Glossary
Foundation model.
A large pre-trained AI model that serves as the underlying capability layer for AI applications. ChatGPT, Claude, Gemini, and Llama are foundation models. Application-layer products like Simply Asking use them as a substrate for reasoning, drafting, and language understanding. They don't replace them.
Foundation models are the engines. Applications are the cars. You don't pick between them. You pick which engine each part of the car needs.
What this looks like in practice
Why "compete with ChatGPT" is the wrong frame.
ChatGPT, Claude, and Gemini are not competitors to a knowledge product. They're general-purpose reasoning engines. They have no idea what your business does. They can't access your files. They generate confident guesses from training data. The right question isn't "which one should we use," it's "which application sits on top of them and grounds them in our business."
Why no single foundation model is best at everything.
GPT models are strong at structured reasoning but expensive on long context. Claude handles long context well but costs more per call. Gemini does multimodal natively. Different strengths, different trade-offs. An application that uses just one inherits its weaknesses across every task.
What an application layer adds.
Retrieval (giving the model access to your actual content), routing (picking the right foundation model for each task), grounding (forcing every answer to cite its source), tool use (letting the model take action), and audit trails (knowing what happened). Foundation models alone do none of this. They're the substrate, not the product.
We don't compete with foundation models. We use them.
Simply Asking routes each task through the foundation model best suited for it, then grounds every answer in your business knowledge. OpenAI, Anthropic, and Google handle the language. We handle the company.
Bring your knowledge.Simply ask.
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