Every venture-backed startup begins with urgency. Product timelines compress, investor expectations mount, and technical debt becomes a side effect of ambition. Yet in the AI ecosystem, where infrastructure must support both experimental models and production-ready workflows, cutting corners on foundational engineering isn’t just unsustainable. It’s existential.
Alessa Cross has seen this firsthand. A founding team member at Ventrilo AI, she is also a recognized thought leader in technical scale strategy and a contributor to the Forbes Technology Council. Her background spans high-impact roles at PayPal and Athelas, where she shaped engineering systems that helped scale operational complexity into investor-ready infrastructure. At the heart of her work is one insight: fundability begins at the architectural layer.
“There’s an assumption that users will wait for reliability once they see novelty,” she explains. “But in production systems, early reliability becomes your reputation long before product-market fit.”
Infrastructure as Due Diligence
The reality of AI product development is that user success hinges not only on accuracy, but on auditability, repeatability, and system-level integration. In fact, McKinsey’s 2024 Technology Trends Outlook identifies observability and infrastructure maturity as key predictors of AI platform success.
Cross’s own work at Athelas/Commure is a case study in this principle. As Software Lead, she architected two foundational systems: a real-time task allocation engine and a Prior Authorization pipeline for Revenue Cycle Management (RCM). These systems became mission-critical infrastructure supporting over 200 operators, thousands of clinical authorization requests, and ultimately a revenue scale-up to what is now over $100M.
“When your infrastructure touches patient care and hospital cash flow, reliability is not a feature, it’s the floor,” she notes.
What made these systems stand out wasn’t just their scale, it was their surgical precision. Built with embedded feedback loops, regulatory logic, and dynamic task routing, the RCM platform became the operational core of Commure’s healthcare SaaS offering.
What Investors Really Evaluate in AI Startups
In her role as a judge for the Globee® Awards in Artificial Intelligence, Cross routinely reviews companies claiming cutting-edge innovation. But she says it’s the quiet infrastructure choices, not the flashy demos, that most often determine whether a startup is investment-ready.
This shift is already visible in investor term sheets, where due diligence has expanded beyond market size and go-to-market. Investors now scrutinize internal APIs, deployment pipelines, version control, and telemetry stacks. Cross, who often builds internal-facing dashboards alongside customer products, says startups that invest in observability early tend to weather growth cycles better, and raise faster.
Turning Prototype Hype Into Scalable Infrastructure
Many AI startups collapse between MVP and Series A. For Cross, the problem is often cultural, an overvaluation of iteration speed at the expense of system clarity. In her recently published scholarly paper, Scaling Innovation in Tech Startups: Engineering-Driven AI Solutions for Venture-Backed Growth and Fundraising-Ready Product Infrastructures, she outlines the engineering signals correlated with scale-readiness. These include modular failover design, version-aware deployments, and end-to-end observability.
“Defensibility isn’t what you file a patent on, it’s what fails gracefully and scales predictably,” she emphasizes. “It’s about designing in a way that lets your product evolve, not get rewritten.”
This philosophy was on display in her RCM platform’s modular task allocation engine, which intelligently distributed claims-processing responsibilities to hundreds of operators while embedding performance analytics. Her Prior Authorization system was built to optimize time-to-approval for high-risk patients. These weren’t just systems, they were operational leverage, transforming complexity into growth.
Designing for the Check, Not Just the Demo
As capital tightens and investor scrutiny sharpens, early-stage AI companies must recalibrate. The product-market fit of 2025 is defined not by velocity, but by traceability, reliability, and strategic foresight.
“You can no longer rely on velocity to validate your vision,” Cross notes. “Infrastructure is your first due diligence test, and an important part of your product that investors should inspect line by line.”
In an industry flooded with prototypes, Cross is building systems that withstand not only user expectations, but also investor scrutiny, regulatory pressure, and real-world complexity. Her work reframes product maturity, not as a milestone, but as a mindset. It’s a call to founders to design with future audits in mind.
The AI startups that thrive won’t be the ones with the most buzz. They’ll be the ones with the most resilience, systems that don’t just scale, but survive. And as Cross proves, that resilience is engineered, not improvised.
