TAMPA, Fla. (BLOOM) – Startups with venture backing often kick off with a strong sense of urgency. They face tight deadlines for product development, rising expectations from investors, and the inevitable accumulation of technical debt due to ambitious goals. In the AI world, which requires infrastructure to support both experimental and deployment-ready models, skipping on essential engineering foundations isn’t just challenging—it’s critical to survival.

Alessa Cross is well-acquainted with these challenges. As an original member of the Ventrilo AI team and a respected figure in strategic technical scaling, contributing to the Forbes Technology Council, she brings a wealth of experience. Her career includes significant roles at PayPal and Athelas, where she developed engineering systems that transformed complex operations into structures poised for investment. Her core belief is that the potential for funding starts at the architectural level.

“There’s a belief that users will tolerate waiting for systems to become reliable after enjoying initial novelty,” she states. “However, in operational systems, establishing early reliability is the key to building a reputation even before achieving a product-market match.”

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. It has since been recognized by the U.S. Department of Veterans Affairs as a finalist in their AI Tech Sprint, a nod to its capacity for real-world medical data processing and automation at scale.

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.

“Fundability increasingly maps to instrumentality,” she explains. “Investors want to know how the system behaves under load, what metrics they can observe, and how issues are flagged and resolved. Model accuracy matters less than system reliability.”

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.