In the competitive SaaS landscape, growth is often dictated by how seamlessly a product fits into a customer’s existing digital ecosystem. For companies targeting enterprise clients, the ability to integrate with dozens of disparate systems is not a luxury but a foundational requirement. This challenge presents a significant engineering hurdle, where the operational cost of building and maintaining integrations can quickly outpace a team’s capacity and become a bottleneck to growth.
This was the precise scenario facing Alexander Jabbour, an Engineering Lead at Rilla, a conversational intelligence platform for in-person sales teams. With a background in building products & technical systems from the ground up, Jabbour was tasked with solving a critical scaling problem: connecting Rilla to over 90 different Customer Relationship Management (CRM) systems. His approach was to transform this potential bottleneck into a strategic advantage by architecting a scalable, automated “integrations machine.”
Prioritizing early CRM integrations
In the early stages of a product’s life, engineering resources are finite and must be allocated with precision. For Rilla, the decision to invest heavily in building a wide array of CRM integrations was driven by direct feedback from the market. The company learned that its product did not exist in a vacuum but had to function within the established workflows of its users.
Jabbour notes that customers were already deeply embedded in their own platforms. He states, “Our users were already living inside their CRMs – Salesforce, HubSpot, ServiceTitan, AccuLynx – which essentially acted as the operating system for their business.” This reality made building the integrations essential for adoption, as they allowed new users to connect Rilla without disrupting their daily operations, a key component of a unified Go-To-Market strategy.
The investment paid dividends across multiple fronts, including user adoption, data enrichment, and market expansion. “By investing early in integration infrastructure, we built the foundation for faster onboarding, richer insights, and a product that naturally became part of how sales teams operate every day,” Jabbour explains. This strategic investment directly enhanced key performance indicators, with seamless integrations significantly improving the Customer Lifetime Value to Customer Acquisition Cost ratio.
Engineering for integration scalability
Supporting 92 different CRMs could easily overwhelm an engineering team with bespoke code and unique maintenance requirements. The key to avoiding this complexity was to establish a scalable, future-proof framework from the start. This involved identifying commonalities among seemingly distinct systems and designing a modular architecture that could be easily adapted for new integrations.
Jabbour’s team focused on abstracting the core functionalities shared across most CRM APIs. He explains, “Building 92 CRM integrations meant dealing with many systems that behaved differently on the surface but shared the same underlying patterns.” Recognizing these shared patterns early was crucial, allowing the team to develop a plug-and-play architecture using reusable components, common blocks of logic, authentication, rate limiting, and data mapping.
This approach, which mirrors concepts found in modular architectural patterns, empowered individual developers to build and deploy new connections without extensive rewrites. “This approach allowed us to move fast without overwhelming the engineering team – each developer could ship new integrations independently, confident that the core logic and infrastructure were stable and consistent,” Jabbour adds. The result was a system that balanced the simplicity of a single codebase with the flexibility needed for rapid expansion, a hallmark of a modular monolith.
From manual work to machine
As Rilla’s customer base grew, the initial one-by-one approach to building integrations was clearly unsustainable. Each new connection added to the operational burden, creating a direct link between market expansion and engineering headcount. A fundamental shift in mindset was required to decouple growth from linear effort.
The turning point came with the recognition that the repetitive nature of the work was a sign of inefficiency. “It became clear that if we continued down this path, we’d grow our integration surface area linearly with headcount, not with leverage,” Jabbour says. This realization prompted the strategic decision to stop building individual integrations and instead build a system that could produce them efficiently.
This new system, internally dubbed the “project mosaic, the integrations machine,” was designed to automate and streamline the entire process, from development to maintenance. By abstracting common logic and investing in robust observability tools, the team reduced the time needed to fix issues from days to under an hour. Jabbour reflects, “The ‘integrations machine’ turned a manual, high-overhead task into an automated, maintainable system – letting each engineer manage an order of magnitude more integrations without burning out the team or the product roadmap.” This mirrors the principles of building an internal developer platform with a focus on core workflows.
Principles for system reliability
With thousands of live connections running simultaneously, reliability became a top priority. The initial design, where each integration operated within its own infrastructure, created unnecessary complexity and bloat. The engineering team’s guiding principle shifted toward simplicity and reusability to ensure the system could handle high-traffic surges and scale efficiently.
A key decision was to move away from a distributed infrastructure for each connection. As Jabbour explains, “Early on, each integration ran in its own infrastructure; we consolidated this into a single monolithic system that could host many smaller integrations in one place.” This move drastically reduced operational overhead and enabled faster development cycles, applying patterns for modular monoliths to achieve efficiency.
