code, enabling the scalability of integrations. Of course, there’s more to it than that, so we recommend reading [Emergence of Universal Integrations: how SaaS build customer-facing integrations in 2023](https://integration.app/articles/future-of-integrations/emergence-of-universal-integrations) for more information.

# Integrate Today!

Integration technology has come a long way, and each generation has brought new advancements, opportunities, and problems to solve. Here at Integration.app, we believe that universal integration platforms are the future of integrations because they combine the best of iPaaS, Embedded iPaaS, and unified APIs.

Ready to learn more? Visit our [website](https://integration.app/) and book a demo. Then, check out our [documentation](https://console.integration.app/docs/overview) to see how easy it is to start.

Discover the evolution of APIs from REST to AI integration. Learn about iPaaS, embedded integrations, and the future of universal integration platforms 

API history from REST to AI

Hello everyone!

I'm thrilled to share some great news with you all.

**Integration.app has successfully secured $3.5 million in seed funding**! This isn’t just a financial achievement, but a resounding vote of confidence in our mission to change how integrations are being built by modern SaaS companies. 

Leading the charge in this round were **Crew Capital** and **Cortical Ventures** with participation from **Seedcamp**, **Accel Starters**, **DataRobot** and **UiPath** founders & executives.

## The Revolution We’re Building

In the vibrant digital landscape where the average company leverages 371 SaaS applications (according to[ Productiv](https://productiv.com/state-of-saas-trends/#growth)), the need for seamless integrations is crucial for winning customers. The on-ground reality has often been one of developers stretched thin, toggling between creating core features and crafting essential integrations. Our vision with Integration.app is to remedy this. 

Existing solutions to this problem have historically been categorized between unified API providers and embedded iPaaS offerings. 

Unified API providers like Merge.dev allow for quick integrations, but limited customizability. iPaaS vendors' embedded integrations like Tray.io are customizable, but have long implementation cycles that are untenable and costly for fast moving developer teams.  


Our **Universal Integrations** architecture is removing the trade-off between time-to-implementation and customizability.

We leverage emerging capabilities of Large Language Models to create a “one-to-many” paradigm, whereby developers can configure one integration scenario (e.g. import customer data from any app) and Integration.app will intelligently map APIs and UIs across multiple applications to generate dozens of integrations at once, speeding time to production by up to 3-6 months. 

This gives developers back their time to focus on building core features, rather than spending hours reading API documentation and building repetitive integrations.

![blog_funding_pic.jpeg](https://static.integration.app/blog_funding_pic_e6fa8e524a.jpeg)

## Who's Riding the Wave with Us?

Our vision isn't a distant dream; it's today's reality. From agile startups to industry leaders like Airmeet, companies are harnessing our platform to redefine their integration strategies.

**Airmeet** saved budget and engineering time by using us to scale their portfolio of integrations with Sales & Marketing Automation tools. 

![Frame 12.jpg](https://static.integration.app/Frame_12_d7dc423426.jpg)

SaaS vendors that use our product save time and money on building integrations, while shortening sales cycles from prospective customers asking for specific integrations to the applications they care about. Our product can used by any engineer in a team, which allows for efficient rotation of resources no matter how large the company is. 

This allows teams to move more nimbly with all their other core engineering priorities, instead of dedicating & locking team members on narrowly-focused integrations projects.

## What’s next?
We are actively working on expanding our existing portfolio of hundreds of connectors later this year. Lots of behind-the-scenes work for myself & the product team on the new product that leverages LLMs to enable developers to build their own custom connector for any application with a documented API. 

The first alpha users who tried the Connector Builder are blown way by its capabilities, we had a customer build a fully-mapped connector for a cloud ERP system in 20 mins, which is a great validation for us. More to come here soon!

## Peeking into the Future of Integrations

I think that 5 years from now, integrations will become a commodity. AI will make it simple and free for a developer of a business application to connect with every other application their customers use. Think of how easily it is for smartphone apps to consume and send data across each other. This should be the same for business apps. We want to provide this deep connectivity layer and pave the way to this future for the next generation of SaaS companies. 

This funding round is the first step in making Integration.app closer to make this vision into reality and I’m excited to have a great team around me to make this happen!

