Every offshore vendor now claims to be "AI-native." Almost none of them can show you what that means in practice. The gap between the claim and the reality shows up in three places: whether AI tooling is embedded in the daily engineering workflow or sits in a sandbox nobody uses after the demo, whether code review and testing were actually adapted for AI-assisted output, and whether the vendor can explain what happens to your code and data when it passes through a third-party model. If a vendor cannot answer specific questions about those three areas within a single call, the "AI-powered" label on their slide deck is decoration, not a delivery practice.
This matters more in 2026 because the baseline has moved.
A 2023 Stack Overflow survey found that 70% of software developers use AI tools in their workflow, and enterprise demand for offshore partnerships to close AI capability gaps is rising. AI tool use in software delivery is no longer a differentiator by itself. What separates a genuine practice from a marketing line is whether the tooling is standard, governed, and measurable, or improvised for your evaluation call.
TL;DR
"AI-native" should describe a standard part of the developer environment with review, testing, and onboarding built around it, not a tool used for one demo.
The sharpest verification questions are about code review adapted for AI output, IP ownership, and where your code and data go once an AI tool touches them.
Ask to see artefacts: repo-level prompt conventions, PR review history, an AI usage policy, and onboarding material for new engineers.
Run a short paid trial on your own codebase and measure your own numbers (PR cycle time, revert rate) instead of trusting a vendor's claimed multiplier.
Any vendor offering a blanket speed claim without qualifying which tasks it applies to is a warning sign, not a selling point.
About the author: 724SOFTWARE is a Vietnam-based engineering partner and a selected Anthropic partner. Our engineers use Claude Code as part of standard delivery. This guide is written to be useful independent of that fact: it is the same evaluation framework we'd expect a serious buyer to apply to us.
What Does "AI-Native" Actually Mean in a Delivery Team?
An AI-native delivery team is one where generative AI tools are a fixed part of the standard developer environment, governed by written practice, and used by every engineer, not a subset running a side experiment. That's the operational definition. It has four concrete components, and a vendor should be able to point to all four without hedging:
Tooling in the standard environment. The AI coding assistant (Claude Code, Cursor, Copilot, or similar) is installed and used by default for the engineers on your account, not spun up specifically because you asked about AI in the sales call.
Review and testing adapted for AI-written code. Code that an AI assistant helped write goes through the same, or a stricter, review gate as human-written code. Teams that treat AI output as automatically trustworthy are skipping a step that matters.
Prompt and context conventions held in the repo. Mature teams keep reusable prompt templates, project context files, and coding standards for AI tools inside version control, the same place they keep linting rules. If none of that exists, the practice isn't standardised, it's ad hoc.
Onboarding that trains new engineers on it. When a new engineer joins the account, is there a defined onboarding step for how they use AI tools on this codebase, or do they figure it out themselves?
None of this requires trusting a marketing claim. Each point above corresponds to something you can ask to see.
What Questions Should You Ask on a Vendor Call?
Building on the four components above, the practical test is whether a vendor's answers get more specific the harder you push, or vaguer. A team with a real practice answers with names of tools, specific review steps, and named policy documents. A team without one answers with adjectives.
Question you ask | Strong answer sounds like | Weak answer sounds like
|
|---|---|---|
"Which AI coding tool does the team assigned to us use, day to day?" | Names a specific tool (e.g., Claude Code, Cursor) and says which roles use it | "We use various AI tools as needed" |
"How is AI-generated code reviewed differently from human-written code?" | Describes a specific review or testing step, e.g. mandatory unit test coverage before merge, additional review flag in the PR | "Our senior engineers check everything" (with no specifics) |
"Where does our code go when an engineer uses the AI tool?" | Explains data handling, model provider, and whether code leaves the client environment | Silence, deflection, or "it's all secure" |
"Can I see an example of a prompt template or context file used on a real project?" | Produces or describes one, possibly redacted | "That's proprietary" used to avoid showing anything exists |
"What's your AI usage policy for developers?" | References a written document with named do's and don'ts | "We trust our engineers to use good judgment" |
A vague answer to any of these is worth treating as a real signal, not an oversight.
What Artefacts Should You Ask to See?
Stepping back from the conversation itself, the more reliable evidence is documentary, because talk is cheap and artefacts are not. Ask to see, in order of how much they tell you:
1. A written AI usage policy. Teams that have actually operationalised AI tooling tend to have a policy document covering what tools are approved, what data can and cannot be passed to them, and how output is reviewed. If the vendor has never written one down, the practice likely isn't standardised across the team.
2. Pull request history showing review discipline. You don't need code access to ask for anonymised examples of PR review threads that show AI-assisted code being challenged, tested, or revised, not rubber-stamped.