While consolidation improved management, the system still needed to handle isolated failures and independent scaling needs. “Finally, we made the system modular enough to scale individual integrations independently. Each integration could scale its own service layer based on live usage metrics,” Jabbour notes. This hybrid approach, akin to a cell-based architecture, ensured that a problem with one CRM connection would not impact others and that resources were allocated dynamically based on demand.
Customer needs driving prioritization
In a resource-constrained environment, deciding which integrations to build first is a critical business decision. Rilla’s approach was guided by a clear focus on customer value and strategic alignment rather than speculative development. This required close collaboration between the engineering, sales, and customer success teams to ensure efforts were directed where they would have the most impact.
Unlike core product features that often emerge from a lengthy discovery process, integrations have a more defined scope. “Integrations, on the other hand, are more straightforward. They’re not exploratory – you plug into a third-party API, extract the data, transform it, and load it into our system,” Jabbour says. The primary prioritization filters were contract value and alignment with the ideal customer profile.
To maximize efficiency and avoid speculative work, the team implemented a just-in-time development model. “We typically didn’t start building an integration until the deal closed – this allowed us to conserve engineering resources and focus on certain commitments,” he explains. With a seven-day turnaround, this approach ensured that engineering efforts were always tied to secured revenue and immediate customer needs, a strategy that also mitigates risks seen in long SaaS sales cycles.
Navigating third-party system unpredictability
As the integrations platform scaled, the primary challenges shifted from internal architecture to external dependencies. Managing connections with dozens of third-party systems introduced a level of unpredictability that required sophisticated engineering solutions to maintain stability and data consistency.
The core issue stemmed from a lack of control over external APIs, which could exhibit schema changes without warning. Jabbour states, “The biggest hurdles emerged from the inherent unpredictability of third-party systems at scale.” Early on, issues like a single CRM’s rate limit change could cause cascading failures across the shared infrastructure. This led to the implementation of circuit breakers and integration-level isolation to contain the impact of any one vendor’s instability.
These experiences underscored a larger lesson about building dependent systems. According to Jabbour, “Building integrations at scale isn’t just about handling more connections—it’s about anticipating and engineering around the chaos that comes from depending on systems you don’t control.” This proactive stance is essential for managing the contractual risks and operational complexities inherent in ecosystems that rely on evolving API versioning strategies.
A competitive advantage in SaaS
For modern SaaS companies, integrations have evolved from a simple technical checkbox to a powerful strategic tool. They are critical not only for acquiring customers but also for retaining them by deeply embedding a product into their core operational stack. This creates significant network effects and a defensive moat in a crowded market.
Jabbour views this function as far more than just a feature. “Integrations are much more than a technical requirement – they’re strategic infrastructure for distribution, adoption, and stickiness in the SaaS ecosystem,” he asserts. By fitting directly into existing workflows, they lower the barrier to adoption and reduce churn, a factor that can significantly impact the average SaaS churn rate.
The strategic value is in becoming indispensable. As Jabbour puts it, “The deeper a product is embedded into a customer’s stack, the harder it is to rip out.” At Rilla, this meant positioning the platform inside its customers’ digital “operating systems,” making it a fundamental part of their daily work. This deep integration fosters the kind of close collaboration between functions that drives long-term value.
Evolving the integrations engine
As Rilla continues to scale, the demand for new integrations will only accelerate. The focus for the future is on further reducing the engineering effort required to build and maintain these connections, primarily through standardization and automation. This evolution is centered on refining the underlying patterns and leveraging emerging technologies.
The team plans to formalize its architecture around a common data pipeline model. “To scale efficiently, we plan to formalize engineering conventions and reusable components across this ETL pipeline – turning common patterns into templates that make each new integration faster and more predictable to build,” Jabbour says. This creates a standardized framework for an autonomous ETL agent.
Looking ahead, Jabbour sees an opportunity to incorporate artificial intelligence into the development workflow itself. He concludes, “We’re exploring agents trained on Rilla’s integration semantics that can autonomously generate the extract layer of the ETL pipeline.” The use of AI agents in ETL processes and for auto-generating API test cases represents the next frontier in scaling integration development at speed.
Rilla’s journey demonstrates how a proactive and strategic approach to a common engineering challenge can yield a significant competitive advantage. By treating integrations not as a cost center but as a product in itself, Jabbour’s work provides a compelling model for how SaaS companies can build the infrastructure needed to support hyper-growth and achieve deep, lasting customer adoption.