## A Word from Our Backers

We are lucky to have as part of our board some amazing investors with years of experience in scaling SaaS & AI startups: 

![Frame 20 (1).jpg](https://static.integration.app/Frame_20_1_724169d5df.jpg)

## Join Us on this Exciting Journey

As we embark on this next chapter, I invite you to explore our [connectors library](https://integration.app/connectors), product. Whether you're seeking integrations solutions or just curious about our journey  shoot us an email at hello@integration.app or [book a demo](https://calendly.com/integration-app/30-min-demo) on our website. 


Our CEO Daniil talks about announcing Integration.app's seed round

Announcing our $3.5m seed fundraise with experts in AI & SaaS automation

AI is changing how engineering teams build product integrations. If you are building a SaaS product, you should know what is happening. 

Better integrations help you convert more prospects into customers and keep them longer. Research consistently show that integrations are [becoming #1 criteria](https://chiefmartec.com/2020/09/integration-important-criteria-selecting-martech/) when choosing or switching business apps. Best SaaS products have hundreds of integrations, which becomes a major component of their competitive moat. 

Large language models are about to disrupt this moat, and democratize the integrations landscape. 

## Before: Point-to-Point Integrations

Since popularization of APIs 15-20 years ago, all native integrations were built in the same way:

1. Prioritize apps you need to integrate with.
2. Pick the next one on the list.
3. Research application, its API, and design the integration logic and UI.
4. Code and test integration logic and UI.
5. Go to step 2.

Depending on the complexity of the integration, it could take from days to months of engineering time to build each one. 

Let’s say your app integrates with project management apps. Your integration development process would look something like this:

![Point to point integrations](https://static.integration.app/universal_integrations_1_7e72492588.png)

While researching and designing an integration is somewhat creative, the rest of the steps are pretty repetitive and tedious: you need to learn how to get access to a given API, how map its data structures to yours, and how to perform operations you need to perform.

As a result, building integrations is often delegated to junior engineers or third-party agencies, which further increases time to production and decreases quality.

But here come Large Language Models to completely revolutionize the process.

## LLMs and integrations

There is an ongoing debate on how exactly AI and LLMs in particular will change the world, but one thing that LLMs are clearly good at is finding and using patterns. 

Implementing integrations is 95% finding and applying patterns.

How do I get a list of tasks from a JIRA or Asana API? How do I map one set of fields to another? LLMs are great at answering this type of questions. All you need to do now is ask the right questions, which is the fun part of building integrations.

If used right, AI speeds up each integration development cycle by 90% of more. Now your process looks like this:

![Automated integrations](https://static.integration.app/universal_integrations_2_1f15499737.png)

This would be great on its own, but with a small change in mindset you can take it to the next level. Enter Universal Integrations.

## Universal integrations

What if instead of planning each integration separately, you planned each **type** of integration once, and implemented it for every application it applies to?

If you want your application to create a task in external application, instead of thinking “how should it create a task in JIRA?” and “how should it create a task in Asana?”, think “how should it create a task in any application that has tasks?”. 

![Universal integrations thinking](https://static.integration.app/universal_integrations_3_cf7b50091d.png)

AI lets you scale this thinking to all the relevant apps automatically by generating application-specific code and data mappings, and the right integration framework lets you verify that whatever AI came up with actually works. 

Thinking this way is harder to figure out because you need to keep in mind the range of possibilities (how tasks can be organized, which fields may be required, etc), but it is worth it. After switching to universal integrations mindset, you can build dozens or even hundreds of integrations in one cycle. 

![Integrate with every app in each cycle](https://static.integration.app/universal_integrations_4_afad715d97.png)

Most SaaS applications have a relatively small number of integration scenarios applied to a large number of external applications. They have the same integrations with all CRMs, all HR systems, all Project Management apps, etc. 

The ability to build an integration for the whole category of applications at once radically changes how you think about integrations. Instead of thinking “what is the next app I want to integrate with?” you think “what is the next integration feature I want to add for every app we integrate with?”. It lets you iterate on your whole integration portfolio 10-100x faster than before. 

## How to start building universal integrations

If you have good enough engineering team, you can try to build the next integration this way: select not one but a dozen apps and ask them to build the feature that works with all of them at once, using AI to generate application-specific code. OpenAI APIs are cheap and simple to start.

Alternatively, try out [integration.app](http://integration.app) - an AI-powered integration stack I’m working on. It is built around the concept of universal integrations and dozens of our customers successfully built integrations that work across whole categories of apps. We are still in invite-only mode, but if you have an interesting use case, we’d love to get you on board and level up the way you build integrations for your app.

AI is changing how engineering teams build product integrations. If you are building a SaaS product, you should know what is happening. 

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