3. Client contract language on IP and code confidentiality. This is where most buyers should concentrate their attention, and where most vendors are weakest. Ask explicitly: does any client code or business data get sent to a third-party model provider's servers, and under what contractual terms? Who owns the output? What happens if an AI tool was trained on, or retains, fragments of your codebase? A vendor with a real practice will have specific, written answers, generally referencing frameworks like GDPR and, increasingly, SOC 2's newer AI-specific criteria covering model governance and training data provenance. Certifications like ISO 27001:2022 and SOC 2 Type II cover information security broadly, but note they do not, by themselves, certify AI governance specifically. That requires a separate standard, ISO 42001, purpose-built for AI management systems.
4. Onboarding materials for new engineers joining your account. If AI tool use is standard practice, there should be a document, not a verbal explanation, that new engineers are given.
How Do You Run a Trial That Actually Tests This?
A related but distinct question is how to test the claim rather than the paperwork. The answer is a short, paid trial on your own codebase, not a reference project or a demo repo the vendor controls.
Structure it like this:
Give the trial team a real, scoped piece of work from your backlog, small enough to complete in one to two weeks.
Agree in advance what you'll measure: not "did it feel faster," but concrete numbers like pull request cycle time, revert rate after merge, and how many review rounds a change needed before it shipped. These are the same categories of metric that longitudinal studies use to document real AI productivity effects. One DORA study on deployment frequency and lead time for changes found measurable differences in development cycle metrics when AI tooling was applied consistently, though the magnitude varies significantly by task type.
Compare those numbers against your own team's historical baseline on similar work, not against an industry average or a vendor's marketing claim.
Watch whether the vendor is transparent about which parts of the work AI assistance actually helped with, and which parts it didn't touch. That honesty is itself a signal.
This is more reliable than any claimed multiplier because it produces your numbers, on your code, under your review standards.
Why Is a Blanket Speed Claim a Red Flag?
A related but important point: any vendor who tells you their AI-native team delivers significantly faster across the board, without naming which specific task types or code patterns benefit most, is overstating what AI assistance can currently do. AI coding assistants materially help with boilerplate generation, test scaffolding, refactoring, and documentation.
They help far less with tasks that require deep system-specific context, architectural judgment on legacy systems, or debugging distributed failures that span services the model has never seen described anywhere. A team that understands this will tell you where AI helps and where it doesn't. A team selling you a single number for everything hasn't measured it, or is rounding a narrow win into a company-wide claim.
Frequently Asked Questions
Does "AI-native" mean the team writes no code themselves?
No. It means AI tools are a standard, governed part of how engineers write, review, and test code, with human engineers still responsible for the output.
Is a vendor with an AI partnership (like Anthropic or OpenAI) automatically more credible?
A formal partnership indicates access to tooling and sometimes training, but it doesn't by itself confirm daily practice. Ask the questions above regardless of what logos appear on the vendor's site.
What's the single most important question to ask about data handling?
Whether your code or business data leaves your environment when an engineer uses an AI coding tool, and under what contractual and technical safeguards. This is the area where vendors are most often unprepared with a specific answer.
Should I expect a written AI usage policy from any serious vendor?
Yes. Teams with a real practice tend to have one covering approved tools, data boundaries, and review requirements. Its absence doesn't prove a vendor is dishonest, but it does mean the practice hasn't been formalised.
How long should a fair trial period be?
Long enough to complete one real, scoped piece of backlog work, typically one to two weeks, measured against your own historical delivery metrics rather than an industry benchmark.
Are certifications like ISO 27001 enough to cover AI governance specifically?
No. ISO 27001:2022 and SOC 2 Type II cover information security broadly with recurring audit cycles, but AI governance specifically is addressed by the newer ISO 42001 standard.
What does 724SOFTWARE's AI practice actually consist of?
724SOFTWARE is a selected Anthropic partner in Vietnam. Our engineers use Claude Code as part of standard delivery work. We don't claim blanket speed gains, and we'd encourage you to apply the checklist above to us the same way you would to any other vendor.
About 724SOFTWARE
724SOFTWARE is a Vietnam-based technology partner working with startups, SaaS companies, and enterprises across Fintech, healthcare, edtech, and enterprise ERP, with 200+ professionals and 58% at senior level. The company holds ISO 9001 and ISO 27001:2022 certifications, is SOC 2 Type II and GDPR compliant, and operates a follow-the-sun support model with sub-10-minute incident response. As a selected Anthropic partner in Vietnam, 724SOFTWARE trains engineers to use Claude Code in day-to-day delivery, and teams are
