“Should we build our own assistant or just give the team ChatGPT?”—this is one of the first questions we hear. The answer isn’t “always custom” (that’d be sales, not advice). It’s: it depends on what you care about—and the criteria can be listed and calculated.
When an Off-the-Shelf Solution Suffices
#Off-the-shelf tools are great and often enough. Choose them when:
- you need generic assistance (writing, brainstorming, code) without access to your data,
- time matters—launch takes days, not weeks,
- scale is small and predictable, and cost per-user is acceptable,
- you don’t need to integrate the assistant with your systems or meet specific RODO requirements.
There’s nothing wrong with this—it’s a reasonable start. The problem arises when needs outgrow what off-the-shelf can do.
When a Custom Assistant Wins
#A custom solution makes sense when at least one of these requirements appears:
- Answers from your knowledge—the assistant must know your documents, offers, procedures (RAG with citations), not answer “from the internet.”
- Data control and compliance—PII masked before the cloud, sensitive paths handled locally, data may not leave the country.
- Integration with systems—CRM, email, databases, an agent executing actions via allow-list and human-gate.
- Cost at scale—with high, steady traffic, custom (via router on your infrastructure) delivers predictable costs instead of a growing per-seat bill.
- No lock-in—we design it so you can change the model provider, never the other way around.
Decision Table
#| Criterion | Custom | Off-the-Shelf (SaaS) |
|---|---|---|
| Answers from your knowledge (RAG) | Full | Partial |
| Data control / residency | Full | Partial |
| Integration with systems | Full | Partial |
| Launch time | Slow | Fast |
| Startup cost | Medium | Low |
| Cost at scale | Low | High |
| Vendor independence | High | Low |
For the full, interactive version (4 languages, citable), see comparisons.
It’s Not “Either-Or” Forever
#The most common good path is hybrid and phased: start with off-the-shelf where it suffices (quick value), and build a custom assistant for one process where your data and integration matter. This is exactly the logic of piloting: the smallest change with the biggest leverage, then scaling. For specifics, use the stack selection tool, and calculate ROI with the ROI calculator.
Try It Live
#Describe your case, and the model will help preliminarily assess whether off-the-shelf suffices or if custom is worth building (playground: PII masked, zero retention):
FAQ
#Is a custom assistant more expensive than ChatGPT/Copilot?
#At the start, usually yes—off-the-shelf has a low entry cost. But at scale, it flips: per-user/per-message solutions grow with traffic, while custom on your infrastructure has predictable costs. The break-even point depends on the number of users and messages—calculate it in the ROI calculator.
Can I start with off-the-shelf and switch to custom later?
#Yes, and this is often the best path. Start with off-the-shelf where it suffices, and build custom for one process where your data and integration matter. Hybrid is the norm, not a compromise.
Why doesn’t an off-the-shelf chatbot know our data?
#Because it answers “from the model’s memory,” not your documents. To know your knowledge, it needs RAG—searching your database and answering with citations. That’s what a custom assistant builds, while off-the-shelf solutions do this only partially and without data control.
What about RODO with off-the-shelf tools?
#With off-the-shelf SaaS, you have limited control over where and how data is processed. With custom, you mask PII before the cloud, keep sensitive paths local, and design everything for RODO and AI Act from the start. If you process sensitive data, this often decides the choice.