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Content Intelligence for pharma

Where the approved messagemeets the machine's answer.

The only platform that pre-checks the content you approve inside and monitors how AI answers about your brand outside, and joins them. End to end, inside and outside, the seam nobody else owns.

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Pharma's message is at a juncture.

01/The problem

The message you approved is not the answer the machine gives.

A juncture is the point where two things join, and a decisive moment. Pharma is at both. The approved message and the machine's answer have started to pull apart, right when HCPs are switching how they ask.

Inside

You approve the message.

Every claim, image and statement clears MLR against the label. The asset is correct the moment it ships.

The shift

HCPs ask a machine instead.

Clinicians and patients move from search to ChatGPT, Gemini and Perplexity. The question goes to a model you do not control.

Outside

The answer drifts.

The machine paraphrases, mixes sources and fills gaps. What it returns is not always the message you approved.

02/Inside · before MLR

Drop in an asset. Watch the verdict resolve.

Juncture reads a marketing asset and checks it against the label before MLR. Each line resolves in seconds, and every verdict cites the clause it was checked against. It catches rule breaks before MLR so the reviewer opens to a decision, not a blank canvas.

Illustrative view, fictional brand Varigel. Claims present, visuals verified, rules cleared, off-label caught, each cited to a label clause.

Pre-check backs the MLR reviewer, it does not replace them. It makes the review faster and safer, and logs every step for the record.

Explore Pre-check

03/Inside · the value

See how much you are reusing, not just what you are claiming.

Pre-check shows how much of an asset is reused verbatim from content you already approved, alongside the claim and rule checks. Reuse is the tangible value: less new-claim risk, a faster approval.

Illustrative view, fictional brand Varigel. Each block is matched back to the approved module it reuses, so the new copy is the only thing that needs fresh review.

Reuse, measured

A reuse percentage and a block-by-block breakdown show exactly how much of the asset is already-approved content.

Risk, narrowed

Exact-match and light-edit blocks carry no new claim. Only the genuinely new copy needs fresh review.

Approval, faster

Reviewers spend their time on what changed, not on re-reading what they already cleared.

04/Inside · MLR-review-ready

Built to back the reviewer, not replace them.

Pre-check makes MLR faster and safer. It flags issues before review, keeps a 21 CFR Part 11 audit trail, and routes each asset to a named reviewer for an e-signature sign-off.

Illustrative view, fictional brand and reviewers. Flag, assign, sign off, publish, with every step logged for the record.

Backs the reviewer

It does not replace MLR.

Pre-check catches rule breaks before the asset reaches MLR. The reviewer still decides, and now opens to a shortlist instead of a blank page.

On the record

A 21 CFR Part 11 trail.

Every check, assignment and edit is time-stamped and attributed, so the review stands up to scrutiny long after it ships.

Signed off

A named e-signature.

The asset is assigned to a reviewer who signs off with an e-signature. Faster and safer, with accountability intact.

05/Outside · after it ships

Then watch how the machine answers about your brand.

HCPs are moving from search to AI assistants. Answer Monitor asks the questions they ask, measures your Share of Answer across the engines, and flags off-label drift the moment it appears, traced back to the label clause.

Illustrative view, fictional brand Varigel. Share of Answer across ChatGPT, Gemini, Perplexity and AI Overviews, with an off-label drift flagged on a sampled answer.

The same drift you pre-checked inside is the drift you watch for outside. One brand truth, two ends of the same seam.

Explore Answer Monitor

06/The join

The seam nobody else owns, between inside and outside.

The only platform that pre-checks the content you approve inside and monitors how AI answers about your brand outside, and joins them. Inside is the message you approve. Outside is the answer the machine gives. Juncture is the join, watched as one continuous thing.

Inside · The message

What you approved

Claims, images and rules cleared against the label, with the reuse from approved content made plain. Pre-check holds the asset to the source of truth before it ships.

Outside · The answer

What the machine returns

Share of Answer, off-label drift and missing claims, tracked across the engines HCPs actually ask.

One source of truth

Your message, intact from the label to the answer.

07/The platform

Two halves, one source of truth.

Pre-check governs the message inside, before MLR. Answer Monitor watches the answer outside, after it ships. Together they close the loop on the seam between them, the one nobody else owns.

Inside

Pre-check

Verify a marketing asset against the label before MLR, and see how much of it reuses approved content. The reviewer opens to a decision.

  • Claims, figures and rules checked to the label
  • Reuse from approved content, block by block
  • MLR-review-ready with a Part 11 audit trail
Pre-check→

Outside

Answer Monitor

See how ChatGPT, Gemini, Perplexity and AI Overviews answer about your brand, and where they drift.

  • Share of Answer by question and engine
  • Off-label and missing-claim alerts
  • Sourced back to your label
Answer Monitor→

The join

One continuous loop

Inside informs outside. What you approve sets the baseline the machine is measured against.

  • Inside meets outside in one view
  • Drift traced to a clause
  • One source of truth
The platform→

08/Questions

Questions, answered.

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

See it on your brand

See Juncture run 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.

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