Flowgear is a globally recognized Integration Platform as a Service (iPaaS) trusted by businesses of all sizes to streamline data and app-to-app interactions, without the complexity of traditional tools. Pulse 2.0 interviewed Flowgear co-founder and CEO Daniel Chilcott to learn more.
Daniel Chilcott’s Background
When asked about his background and the experiences that led him to establish Flowgear, Chilcott shared:
I was an early employee at a software business creating custom software. Over time, that evolved into a CRM product, and whenever we sold it, customers wanted to integrate it into their other applications.
I found myself creating a lot of the same integration boilerplate over and over and realized it would be better to create a platform where the shared pieces were reusable. I built a lightweight on-premises integration platform, and a few years later, co-founded Flowgear, where we rebuilt that idea as a cloud platform.
How Flowgear Started
When discussing how the idea for Flowgear came together, Chilcott explained:
Flowgear came directly out of that experience. We saw that integration work was essential, but much of it was repetitive, fragile, and too dependent on custom development.
Businesses needed a way to connect their systems without starting from scratch every time. The idea was to create a platform that made integrations faster to build, easier to maintain, and reusable across teams and customers.
U.S. Growth Plans
When asked about Flowgear’s plans for growing in the U.S. market, Chilcott outlined:
The U.S. is a major market for us because so many companies are dealing with exactly the problems Flowgear was built to solve: growing SaaS adoption, ERP modernization, data silos, and now the need to apply AI safely in real business workflows.
We already have a U.S. headquarters in Atlanta, and our goal is to expand through direct customer growth and partner-led delivery.
A lot of American businesses are trying to understand where AI can create value without introducing unacceptable risk, and integration is a big part of that.
Becoming An Operational Backbone
When asked about his most memorable experiences working at Flowgear, Chilcott reflected:
What stands out most is seeing customers turn Flowgear into part of their operating backbone. Integration is often invisible when it works, but it is business-critical.
When a customer tells us that a process that used to require substantial manual work now runs automatically, or that a small team can own integrations that previously required ongoing consulting, that is very rewarding.
Core Products And Features
When describing Flowgear’s core products and features, Chilcott detailed:
Flowgear is an integration platform that helps teams build, run, and govern workflows across cloud, hybrid, and on-premises systems.
The core features include a visual workflow designer, prebuilt connectors, API publishing, scheduled and event-based workflows, release management, environment control, diagnostics, and support for both technical and non-technical builders.
More recently, we launched a new Runtime and AI-assisted workflow experience, which helps users build, test, troubleshoot, and refine integrations faster.
Removing Operational Friction
When asked about challenges in the enterprise integration sector and how Flowgear has addressed them, Chilcott noted:
There are many, but one challenge is that everyone now expects AI to make work simpler. However, AI does not remove the underlying need for good integration.
Systems usually show their weaknesses at the edges: duplicated work, manual handoffs, slow reporting, and teams creating workarounds outside the official process.
Our approach is to get close to those operational problems, understand the real source of friction, and then use Flowgear to remove the highest-pain manual steps first.
Technology Evolution
When discussing how Flowgear’s technology has evolved since launching, Chilcott described:
Historically, Flowgear made it easier for non-developers to build integrations. In the early days, that was framed around visual design, and later it became low-code and no-code.
Today, AI creates a new unlock for the same audience: people who understand the domain deeply but are not necessarily developers.
We’ve rebuilt our platform to take advantage of agentic AI, and the result is that experts in a domain are able to build designs far beyond their technical expertise.
The key tension we balance is enabling customers to take advantage of this while maintaining appropriate governance and safety guardrails in the platform.
Rebuilding The Platform
When asked about Flowgear’s most significant milestones, Chilcott recalled:
The most significant milestone for us has been the complete platform rebuild. We happened to start this work around the time ChatGPT arrived.
I’d love to take credit for foresight, but in truth, the timing was perfect, as we were able to make many architectural decisions that helped the platform take advantage of conversational AI first and agentic AI later.
That is very hard to do well as a retrofit.
TreasuryONE Automation
When invited to share a specific customer success story, Chilcott highlighted:
One example is TreasuryONE, which uses Flowgear as an automation backbone for treasury workflows.
The company estimates that Flowgear saves at least 5,000 hours per month compared with manual processing, and it has built hundreds of workflows across many different areas.
Customer Growth
When asked about Flowgear’s recent customer growth, Chilcott observed:
We do not disclose detailed revenue figures publicly, but demand has been increasing as companies move from isolated automation projects toward more strategic integration programs.
The trend we are seeing is that customers are treating integration as a long-term layer of their business, not a one-off technical project.
That is especially true now that AI is entering everyday workflows because AI needs reliable, governed access to business systems to be useful.
A key area of growth for us has also been customers starting to build applications that straddle business processes with multiple underlying applications. They’re using Flowgear as the mediation layer to achieve that.
This is relatively new, and it’s a high-growth area because once a customer has built an application, they quickly realize there are other areas where they can apply the same technique.
The Gap Behind Builder MCP
When asked what gap in enterprise integration capabilities inspired the launch of Flowgear’s Builder MCP, Chilcott clarified:
Generally speaking, the bottleneck for getting value from AI is not so much the raw capability of the model anymore as its ability to directly interact with the systems and applications that hold the data it needs to perform a task.
For example, in broad, high-value knowledge work, AI performs best when it can work directly with the data needed for a task instead of a user having to copy and paste data to and from it.
This is why the AI labs have put a lot of effort into supporting spreadsheets directly and enabling general computer use, where AI can take a screenshot of a browser window and determine where to click or enter text.
The reason this integration is important is that by taking the human out of the loop, the AI is able to iterate quickly. On a complex task, it is usually not going to be right the first time. It needs to check data, make a change, and then assess the result.
The unlock comes from having it run those cycles without a person acting as an intermediary.
How MCP Works
When describing the role of the Model Context Protocol in connecting AI agents with applications and services, Chilcott continued:
MCP is a standard that provides a way for an AI agent to interact with an application or service. It is actually quite similar to how APIs give programs the ability to interact with other programs.
Basically, with MCP, the application says to the agent: “Here are the things I can do for you, and here is how you use them.” Essentially, it provides a set of tools, the information required to use each tool, and the information the application will return.
Building And Testing Workflows With AI
When discussing how Flowgear’s Builder MCP enables agents to create integrations, Chilcott elaborated:
Flowgear’s Builder MCP lets an agent directly build and test workflows by providing the same set of tools that a user would have if they were building manually.
For example, it can see a list of connectors and the templates available for them. It can then piece together sections of integration workflows and run them to test them.
That last part is important because it creates a feedback loop that can be automated.
The point of Builder MCP is to let an AI agent use the Flowgear platform in a very direct way.
Making Integration More Accessible
When describing the expected impact of Builder MCP, Chilcott emphasized:
Two things will happen as a result of this.
First, a larger audience will be able to create these integrations. The platform becomes more accessible because users’ AI agents are able to access it from wherever it needs to be used.
Second, customers can use it to create integrations that are a means to an end. For example, a user could be creating an application that needs to pull data from an ERP system and combine it with data from a CRM system.
Flowgear Builder MCP means the AI agent can build a workflow to express that process rather than coding the integration directly into the application.
That is much faster because all the building blocks are already there, and it is much safer because the platform applies guardrails to protect against the risks inherent in agentic coding.
Supporting The Customer’s Preferred Agent
When summarizing the broader goal behind Builder MCP, Chilcott concluded:
That is the gap being closed. We don’t want to limit our customers to using the AI agent we have built into the platform.
We want them to be able to use the agent of their choice, from the application of their choice, to work with Flowgear.
