How to Vet an Offshore Partner's AI-Assisted Development Before You Sign

Almost every offshore team now writes code with AI assistants. The question for buyers is no longer whether they do, but whether they can show you how it is governed.

Why the vendor conversation has changed

Cost is no longer the main reason companies outsource. Surveys this year show it falling to under a third of buyers, with access to talent and AI-augmented delivery taking its place. That shift is healthy, but it adds a new layer of risk. AI-assisted delivery can raise speed and lower cost, and it can also introduce subtle bugs, licensing questions and data exposure that a traditional vendor review never asked about.

Most due diligence checklists were written before coding assistants were standard. They ask about certifications, references and team turnover. They rarely ask which AI tools touch your code, where your data goes when an engineer pastes it into a prompt, or who owns the output.

Ask which tools are used, and on what terms

Start with a plain question: which AI tools do your engineers use on client projects, and under what plan? Enterprise plans that exclude your code from model training are very different from personal accounts used on a side screen. A trustworthy partner can name the tools, show the policy and tell you who approves exceptions.

A useful red flag is a vendor who has not read the terms of service of the tools their engineers use every day. It is more common than it should be.

Get IP ownership in writing

Standard contracts assume a human wrote the work. Ask that the agreement address AI-assisted output directly: who owns it, what the vendor warrants about third-party code, and whether any indemnity survives when generated code is involved. Some model providers now offer IP indemnity on their outputs, but many downstream vendors carve AI-generated content out of their own indemnities. Do not assume yours is covered.

Check where your data travels

If you work in healthcare, energy, education or any regulated space, the questions get sharper. Can the vendor sign a BAA? Do they list the sub-processors behind their AI tooling? Do they disclose where prompts and code are processed? A "HIPAA compliant" label on a coding tool is not a certification, and you should ask for the evidence behind it.

Look for review discipline, not just speed

Faster output only helps if someone reads it. Ask how AI-generated changes are reviewed, whether tests are required before merge, and how the team tracks defects that trace back to generated code. Teams that measure this can explain it in a sentence. Teams that do not will talk about productivity instead.

Test the answers on a small engagement

Written answers are easy. A two-to-four-week paid pilot shows you the real process: pull request quality, documentation, how questions are handled and whether the tools policy matches what you see. Treat it as an audit of the working relationship, not just the code.

How we approach it at Ontoborn

At Ontoborn we use AI assistants in our own delivery, and we tell clients up front which tools are in play, what stays out of them and how every change is reviewed by a named engineer. Clients own the code, the documentation and the repository from day one, so switching partners never means starting over.

A short checklist to take into your next vendor call

• •Which AI tools are used on our project, and under what plan?
• •Is our code excluded from model training?
• •Who owns AI-assisted output, and what does the indemnity actually cover?
• •Where is our data processed, and who are the sub-processors?
• •How are generated changes reviewed and tested?
• •Can we run a short paid pilot before committing?

A good partner will answer these without hesitation. If the answers are vague, that tells you what you needed to know.