I get the temptation. You're smart. You’ve got great engineers. You read a few LangChain tutorials and now you’re 3 prompts away from “revolutionizing customer support.”

For many teams, the next question becomes: should we just build our own?

And to be fair — there are real reasons to build. More control, more flexibility, full ownership of the stack. But before you dive in, it’s worth going in with eyes open. Because while the demo might come together in a weekend, turning that into something reliable, scalable, and production-ready is a much steeper climb.

If you’re weighing up whether to build or buy, here’s how to think about it.

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Build

Pros

The main reasons teams choose to build come down to control and customization. If data ownership and security are top priorities, building gives you full control over how data is stored, processed, and retained. And if your internal workflows are highly specific or tightly coupled with other systems, building lets you tailor the agent to fit exactly how your team works.

Cons

1. It’s harder than it looks.

Every build-first story starts the same: someone on your team wires up OpenAI with a Slack bot over the weekend. It answers one internal question. Everyone’s impressed.

Most teams start by wrapping GPT around help docs. That gets you quick wins — FAQs, static content. But once you want an agent that actually resolves tickets end-to-end, things escalate quickly. You’ll need a search and retrieval system that stays current with product changes, access to internal systems, action-taking capabilities, escalation logic, and more. At that point, you’re not just writing prompts — you’re building infrastructure.

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2. Maintenance is higher than your ex’s emotional needs.

AI is not like building a typical SaaS app. With something like React, you can build once and not worry for 3-5 business years. AI doesn’t work that way. Model new versions come out every month. Infrastructure updates every week. What worked yesterday might break tomorrow — or just get worse.

If you don't devote time to update code with new models/apis/frameworks every week, soon you're knee-deep in hallucinations and agents that “don’t sound quite right.” Durable AI systems are hard - it’s active, forever investment.

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3. The Cost Is Way Higher Than You Think

It all comes down to cost, so let’s do the math:

• An AI engineer = ~$200K/year
• You’ll need at least 2 of them
• Oh, and LLM cost, compute cost, eval tooling, observability, etc.

That’s easily $500K+ annually. Even if the software is “free,” the team maintaining it definitely isn’t.

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Buying

Pros

1. Full package from day one

No need to spend months tweaking prompts or chasing down weird edge cases. You get a system that already works — tested across thousands of tickets and ready to go live without the drama. The supporting infrastructure is baked in too: testing foundations, observability, feedback systems, etc.

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2. A team obsessed with support automation — so you don’t have to be

When you buy, you get a whole crew of AI nerds constantly tuning, testing, and improving. Your engineers are expensive. Every hour spent debugging bugs and patching flaky search is an hour not spent building your core product. Unless your core mission is “build a slightly worse version of something you could’ve bought,” this is a distraction tax you don’t want to pay.

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3. Predictable cost

No surprise infra bills, no adding three engineers just to keep the lights on. You know what you’re paying, and it usually maps cleanly to ticket volume — which makes the ROI conversation way easier.

Cons

1. Limits on customization

If you rely on internal tools or need to query in-house systems, some additional customization will likely be required. Same goes for teams with complex logic around how different ticket types should be handled — or specific rules for how scenarios are escalated and resolved. If your workflows are highly tailored, a one-size-fits-all solution may fall short without some extra configuration.

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2. Navigating data compliance

AI raises fresh questions around data handling — especially with hosted solutions. Make sure your vendor offers a solid DPA, enterprise-grade contracts with model providers (like OpenAI), and clear policies on data storage and retention.

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Conclusion

Build if:

• You have 1M+ monthly support tickets
• You already employ a full-stack AI team
• You’re allergic to vendors

Buy if:

• You want something that works
• You want to move fast
• You want your team focused on what actually matters

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That’s why we built Duckie — so you don’t have to. At Duckie, we take a hybrid approach. You get a strong foundation out of the box — a support agent that can answer and resolve tickets with solid retrieval and action capabilities — plus the flexibility to make it your own with workflows and tools.

We give you the building blocks: agent tools and workflows builders. You can define triggers, conditions, and actions to tailor how Duckie handles different ticket types or scenarios. You can plug it in your own tools/systems, and fine-tune complex behaviors. It’s plug-and-play where you want it, and fully customizable where you need it.

We obsess over AI support, so you can focus on building the thing you actually want to be known for.

Want to see Duckie in action? Book a demo.

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P.S. If you’re still considering building your own AI agent, I’d genuinely love to chat. Either to help you avoid the pain... or to hear your war stories later. Both are fun. 

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