Patched AI or native AI: the difference that matters

RAG (retrieval augmented generation) means the system searches your documents for relevant passages before it writes anything. A generic RAG does this for a whole conversation: it pulls documents that look close to the topic and lets the model sort them out. One RAG per field narrows the search to the exact item you are filling in, a risk, a measure, a pre-assessment field, and injects only the passages that bear on it. The difference shows when you check the output: each line points back to the passage that justifies it, not to a pile of documents.

Not at the model level: a language model remains probabilistic, and two runs can phrase things differently. What matters is that the result can be verified. At Vailor, every proposal starts from the field to fill in and the excerpts of your documents that concern it; when it relies on a passage, it quotes it, and Vailor checks word for word that the passage is in the document. An expert reviews, corrects and validates, and that decision is traced. That is the opposite of a chatbot on its own, where the quality of the answer depends on how the question was phrased and on what the conversation happened to contain.

No, and that makes a useful buying criterion. When the result depends on the wording, the tool shifts the work onto the user: an experienced practitioner gets a sound analysis, a newcomer gets a plausible one, and nothing tells them apart on reading. A native platform starts from the object being filled in and its context, not from an instruction someone typed by hand. The skill expected from the team goes back to being its own, reviewing and deciding, rather than writing instructions to a machine.

Yes. Nothing makes generated content infallible, and a vendor who claims otherwise is asking you to take its word for it. What changes between the two approaches is the cost of checking. When every element points back to its source and to the field it belongs to, an error is spotted quickly and corrected where it originated. When the text comes out of a chat window and is pasted into a document, checking means rereading everything with no idea where each sentence came from. Human review remains the condition for validation either way.

It keeps your compliance work from being tied to a single model provider's catalogue. A model is retired, its pricing changes, its terms of use move: with an architecture bound to one provider, those decisions become yours to absorb. At Vailor you choose the model, including one self-hosted on your premises. In our SaaS, data is stored in France (AWS Paris region) and the models run on AWS Bedrock in EU regions. Hence two separate questions worth putting to any vendor: where your documents live, and where the models called during processing actually run.

It depends on the use. The European AI Act, adopted in 2024, grades obligations by level of risk: internal productivity uses carry few, while systems classed as high risk concentrate the requirements, including for the organisation deploying them. The regulation is not a framework an organisation gets certified against: compliance is shown use by use. ISO/IEC 42001, which sets out an artificial intelligence management system, is certifiable. In both cases a maintained inventory of your AI systems is the starting point.

Because there is nothing structured for it to work with. In a spreadsheet, a risk, the control that treats it and the evidence that supports it are three cells with no declared link, and the reasoning behind a rating stays in the head of whoever typed it. An AI plugged into that can only comment on text: it cannot tell which field its output belongs to, or what needs updating when a decision changes. Structuring the objects and their links first is what makes the work automatable afterwards, and defensible at audit.