AI-augmented vs AI-native software companies: how to tell them apart in a proposal

This guide is for founders, CTOs and procurement leads comparing two or three proposals that all say "AI". It sits in the series on choosing a delivery partner that starts with large or boutique Vietnam IT vendors, and it is written for buyers of custom software development who want to know what the AI wording in a proposal actually commits the vendor to.

In this guide

• What is the difference between AI-augmented and AI-native software development?
• Why does the label in a proposal matter to a buyer?
• Eight signals that tell them apart in a proposal
• How to screen any vendor in five steps
• When is an AI-augmented company the better choice?
• When should you not pay for AI-native claims?
• Where AI fits
• What delivery record exists, and what does not
• Alternatives: other ways to judge a vendor's AI use
• Limits of this guide
• Frequently asked questions
• Next step

What is the difference between AI-augmented and AI-native software development?

AI-augmented software development means engineers keep the traditional process and use AI assistants inside it: code completion, test drafts, documentation and review hints. IBM describes these assistants as "tools designed to support, not replace, software engineers" in its pattern for AI-augmented development. The process, the plan and the staffing look the same as before; the individual developer decides when to use AI.

AI-native software development changes the process itself. AI drafts most code, tests, migrations and documents from an agreed specification by default, and the team is organised around writing that specification well and reviewing what comes back. The buyer-side consequences of that model, from scope to contract terms, are covered in AI-native software development: what changes for the buyer.

Neither label is a quality grade. A disciplined AI-augmented team can deliver better software than a careless AI-native one. The point of telling them apart is to match the vendor's method to your project and to check that the proposal's AI wording is backed by a process you can inspect.

Why does the label in a proposal matter to a buyer?

The label matters because it changes what you should hold the vendor to. Most developers now use AI tools: the Stack Overflow 2025 Developer Survey reports that 84% use or plan to use them, while more developers distrust the accuracy of AI output (46%) than trust it (33%). So "we use AI" says almost nothing on its own. What separates vendors is the discipline around the tools.

Google's DORA research reaches a similar conclusion: its 2025 report on AI-assisted software development describes AI as an amplifier of an organisation's existing strengths and weaknesses. A vendor with weak specifications and weak review gets faster at producing weak software. A proposal therefore has to show the review gate, the specifications and the acceptance tests, not just the tools.

A proposal that claims AI-native delivery but shows an hours-based plan, no review gate and no acceptance tests is AI-augmented at best. That is not wrong in itself, but you should then evaluate it as an AI-augmented proposal.

Eight signals that tell them apart in a proposal

Read each proposal against these eight signals. The middle columns show the answer each type of vendor typically gives; the last column is the document to ask for when the proposal is silent.

A vendor that answers five or more signals in the right-hand way, with documents, runs an AI-native process. One that answers two or fewer is AI-augmented, whatever its marketing says. Anything in between is a team in transition: ask which projects already run the new way.

When is an AI-augmented company the better choice?

An AI-augmented vendor is often the safer buy when your own engineers lead the product, set the architecture and review every pull request. In that set-up you want developers who work inside your process, and their individual use of AI tools is a productivity detail you control through your own review.

It also fits work that cannot yet be specified: early research, a prototype to learn what users want, or a legacy system nobody has documented. An AI-native process depends on testable specifications; without them it produces code quickly with nothing reliable to accept it against.

When should you not pay for AI-native claims?

Do not choose a vendor because of an AI-native label when:

• nobody on your side can review milestone output at the pace the plan assumes;
• the vendor cannot show a review gate, a sample specification or acceptance tests from a past project;
• your data or code may not leave your environment and the vendor cannot run its tools inside it;
• the proposal promises a speed or cost multiple without saying how it is measured.

In those cases, a well-run AI-augmented team with your own review, or a short discovery phase first, carries less risk.

What delivery record exists, and what does not

Netbase's lead offers are consulting, solutions, custom development and AI-enabled digital transformation; dedicated teams and outsourcing are secondary options. AI is integrated into every Netbase service and solution, and Netbase delivers its own projects this way. Most Netbase projects are delivered on fixed-price contracts, with milestone-based, monthly team retainer and KPI-linked models also offered; the delivery lifecycle runs from discovery through agile execution with outcome-based milestones to rollout and ongoing support.

Governance includes weekly reviews, KPI dashboards and a dedicated account manager and project manager. Security practice includes secure code review and version control, role-based access control, vulnerability scanning and NDAs for contributors. For custom development, the client owns the IP created for it; Netbase's productized modules are licensed, not transferred.

The delivered AI feature named with a client is 4over4's recommendation engine, which suggests products from browsing history and previous purchases. Beyond it, Netbase has delivered AI projects for clients that are not named, including retrieval-augmented knowledge assistants, document AI and MLOps pipelines.

What does not exist: no published figure for how much faster AI-native delivery made any Netbase project, no published defect or cost comparison between AI-augmented and AI-native delivery, and no client named for the anonymised AI projects. This page gives a method for reading proposals, not results.

Alternatives: other ways to judge a vendor's AI use

• Ask for a reference call about AI use specifically. Choose it when the vendor has clients willing to talk; the trade-off is that references are selected by the vendor.
• Rely on a certification or framework claim. Choose it as a floor for security and governance; the trade-off is that certificates cover management systems, not how a given team reviews AI output.
• Run a competitive pilot with two vendors. Choose it for a large, long programme where the selection cost is small next to the risk; the trade-off is time and the cost of two pilots.
• Pick on contract model first. Compare a team you direct with a fixed-price project, as in dedicated team vs fixed price, then apply the eight signals to the shortlist. How each commercial model works is set out on engagement models.

Limits of this guide

• The eight signals are a screening method, not an audit; a vendor can pass them and still deliver poorly.
• Survey figures describe developers in general, not any single company.
• It is not legal advice; have counsel review AI and IP clauses for your jurisdiction.

Next step

Bring the proposals you are comparing, and Netbase will go through the eight signals with you: book a solution review.

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