The (other) Guilty Secret of AI Startups? Forward-Deployed Engineers.

Many AI startups tout sleek, scalable platforms powered by cutting-edge models. But peel back the curtain, (no – not that curtain, Builder.ai !) and you’ll often find a different reality: Forward-deployed engineers manually configuring, customizing, and operationalizing each client deployment.

This isn’t inherently bad. In fact, embedding engineers with customers early on can be invaluable for:

• Quickly building deep understanding of customers’ real-world use cases without ambiguity.

• Fast iteration and focus on UX and workflow design.

• Validation of product-market fit and the configurability of the core solution.

The problem is that many startups hide this model under the guise of “fully automated AI.” What customers believe is a product is often a bespoke consulting engagement in disguise.

This creates tension on multiple fronts:

• Investor expectations of SaaS-like margins clash with the realities of service-heavy delivery.

• Customers expect self-serve AI but instead depend on one or two key engineers for everything to work.

• Product teams struggle to standardize or scale because so much implementation is “off-road.”

• Maintenance costs associated with bespoke implementations cannibalize resources that would otherwise be applied to building out the core AI solutions of the business.

Over time, this builds a brittle foundation: Ballooning technical debt, overworked teams, and customer dependencies that can’t be unwound. Having worked for (non-AI) startups that have made the same mistakes and seeing how they've played out I know it's only too easy for businesses to become ensnared in traps of their own making.

The solution isn’t to abandon forward-deployed Engineers — it’s to be transparent about them. Use them intentionally. Learn fast and then double down on productizing what works. Maintain a backlog of all your bespoke implementations that the company focuses on paying down. Move functionality from custom implementation to configuration.

 Early-stage AI is messy by nature. But long-term defensibility comes from building true platforms, not propping up demos with people in the loop