Buy vs Build in recruitment Tech

With AI, everyone thinks they can build anything in a week. And in many cases, they can. You can get a working prototype up crazy fast.

But a prototype is not a product.

The 80/20 trap

Getting to a working prototype is maybe 80% of the way there. The remaining 20% — the edge cases, the compliance, the reliability, the polish — that's where the real work starts. And it often takes longer than the first 80% combined.

This is especially true in recruitment. A basic AI chatbot that asks candidates a few questions is straightforward. But an AI system that conducts deep screening interviews with open-ended follow-ups, adapts to different job types, works across voice, chat and WhatsApp, assesses language proficiency, matches candidates to the right roles, schedules follow-ups and gives every candidate personalised feedback — all while being multilingual, consistent and available 24/7 — requires a fundamentally different level of engineering and domain expertise.

The product is never finished

But let's say you push through the 20%. You now have a working product.

Except it's never finished.

AI models change. What you built on today's model might be outdated in six months. Regulations evolve. Users find new edge cases. Competitors improve. Your product now needs a team maintaining it, updating it, keeping it current. Not as a side project. Full time.

The question nobody asks

Most companies ask "can we build this?" The better question is "do we want to maintain this for the next five years?"

Building is a one-time decision. Maintaining is a permanent commitment. Ongoing cost, ongoing focus, ongoing distraction from your actual core business.

When you factor in continuous maintenance, model upgrades, compliance updates, bias monitoring, and the AI/ML engineers needed to keep it all running (who are scarce, expensive, and hard to retain for a role that isn't your core business), the total cost of ownership often exceeds what a specialised partner would charge.

The legal layer

AI in recruitment falls under "high risk" within the EU AI Act. That means concrete obligations around informed consent, audit trails, human oversight and continuous bias monitoring. If you build it yourself, you carry 100% of the liability.

And compliance is not a one-time checkbox. It's a constant exercise that requires your legal, data and engineering teams to stay aligned on an evolving regulatory landscape.

You miss what you can't see

There's another dimension that's easy to overlook: learnings across customers.

When you build internally, you only learn from your own data, your own candidates, your own edge cases. A specialised partner learns from every customer. Every agency that uses the platform surfaces new insights, new patterns, new edge cases. That iteration cycle is faster and richer than anything you can achieve alone.

The product gets better because of the collective experience of all its users. That's a compounding advantage that an internal build will never have.

So when should you build?

Not everything needs to be bought. Some things are simple enough to build in a weekend and never touch again. A basic automation, a simple internal tool, a dashboard that pulls data from your ATS. If it's stable and won't need continuous iteration, build it.

But if the domain is complex, regulated, and constantly evolving, buying from someone whose full-time job it is to solve that problem is almost always the smarter move.

The question isn't whether you can build it. You probably can. The question is whether you want to maintain it, improve it, keep it compliant, and keep it competitive for the next five years. For most agencies, the answer is clear.