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How it works

From the label to the answer, in one loop.

Juncture is the only platform that pre-checks the content you approve inside and monitors how AI answers about your brand outside, and joins them. It connects the label as the source of truth, pre-checks assets against it before MLR, and watches how the machine answers, so what you approve sets the baseline the answer is measured against.

01/The sequence

From the label to the answer, step by step.

One loop, six steps. Connect the label, pre-check before MLR, ship the approved message, then watch and close the gap on how the machine answers.

  1. Connect

    Connect the label as the source of truth.

    Point Juncture at the approved label and your asset registry. This becomes the single reference every check and every answer is measured against.

    Label clauses, approved claims, registered visuals.

  2. Drop

    Drop in a marketing asset.

    A DTC tile, an HCP email, a detail aid. Juncture reads the asset the way a reviewer would, then begins checking it line by line.

    Claims, figures, images, fair balance, safety.

  3. Pre-check

    Pre-check the asset before MLR.

    Each claim, figure and visual resolves against the label in seconds. Off-label use is caught early. Every verdict cites the clause it was checked against.

    Cleared or caught, with a clause for each.

  4. Ship

    Ship the approved message.

    The reviewer opens to a decision, not a blank canvas. The asset clears MLR already checked, and what you approve becomes the baseline.

    The approved message, on the record.

  5. Monitor

    Monitor how the machine answers.

    Answer Monitor watches how ChatGPT, Gemini and Perplexity answer about the brand. It measures Share of Answer and flags drift, sourced back to the label.

    Share of Answer, off-label drift, missing claims.

  6. Close

    Close the gap with approved content.

    Where the answer drifts from the label, Juncture shows the clause it missed and the approved content that closes the gap. Inside informs outside.

    The loop stays closed against one source of truth.

What you approve sets the baseline the answer is measured against.

02/The loop

One closed loop, not two tools.

Inside informs outside. The label clears the message before MLR, and the same label judges the answer the machine gives after it ships. Close the loop, and the seam stops drifting.

Inside

The approved message, cleared against the label before MLR.

The label

The single source of truth that joins both halves.

Outside

The machine's answer, judged against the same label.

03/The flow

The only platform that joins inside and outside, end to end.

It pre-checks the content you approve inside and monitors how AI answers about your brand outside, and joins them against one source of truth. Here is what each step looks like.

01/Inside

Pre-check the asset against the label, before MLR.

Drop in an asset and the verdict resolves line by line. Each claim, figure and visual clears or is caught against the label, and every line cites the clause it was measured against. This backs the MLR reviewer, it does not replace them: the reviewer opens to a sourced decision, not a blank canvas.

The pre-check view for the fictional brand Varigel. Five checks cleared, one off-label use caught, each tied to a label clause, before MLR. Illustrative content.

02/Inside

See how much is reused from already-approved content.

Alongside the claim and rule checks, the pre-check shows how much of the asset is reused verbatim from content you have already approved. A reuse percentage and a block-by-block breakdown mark each piece exact match, light edit, or new. More reuse means less new-claim risk and a faster approval, because only the genuinely new copy needs fresh review.

75 percent of this Varigel asset is reused from approved modules. Only the new headline is routed for a fresh claim check. Illustrative numbers.

03/The seam

The reviewer signs off, with a Part 11 trail behind it.

A named reviewer makes the call and applies an e-signature sign-off. Every check, override and approval is time-stamped for a 21 CFR Part 11 audit trail you can export on demand. The pre-check makes the decision faster to reach and easier to defend; the person still decides.

The four-step rail from flagged to published, a Part 11 audit trail and an e-signature sign-off with fictional reviewer names. The pre-check backs the reviewer, it does not replace them.

04/Outside

The same label judges how the machine answers.

Once the approved message ships, Answer Monitor asks the questions HCPs type into ChatGPT, Gemini and Perplexity and scores each answer against the same label that cleared the asset inside. It measures Share of Answer, flags off-label drift and missing claims, and traces every finding to the clause it breaks. What the machine gets wrong outside tells you what to approve inside. End to end, in one loop.

04/Questions

How it works, answered.

The short version, in the shape an answer engine can quote.

Run the loop on your brand

Walk it through on a real asset.

Bring an asset and a brand. We will pre-check the asset against the label and show how the machine answers about the brand today, end to end.

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