Why Custom AI Is (Almost Always) a Bad Idea in Recruitment

It always starts with good intentions

'We'll build it ourselves. Then we'll have full control.' It sounds logical. Your recruitment process is unique. Your CRM structure is specific. Your clients expect a certain format. So why wouldn't you build your own AI solution that does exactly what you need?

Because it almost always goes wrong. Not due to lack of ambition or talent. But because of a systematic underestimation of the complexity, costs, and timeline. It starts as a three-month project and ends as a black hole consuming talent, time, and budget.

In this article, I'll explain why custom AI for recruitment is a costly mistake in 95% of cases. And in which 5% it might make sense.

The complexity you underestimate

'We'll take a speech-to-text API, add a summary prompt, and connect it to our CRM.' That simple on paper. In a demo from a developer who built it in a weekend, it looks impressive. But let's walk through the actual complexity.

Problem 1: Speech recognition is harder than you think

Yes, there are speech-to-text APIs. Whisper, Google, Azure. They work fine. In English. In a quiet environment. With one speaker. With a good internet connection.

But in recruitment:

• You call in Dutch, German, French, or a mix of languages. Mid-sentence, the candidate switches to English. Or the client throws in a German word.
• You call from a mobile in the car with background noise, wind, and tunnels.
• Two people talk over each other in a Teams meeting. Or the candidate has kids in the background.
• The candidate speaks with an accent, dialect, or soft voice.
• There are names, place names, and company names that need to be transcribed correctly. 'Van der Linden-Pietersen' isn't trivial for an AI.

The base accuracy of a speech-to-text API is 85-90%. For recruitment, you need 95%+. That 5-10% improvement costs months of work on fine-tuning, domain-specific vocabulary, and error correction. And then you still don't have a solid system that consistently works across all channels and conditions.

Problem 2: Summaries aren't 'just a prompt'

'Summarize this conversation.' Anyone can write that prompt. In a demo, it looks fantastic. But a good recruitment summary is much more:

• The format must match the conversation type. An intake isn't the same as a sales call. A client meeting produces different information than a follow-up with a candidate.
• The summary must recognize and extract specific data fields. Not just 'the candidate wants to earn 55,000,' but the number 55000 in the 'salary expectation' field. With the right data type.
• It must recognize inconsistencies. The candidate first says '3 years experience' then later '5 projects in 4 years.' Which is correct? The AI must flag this.
• It must handle noise. Small talk about the weather, repetitions, interruptions, 'sorry, I was on mute.' Those need to be filtered without losing relevant information.
• And it must be customizable per client, per team, per recruiter. Because client X expects a different format than client Y.

Building a single prompt that can do all this takes weeks of testing and iteration. Maintaining it as clients change and requirements evolve costs months per year. And with every update to the underlying language model, you need to retest.

Problem 3: CRM integration is a beast

This is where most custom projects get stuck. Because sending data to your CRM isn't 'making an API call.' It's a complex integration project:

• Reading the CRM's field structure. Which fields exist? What type are they? Which options are in dropdowns? Which validation rules apply?
• Matching data to the right field. 'Full-time' matching with the value 'FT' in the dropdown. 'Available by May' matching with date format 2026-05-01.
• Handling field validation rules. Date formats that differ per CRM instance. Numeric fields that don't accept text. Required fields that aren't always available.
• Error handling. What if a field doesn't exist? What if the API returns an error? What if the CRM is offline? What if there's a conflict with an existing value?
• Supporting multiple CRM systems. Bullhorn works fundamentally different from Salesforce. Mysolution has a different API than Byner. Each integration is a separate project.

Building a native integration with one CRM takes 3-6 months. With five CRMs? Count on 18+ months. And then you have to maintain them, because CRMs update their APIs. Fields change. New versions come out. It's not a one-time project, it's an ongoing obligation.

Problem 4: Multi-channel recording

Omnichannel recording means supporting not just Teams and Meet, but also phone, mobile, and VOIP. Each channel has its own technical challenges:

• Meeting bots for Teams/Meet must comply with Microsoft and Google API requirements. Those change regularly. A Teams API update can break your bot.
• Phone recording requires telecom integration. SIP trunking, recording legislation per country, audio quality, echo cancellation.
• Mobile apps need to work on iOS and Android, run in the background, be battery-efficient, and work with poor connectivity.
• VOIP integration with local phone numbers requires a telecom license and SIP provider. That's a world in itself.

