How to Evaluate a Software Development Partner in 2026

Choosing a software development partner has never been a simple procurement exercise. But in 2026, the decision is more consequential than it used to be.

AI has changed the economics and mechanics of software delivery. Development partners now routinely use AI to write a significant proportion of your codebase. That’s a genuine advantage when it’s done well: faster delivery, lower cost, more consistent output. But it also introduces new questions around intellectual property, security, compliance, and long-term maintainability that most evaluation frameworks haven’t caught up with.

This is a practical checklist for CTOs, procurement leads, and technology decision-makers who are shortlisting partners and want to know what to look for, and what to be wary of.

The 2026 evaluation checklist

1. Do they have a structured AI adoption practice?

The question is not “do they use AI?” In 2026, virtually every development team uses AI tools to some degree. The question is whether they have a disciplined, measurable approach to adopting and evaluating those tools.

Look for a defined evaluation cycle: how often do they assess new tooling, what criteria do they use, and how do they measure impact? A partner who adopted GitHub Copilot three years ago and hasn’t reassessed since is not the same as one running a quarterly evaluation cycle that tests new models, measures output quality, and retires tools that underperform.

Ask for specifics. What percentage of their code is AI-authored? What model do they use and why? How do they handle the transition when better tooling becomes available? Vague answers like “we use the latest AI” should give you pause.

2. Will you own the code?

Full source code ownership should be non-negotiable. Your code should be hosted in your own repository (GitHub or Azure DevOps), documented, and fully transferable. There should be no contractual lock-in that prevents you from changing suppliers, scaling your internal team, or evolving the system independently.

This has become more important, not less, as AI enters the picture. When AI generates a significant proportion of the codebase, the question of who owns that code becomes a live concern. A credible partner will have clear terms on IP ownership and will address AI-generated code explicitly in their contracts.

Also ask about the distinction between bespoke code and any underlying accelerators or libraries. On fully custom greenfield builds you should receive full IP transfer and own the code outright. On projects that use accelerator libraries, you should receive a perpetual licence to those components with full ownership of all project-specific code. Read more about source code ownership and intellectual property in software development.

3. Are they ISO 27001 certified?

ISO 27001 is the international standard for information security management. It covers how an organisation handles client data, manages access, responds to incidents, and maintains operational security across every project.

This isn’t a nice-to-have. If your partner is handling sensitive data, building systems that process personal information, or operating in a regulated sector, ISO 27001 certification gives you a baseline of confidence that their security practices have been independently audited.

Ask whether their certification covers client project work specifically, not just their internal IT. Some organisations hold ISO 27001 for their own systems but don’t extend its scope to the software they build for clients. Our ISO 27001 certification covers client work, internal systems, and operational processes.

4. Can they work within government procurement frameworks?

If your organisation is in the public sector or receives public funding, your partner’s presence on government frameworks can simplify procurement significantly. The two most relevant frameworks in the UK are G-Cloud (for cloud software and support) and Digital Outcomes and Specialists (DOS) for bespoke digital delivery.

Being listed on these frameworks means the supplier has already passed a due diligence process and can be procured through the Digital Marketplace without a separate tender. It also means their pricing, terms, and service descriptions are publicly available and have been reviewed.

Even if you’re not in the public sector, a partner’s presence on Crown Commercial frameworks is a useful indicator of their maturity and willingness to operate transparently.

5. Do they have a real QA story for AI-generated code?

This is the question that separates partners who genuinely use AI responsibly from those who use it as a speed lever without adequate safeguards.

When AI generates code, it can introduce subtle issues: inconsistent architecture, security vulnerabilities, open-source licence contamination, and logic errors that pass basic tests but fail under edge cases. Your partner needs a documented process for catching these.

Specifically, ask about: human review of all AI-generated code before it reaches production, automated licence scanning in their CI/CD pipeline to detect open-source contamination, ISTQB-qualified or equivalent testing professionals, and a defined approach to security testing that accounts for the specific risks of AI-generated output.

If their answer is “AI generates the code and we deploy it,” you have a problem, not a partner. For context on why this matters, read about what goes wrong when AI ships code unsupervised.

6. What does their support model look like after launch?

Software is never finished when it launches. The first three to six months after go-live typically involve stabilisation, performance tuning, and feature refinement based on real usage data. Beyond that, every system needs ongoing security patching, monitoring, and incremental improvement.

Ask about their managed application support model. Is it SLA-backed? Is monitoring proactive or reactive? Do they offer tiered support options? What happens if you need to scale the system or adapt it to changing requirements?

A partner who disappears after handover is a risk you’ll feel acutely the first time something goes wrong at 2am. Look for one who treats support as a relationship, not an afterthought.

7. Can they show long-term client relationships?

Project references are useful. Long-term client relationships are more revealing. A partner who has worked with the same clients for years (not months) demonstrates that they deliver sustained value, not just an initial build.

Ask for case studies that show ongoing engagement: systems they’ve built and then supported, iterated, and evolved over time. Look at the range of clients and sectors to understand whether they have experience relevant to your context. Our case studies cover education, transport, fitness, government, and charity sectors, with relationships spanning multiple years.

8. Do they share their AI metrics openly?

Transparency about AI usage is a strong signal of maturity. A partner who can tell you that 51 percent of their code is AI-authored, that they run quarterly evaluation cycles, and that they’ve moved from one model to another based on measured performance is demonstrating a level of rigour that should give you confidence.

By contrast, partners who can’t quantify their AI usage or won’t share their metrics are either not measuring (concerning) or not willing to be transparent (equally concerning). The AI productivity gains are real, and you should be able to see evidence that they’re being passed through to you in the form of faster delivery, lower cost, or both. For a practical look at how this translates to budgets, read what AI-augmented development means for your budget.

Red flags to watch for

Not every partner will score highly on every item above, and that’s acceptable depending on your requirements. But some signals should give you serious pause:

• No compliance story at all. If a partner cannot demonstrate any security certification (ISO 27001, Cyber Essentials, or equivalent) and does not operate within recognised procurement frameworks, you are carrying the compliance risk yourself.

• Vague “we use AI” claims with no specifics. Every agency says they use AI now. If they can’t tell you which models, what percentage of code, or how they evaluate tooling, the claim is marketing, not practice.

• No code access or repository ownership. If you don’t have access to your own code repository from day one, you are locked in whether or not the contract says otherwise.

• AI tool costs passed directly to you. Per-seat AI licensing costs should be absorbed as part of the partner’s delivery capability, not line-itemed on your invoice. The savings AI provides should reduce your project cost, not appear as an additional charge.

• No post-launch support model. If the conversation ends at “we’ll deliver the project,” ask what happens when the project needs its first security patch, its first performance issue, or its first feature change. The answer will tell you a lot.

Frequently Asked Questions

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