The custom AI model myth: what vendors are often really selling
Not every company needs a custom AI model. Learn the difference between real model training, RAG, prompts, and product configuration.
"A custom AI model for your company" sounds powerful. Sometimes it is real. Often it means something else: a prompt template, a workflow, a RAG pipeline, or a product configured with your documents.
That can still be valuable. But companies should understand what they are buying.
What can be called custom
Vendors may use "custom AI" to describe:
- A model that has actually been fine-tuned.
- A retrieval system connected to your documents.
- A prompt with company instructions.
- A chatbot interface with your branding.
- A set of integrations and permissions.
These are not the same thing.
Why the distinction matters
If the goal is answering questions from internal documents, a RAG system may be better than a fine-tuned model. If the goal is a repeatable writing style, fine-tuning or examples may help. If the goal is workflow automation, integrations may matter more than model training.
Calling everything "custom" hides the architecture and makes ROI harder to evaluate.
What to ask vendors
Ask:
- Is the model being trained, or are documents retrieved at answer time?
- How are sources cited?
- How are permissions enforced?
- What happens when a document changes?
- Can data be deleted?
- What is actually unique to our company?
- How do we measure success?
Clear answers are more important than impressive terminology.
The practical conclusion
Most companies do not need marketing language. They need a working system that connects approved knowledge, answers with sources, respects permissions, and improves over time.
Polp is positioned around that practical layer, not the myth that every company needs to train its own foundation model.
Sources:
Stop searching. Start asking.
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