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Pharma Content Intelligence

The only pharma content intelligence platform built to connect the content you approve with the answer AI gives.

It connects pre-MLR asset checks with AI answer monitoring, both measured against the same approved label. Pre-check clears the asset before MLR. Answer Monitor watches ChatGPT, Gemini, Perplexity, AI Overviews and Claude after launch. Both are measured against the same approved label.

Juncture is the approved message intelligence layer. Here is how it is built: inputs in, one engine that reads a single label, traceable outputs out. So every verdict and every finding cites the clause it was measured against.

00/The platform at a glance

Two halves, three engine readers, traceable outputs.

Everything Juncture does, in one view. Pre-check and Answer Monitor read one label. The engine readers and the outputs below each link to where they live on this page.

Pre-check, before MLR

The inside half. Holds every claim, figure and visual to the label before a reviewer opens the asset.

Answer Monitor, after launch

The outside half. Reads how ChatGPT, Gemini, Perplexity, AI Overviews and Claude answer about your brand.

Reuse scoring

One engine reader. Matches each block of an asset back to already-approved content, block by block.

Claim coverage

Tracks which approved claims are present, weakened or missing, measured against the same label.

Image verification

Verifies approved visuals by asset ID, so only cleared imagery clears the pre-check.

Fair balance and ISI

Checks that the fair-balance and important safety information travel with the claim, not apart from it.

Off-label detection

Catches off-label use inside the asset and off-label drift in the AI answer, flagged to the clause.

Part 11-supporting audit trail

A time-stamped, tamper-evident trail with e-signature sign-off and role-based access, that you validate under your SOPs.

Share of Answer

Measures how often your brand is the answer, per AI model, question by question.

AI Pickup

Shows whether the approved message is the one the assistants actually paraphrase back.

Close Gaps

When the machine answers off-label, surfaces the one already-approved module that covers the gap, for a named human to pick and approve. Juncture never writes a claim.

Reference quality

Five checks per claim reference: present, right section, numbers match, current label version, evidence hierarchy.

Prompt Creator

Builds the question set for a brand from voices and moments, then runs any question on demand.

AI Answer Readiness

A 33-check diagnostic of how ready your web presence is to be read and cited by answer engines.

Brand Report

Rolls both halves into one PromoMats-attachable page, shareable by a public, revocable link and PDF.

The inside-to-outside join

The seam. Both halves read one label, so a pre-check verdict and a post-launch finding cite the same clause.

01/Why we are different

Existing controls solve parts of the lifecycle, not the connection.

Each capability adds value, but findings, approved claims, performance data and AI signals live in separate systems and workflows. No shared message model connects what is approved, what is created, what passes review and what AI communicates.

Before review

Pre-checkers

Solves. Catch format, wording or compliance issues before MLR.

Misses. They may not assess whether the asset executes the message.

The building blocks

Claims and module libraries

Solves. Provide approved building blocks for reuse.

Misses. They stay separate from checking, measurement and AI monitoring.

After publication

Content measurement

Solves. Tracks production, reuse and engagement.

Misses. It rarely shows whether priority messages remain intact.

Outside, after launch

AI-answer monitoring

Solves. Reveals how AI describes the product.

Misses. It lacks a connection to approved claims and source clauses.

One source of truth

Juncture

Solves. Connects these controls through one approved source of truth, without replacing the systems you already use to create, review or publish content.

And. One definition of the approved message, consistent checks and traceability, and smarter decisions at every stage of the lifecycle.

04/The loop

One continuous loop, not two tools.

The approved label clears the asset, the asset ships, the answer is watched, and the drift comes back as a fix. Juncture runs the whole circuit as one continuous loop, with the label at the centre of it.

01Define

Approved label

The source of truth the whole loop resolves to.

02Execute

Pre-check

Holds every claim, figure and visual to the label before MLR.

03Execute

MLR-ready asset

Clause-cited and routed for medical, regulatory and commercial sign-off.

04Execute

Content Intelligence

The approved asset joins the system of record: the modular and claims library you reuse and measure.

05Execute

Published content

The approved content goes live across channels.

06Learn

Answer Monitor

Reads ChatGPT, Gemini, Perplexity, AI Overviews and Claude back against the label, on the six Core KPIs.

07Learn

Drift alerts

Off-label, weakened or missing claims in the AI answer, flagged with the clause.

08Learn

Content fix

The finding routes back into the approved core, and the loop runs again.

Define · Execute · Learn, one approved message at the centre. What the answer teaches refines what you define.

01/The architecture

Inputs in. One engine. Traceable outputs out.

Juncture has two halves and one source of truth: the label. The label, your claims library, approved assets and visuals, and the questions HCPs ask are the inputs. One engine reads them. Clause-cited, traceable assets and answers come out.

A · Inputs

What goes in

  • The labelThe source of truth
  • Claims libraryApproved claims and figures
  • Approved assetsAlready-cleared modules
  • Approved visualsTagged by asset ID
  • HCP questionsWhat gets asked in the wild
reads

B · Engine

One label, read for both halves

  • Pre-check

    Holds every claim, figure and visual to the label before MLR.

  • Reuse scoring

    Matches each block back to approved content, tagged exact-match, light-edit or new.

  • Answer Monitor

    Runs HCP questions across the AI assistants and reads the answers back against the label.

Every check, score and finding is measured against the same label.

returns

C · Outputs

What comes out

  • MLR-ready assetChecked, clause-cited, routed for sign-off.
  • Drift alertsOff-label or weakened claims, flagged in the answer.
  • Share of AnswerHow often each engine surfaces your brand.
  • Audit trailTime-stamped, attributed, ready for the record.