This isn't a 'weekend project.' This is an entire product in itself. With its own compliance requirements, its own maintenance, and its own expertise.

The real costs

Let's be realistic about costs. Building a custom AI solution for recruitment requires at minimum:

• 2-3 AI/ML engineers (minimum 18 months): $350,000 - $700,000 per year
• 1-2 backend developers for integrations: $175,000 - $300,000 per year
• 1 product manager steering the project: $120,000 - $180,000 per year
• Cloud infrastructure (GPUs for AI models, storage for audio): $35,000 - $90,000 per year
• Speech-to-text API costs: $25,000 - $60,000 per year (depending on volume)
• LLM API costs (GPT-4/Claude): $12,000 - $45,000 per year
• QA and testing: $60,000 - $120,000 per year

Total first year: $775,000 - $1,495,000. And that's a conservative estimate. Most projects go 30-50% over budget and 6-12 months over time. That's not pessimism, that's the reality of software development.

Compare that with a SaaS solution that costs per user per month. With a team of 20 recruiters, you pay a fraction of what custom building costs. And you have it running tomorrow, not in 18 months. And someone else maintains it, updates it, and continuously improves it.

The hidden costs nobody mentions

Beyond direct costs, there are hidden costs rarely in the business case:

• Dependency on individuals. If your lead AI engineer leaves (and that happens), you're stuck with a system nobody else understands.
• Technical debt. Quick fixes during the build become permanent problems. After a year, you're stuck in a codebase that's hard to maintain.
• Training costs. You need to train your own team on the system. Write documentation. Provide support. That takes time.
• Security. ISO 27001 certification doesn't come as a byproduct. That's a separate process costing months and tens of thousands.

When custom AI does make sense

There are exceptions. Custom AI can make sense if:

• You have a very specific use case that no existing tool covers and that can't be solved with API configuration.
• You have a team of 500+ recruiters where the scale advantages of custom building outweigh the costs.
• You see AI as core business and want to make a separate product that you sell to others.
• You already have an AI team of 5+ engineers with the in-house expertise.

For 95% of recruitment companies, none of these apply. And even then, it's often smarter to take an existing solution and customize it via an API or custom integration to your specific needs. Why reinvent the wheel when someone's already done it for you?

The alternative: buy and configure

The smartest approach for most companies is: buy a proven solution and configure it to your needs. Don't build, configure.

With Simply, that concretely means:

• Setting up summary formats that match your conversation types and clients. No code, just configuration.
• Configuring CRM field mapping so the right data lands in the right fields. Visual, not technical.
• Activating the integration with your specific CRM. Salesforce, Bullhorn, Mysolution, Byner, Tigris. Click, connect, done.
• Building custom workflows via the API if really needed. For the 5% that isn't covered by default.

That doesn't cost 18 months and a million dollars. It costs a day of setup and an hour of configuration per adjustment. And you have a team that maintains, updates, and improves it for you. Without having to hire developers.

The opportunity cost

The most underestimated argument against building custom is the opportunity cost. Every dollar and every hour you spend building AI, you don't spend on:

• Recruiting more candidates. More conversations, more placements, more revenue.
• Improving your service to clients. Better service, higher satisfaction, more repeat business.
• Training your recruiters. Better interview techniques, more placements per recruiter.
• Growing your business. New markets, new clients, new services.

The question isn't: 'Can we build this?' The question is: 'Is this the best use of our time and money?' For recruitment companies, the answer is almost always no. You're a recruitment company, not a tech company. Focus on what you're good at.

The 'we want control' argument

The most common argument for building custom is control. 'We don't want to depend on an external vendor.' Understandable. But consider:

• You're also dependent on your CRM vendor. On your telecom provider. On Microsoft or Google for your email. Dependency is unavoidable in technology.
• Custom building creates different dependencies. On your development team. On specific engineers. On a codebase only you understand.
• The control you gain by building custom (modifiable code) you lose to complexity (harder to maintain, slower updates, higher costs).

Real control isn't in owning the code. It's in choosing a reliable partner who respects your data, understands your processes, and doesn't lock you in.

How Simply fits your tech stack

Simply is built to fit into your existing workflow. Not to replace it. Integrations with Salesforce, Bullhorn, Mysolution, Byner, and Tigris are native. Enterprise security with ISO 27001 and GDPR compliance is standard. Transparency about how AI reaches conclusions is built in.

Want to know how Simply fits your environment? Read how integrating AI into your ATS works, or get in touch for a concrete demo with your system.