What is Deterministic Quoting?

What is deterministic quoting?

Deterministic Quoting is the mechanism that stops a generative agent from paraphrasing approved content into something unapproved.

Most agents treat approved claims as raw material. They rephrase, compress, or blend them with other text, and the output drifts from what was actually cleared. Deterministic Quoting removes that step. Approved text reaches the patient or HCP exactly as written. Every instance is verified against the source document at the point of output and cited to its page.

Why pharma has never used conversational AI

Conversation is the best engagement format there is. It's the only one built for true personalization. But generative conversation means improvised wording, and no review committee can approve improvised wording in advance. So brands ship one static message to everyone.

The two choices brand teams know

Until now, brand teams had two options.

The first is ungoverned generation: A general-purpose model answers freely. It can word a claim in ways nobody approved, drop required safety information, or drift off-label.

The second is the walled garden: You get control back by confining the AI to a single destination with a fixed content set. It's safer, but the brand loses channel flexibility. Often it can't even show its own ISI or approved claims and the engagement is rented from someone else's platform.

A conversation that adapts while its content stays fixed

Deterministic Quoting pulls every answer word for word from the brand's MLR-approved content library. The model handles understanding and navigation. It works out what the person is asking and locates the approved content that answers it. However, it never writes the medical or promotional content itself.

That way, the conversation can be personalized while the claims stay fixed. The patient or HCP gets a natural, responsive exchange. The brand gets an output where every claim already has been reviewed.

How it works

Source of truth: Your MLR-approved content lives in your CMS: approved claims, indication statements, ISI, and answers to anticipated questions.

Retrieval and navigation: When a patient or HCP asks a question, the model reads the intent and picks the approved content that best answers it.

Deterministic surfacing: The conversational interface shows the selected content and marks it as deterministic, so the reader can see exactly which words came from an approved source and required elements like ISI appear when appropriate.

The pre-approved content loads straight from your CMS, so the moment you update an approved claim, the conversation shows the new wording. There is no second copy to drift out of sync, and no separate review cycle for the AI.

What MLR can review

• Predictability: Every claim is pre-approved. The model cannot rewrite anything that would deviates from the quoted claims.

• Traceability: Each line the model shows, links back to a specific approved asset in the CMS, so a reviewer can open any conversation and see where every line came from.

• Visible marking: Deterministic content is labeled on screen. There is no ambiguity for the patient, the HCP, or an auditor about what is sourced.

• ISI and fair balance: Required safety information appears according to the regulation. Each brand controls both the content set and the logic that shows with it.

• One source of truth: When approved content changes in the CMS, it is synced to the RoseRx library. There are no outdated claims and no parallel approval queue.

What's quoted, what's generated

The model quotes the approved corpus word for word: claims, indication language, dosing, safety information. The framing around it, a greeting, a transition, a clarifying question, is generated within pre-approved bounds.

When the corpus has no answer, the agent says so. It doesn't guess. This is the expected behavior physicians already trust in their daily evidence tools.

One mechanism across every channel

Deterministic Quoting isn't tied to a destination. The same engine, drawing on the same approved corpus, runs across SMS, WhatsApp, email and web.

The examples on this page use US frameworks: MLR, OPDP, 21 CFR 202.1. The mechanism itself doesn't depend on jurisdiction. Entering a new market means loading a different approved corpus under that region's rules.