Hiring an AI Software Development Partner in the Philippines: A Due-Diligence Checklist
"AI-native" has become the label every software vendor in the Philippines reaches for, the same way "cloud-native" and "agile" did before it. The problem isn't that the label is meaningless — for some vendors it describes a genuine, restructured delivery process. The problem is that from the outside, a vendor with a real AI-native process and a vendor who added a code-completion plugin to their existing workflow sound identical in a sales call. Here's the checklist to tell them apart before you sign, not after the first missed deadline.
Proof: Ask for a specific number, not a vibe
"We use AI extensively" is not a verifiable claim. "80% or more of the codebase on a typical project is AI-generated against an approved specification" is. A vendor who can't or won't give you a real percentage — even a range, with the caveat that it varies by project type — probably doesn't track it internally, which tells you their AI-native claim is closer to marketing than to process.
Platform: Ask what's actually generating the code
Not all AI code generation is the same, and the gap matters more than the "AI-native" label suggests. A general-purpose AI app builder is good at producing a working prototype quickly, but most weren't built to handle multi-step business rules, regulatory compliance requirements, or integration with the systems you already run — the things that separate a demo from software a regulated mid-market company can actually operate on. Ask specifically what platform underlies the vendor's process: a consumer-grade tool anyone can sign up for, or something purpose-built for production software with the compliance and business-logic depth an enterprise system actually needs. The AI-generated percentage a vendor quotes you means something different depending on which one they're actually using.
Quality Control: Ask what catches the AI's mistakes
AI-generated code is not automatically correct code. The real question is what happens between generation and delivery: is there a defined quality gate — a specific tool, a specific set of thresholds, a human review step — or is "we review everything" the entire answer? Ask what percentage of AI-generated code fails that gate on a first pass. A vendor with a real process has this number. A vendor without one will look surprised you asked.
Maintainability: Ask whether documentation is generated or an afterthought
AI-generated code that ships fast today isn't automatically code your team — or a different vendor — can maintain two years from now. Ask specifically whether documentation is produced as part of the generation process itself, tied to the specification, or written up separately after the fact if a client happens to ask for it. The former tends to stay accurate as the system evolves; the latter tends to go stale within a few sprints. Also ask what a typical AI-generated module looks like to a human reader unfamiliar with the project — readable, consistently structured code, or a working but opaque black box.
Data & IP: Ask where your code and data actually go
Every AI code-generation tool processes your specifications, your business logic, and sometimes your actual data through a model somewhere — the question is whose model, and under what terms. Ask specifically whether the vendor runs an enterprise-isolated instance where your inputs aren't used to train anyone else's model, or a consumer-grade tool with murkier data terms. For anyone handling regulated data or genuinely proprietary business logic, this isn't a formality — it's the difference between your competitive advantage staying yours and it quietly becoming training data for a tool your competitor also uses.
Code Ownership: Ask if you could actually hand this off to someone else
A codebase generated through a vendor's proprietary AI pipeline is only as portable as its output. Ask directly: if you switched vendors next year, could a different team pick up this codebase, understand it, and maintain it — or is it functionally tied to this vendor's specific tooling and process? A vendor confident in their delivery model should have a straightforward answer. One who hesitates is telling you something about how locked-in you'd actually be.
Change Management: Ask what a mid-project scope change actually costs
This is the question that separates a genuinely restructured delivery model from a traditional one with an AI layer bolted on top. In a real AI-native process, a feature-level change — a new field, a workflow adjustment — should be fast and inexpensive to implement, because regenerating code against an updated specification doesn't require re-planning a human team's remaining sprint capacity. A foundational change — the data model, a compliance requirement discovered mid-build — should still get real scrutiny and cost accordingly, since that's not something AI regeneration makes cheap. If a vendor treats every change the same way regardless of scale, they haven't restructured anything; they've just added AI-generated code to the same change-order process everyone else uses.
Team Structure: Ask who's actually on the team, not just who signs the contract
AI-native doesn't mean no developers. It means a different mix: fewer people writing code line by line, and more people writing specifications, reviewing AI output, and making the judgment calls a model can't. Ask specifically who reviews the generated code, what their background is, and whether that person is a named individual on your project or an anonymous pool. A vendor who can't name the human checking the AI's work is asking you to trust a process you can't verify.
Process & Collaboration: Ask about the process and where your team comes in
An AI-native process doesn't remove your team from the picture — it changes when and how much they're needed. Ask specifically what's expected from your side: who needs to review and sign off the specification before build starts, how often you'll be shown working software rather than a status update, and what the turnaround expectation is when the vendor needs an answer from you. Also ask what happens when your team is slow to respond — does the pipeline stall the same way a traditional build would, or can the vendor keep moving on the parts of the system that don't depend on your answer. A vendor who can't describe this clearly is asking you to find out the hard way, mid-project.
References: Ask for a reference that includes what went wrong
Every vendor has a case study where everything went smoothly. Ask instead for a project where the specification changed mid-build, or where the AI-generated code needed significant rework, and ask how that was actually handled — timeline impact, cost impact, communication. How a vendor handles the case that didn't go perfectly tells you more about their actual process than any case study they chose to publish.
Pricing: Don't ask "how much cheaper is this"
It's tempting to lead with price, but for a comparable, well-specified project, a genuinely AI-native vendor's price won't necessarily be lower than a traditional one — the hours saved on hand-coding get reinvested in the tooling and quality infrastructure that made the speed possible in the first place. What should be different is the timeline, the consistency of the output, and how the vendor handles scope changes. A vendor who leads with "we're cheaper because of AI" without being able to answer the questions above is selling the label, not the process.
If you're evaluating multiple vendors and want a second opinion on whether their AI-native claims hold up, let's talk through what you're seeing in their proposals.
Comments