When we founded Ambar, we had a singular vision: create the most reliable, developer-friendly data streaming platform on the market. We were tired of seeing brilliant engineering teams waste months wrestling with Kafka configurations or building homegrown solutions full of edge cases and reliability issues. Our solution—a robust streaming service with end-to-end correctness guarantees.

But as time went on, we noticed something. Teams building new products have more important concerns than data streaming. Namely, they need to ship. And they need to ship fast, affordably, and without giving up quality.

One CTO put it bluntly during a feedback call: "Ambar's streaming service is great, but what I really need is the entire application, and I need it fast. Data streaming tooling solves 25% of my problems at best, but I still need to solve the rest."

The question on our whiteboard was simple: "Are we in the right business?" And if the answer was no, how could we solve our customers' most critical problems? This is the story of how we evolved to meet our customers' real needs.

The Pivot Point

The breakthrough came during a course we were teaching on event-based architectures. We shared war stories with our students about how event-based architectures had helped us ship faster in the past. Then, a student asked us to explain the advantages in a more formal, computer-science-oriented fashion.

We didn't answer on the spot, but when we eventually got back to him, we explained that event-based architectures use not just a different architectural pattern but are fundamentally different computation graphs. Traditional applications form dense graphs where every feature potentially interacts with every other feature. Event-based systems form sparse graphs where there are very few interdependencies.

The implications of this formalization were profound. Dense graphs become more difficult to modify as they grow, whereas sparse graphs scale linearly. In other words, those adopting event-based architectures move faster because their applications are easier to change and build upon.

But how could we use this insight to better serve our customers?

From Infrastructure to Applications

The answer was staring us in the face. If event-based architectures create sparse dependency graphs, which are inherently less complex to reason about, we could develop applications significantly more efficiently.

What if we leveraged our expertise to deliver entire applications instead of only providing the streaming infrastructure?

The key would be to build tooling that:

1. Maintained the sparse graph structure of event-based systems

2. Allowed our engineers to specify application behavior in an intuitive and compact way

3. Automated the tedious parts of implementation

Building the Type System

We assembled our team of engineers—veterans from Amazon and Facebook—and gave them a mission: create a type system that captures complex application logic with minimal cognitive overhead. We dove deep into natural language processing, graph theory, type theory, and programming language design. We reasoned about how humans naturally express business requirements and how those expressions could map to formal specifications.

What emerged was a compact but remarkably expressive type system. It allowed our engineers to describe application behavior using concepts that closely matched how business stakeholders spoke about their features.

The Human-Machine Partnership

With the type system in place, we discovered something unexpected. When requirements are expressed in a precise, well-structured type system, large language models (LLMs) become remarkably effective at generating the implementation. They don't get bored writing boilerplate, they don't take shortcuts when implementing error handling, and they don't introduce inconsistencies across similar patterns. Plus, with our approach, the LLMs don't hallucinate or produce faulty code because a human has already addressed the tricky parts (ambiguous requirements, edge cases, good user experience, etc.) And most importantly, LLMs continue to generate high quality code as our codebases grow in size and complexity—our context window size requirements stay static no matter how large our applications. Why? Because our applications are inherently architected as sparse graphs!

This led to our current approach:

1. Expert engineers work with clients to gather requirements, design the system architecture, and express it using our type system

2. Our customized LLM pipeline generates the implementation code

3. Engineers review, test, and refine the generated code

The result? Applications that traditionally take 9-12 months to build are completed in 4 weeks or less without sacrificing quality.

Our Streaming Foundation

Our data streaming service hasn't disappeared through this evolution – it has become the foundation that makes our application development approach possible. Every application we build runs on top of our battle-tested streaming infrastructure, ensuring reliability, scalability, and correctness guarantees from day one.

In fact, our data streaming technology has become even more robust as we've applied it to increasingly diverse use cases. The difference is that instead of asking clients to build on top of our streaming platform themselves, we can deliver complete applications built on this foundation.

Looking Forward

Ambar has evolved from a data streaming service to an application development company backed by a data streaming service. We deliver complex web, data, and mobile applications in 4 weeks or less, at the price of a junior developer, but with quality that matches the best engineering teams in the world.

This isn't just a new business model—it's a fundamentally different approach to software development. By combining the sparse dependency graphs of event-based architectures with a carefully designed type system and an intelligent division of labor between humans and LLMs, we've created a formula that dramatically compresses development timelines without sacrificing quality.

For businesses facing competitive pressure to digitize quickly, this approach offers a compelling alternative to traditional development, offshore teams, or no-code platforms with inherent limitations.

Our journey wasn't planned, but looking back, it feels inevitable. We followed the data, listened to our customers, and built something more directly addressing their core needs than our original vision.

And we're just getting started.

When you are ready to see what we can build for you, get in touch, and we'll get back to you within 30 minutes.