Inputs in, one engine reading a single label, traceable outputs out. Clause-cited verdicts on the inside, Share of Answer and drift on the outside, with one audit trail across both.

02/The two halves

Two halves, wired to one label.

Pharma has always run these two jobs apart: the content check inside, the answer outside. In Juncture they are the same engine reading one label. The inside half clears what you ship. The outside half reads what the machine says about it.

Inside · Before MLR

Pre-check governs the content you approve.

Pre-check reads a marketing asset and holds every claim, figure and visual to the label before a reviewer ever opens it. It also shows how much of the asset is reused verbatim from already-approved content, so the asset arrives at MLR already checked, with each verdict citing the clause it was measured against.

  • Reuse from approved content, measured block by blockComposition
  • Claims and figures checked to the labelLabel 1.1, 14.2
  • Approved visuals verified by asset IDAsset registry
  • Off-label use caught and flagged earlyIndication scope
Explore Pre-check→

Outside · After it ships

Answer Monitor watches how AI answers.

Answer Monitor tracks how ChatGPT, Gemini, Perplexity, Google AI Overviews and Claude respond to the questions HCPs actually ask about your brand. It measures Share of Answer, surfaces off-label drift and missing claims, and traces every finding back to the label.

  • Share of Answer, question by questionPer model
  • Off-label drift detected in the answerIndication scope
  • Missing or weakened claim alertsLabel 1.1
  • Every finding sourced to a label clauseCitation trace
Explore Answer Monitor→

03/Inside engine · reuse scoring

One engine output: how much of the asset is already approved.

Reuse scoring is one of the three engine readers on the inside. It matches each block of an asset back to already-approved content, so the reuse score feeds straight into the MLR-ready output.

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

More reuse means less new-claim risk and a faster approval. The full reuse mechanics, block tagging and stringency live on Pre-check. Explore Pre-check.

04/Inside engine · MLR-ready output

The output is an asset that arrives already checked.

The inside engine produces an MLR-ready asset: clause-cited, routed to a named reviewer, and recorded. The accountable decision stays with the human reviewer.

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

Juncture provides the technical controls for 21 CFR Part 11: a time-stamped, tamper-evident audit trail, e-signature sign-off, and role-based access control. You validate it for Part 11 use under your own SOPs. The full pre-check workflow and sign-off detail live on Pre-check. Explore Pre-check.

05/Outside engine · Answer Monitor

On the outside, the engine reads the AI answer.

Answer Monitor is the third engine reader. It runs the questions HCPs ask across the AI assistants, measures Share of Answer per engine, and flags off-label drift, all read back against the same label the inside half used.

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

The Share of Answer definition, question strategy and the close-the-gap loop live on Answer Monitor. Explore Answer Monitor.

06/The seam

The seam between inside and outside.

Inside is the content you approve. Outside is the answer the machine returns. The seam is the structural join between them. Juncture wires both halves to one label, so the seam is something the architecture measures, not a gap.

Inside · The approved messageOutside · The machine's answer

The seam is the juncture. It is the one thing Juncture watches as a single, continuous join.

07/The source of truth

One label. Both halves measured against it.

Pre-check and Answer Monitor are not two scores. They are the same label, read twice: once to clear the content you ship inside, once to judge what the machine says about it outside.

Traceable

Every verdict cites a clause.

Nothing is a black-box score. Each cleared check and each flag points to the exact label clause it was judged against, so reviewers can audit the decision.

Continuous

Inside informs outside.

Both halves read the same label, so they never drift apart. When the label changes, the change propagates to both engines at once and the two halves stay in step.

Quotable

Built to be answered.

The same definitional, sourced content that clears MLR is the content an answer engine can quote correctly. Govern the message and the answer improves.

One source of truth, wired to both halves of the engine.

08/What you get

Two artifacts come out: a closed gap, and a brand report.

The architecture is not the deliverable. What you get is on the screen and in the file: a detected gap closed with approved content, and a brand-level report you can attach to the next review. Both trace back to the same label.

Illustrative view, fictional brand Varigel. The machine answers off-label, and Juncture surfaces the one already-approved module that covers the gap, cited to the clause it is cleared against. A named reviewer picks and approves it before anything moves.

Never a new claim

Close Gaps only ever surfaces a module MLR already cleared. It never drafts, rewrites or invents a claim, and it never composes a fix.

Human approves

Juncture surfaces the approved module to a named reviewer. The reviewer picks and approves it, and nothing publishes on its own.

Cited to a clause

The approved module carries the label clause it is cleared against, so the chosen module is defensible the moment it lands.

Illustrative brand report, fictional brand Varigel and fictional reviewer. A Content Health Score, claim coverage, Share of Answer across the AI engines, an open drift count and the top gaps to close, exported as a Part 11-supporting, PromoMats-attachable file.

The brand report rolls both halves into one page a brand lead can hand to medical and attach to the next MLR review. It is a Part 11-supporting export: the controls are provided, and you validate it under your own SOPs. The clause citations travel with it.

04/Worked examples

Four asset types, one approved label

Different formats, the same control point. Each example is cleared before MLR against the approved modules, then watched in the AI answer after launch. Every brand and number here is invented and illustrative.

HCP email

A rep-triggered HCP email for the fictional brand Varigel. Cleared before MLR with 81% reuse, then one missing-interaction drift caught on Perplexity after launch. Illustrative.

DTC display banner

A consumer display banner for the fictional brand Nelora. Cleared before MLR with 64% reuse, then one off-label drift caught in AI Overviews after launch. Illustrative.

Rep sales aid

A field sales aid for the fictional brand Cedavix. Cleared before MLR with 88% reuse, then one overstated speed-of-onset drift caught on Gemini after launch. Illustrative.

Congress deck

A congress slide deck for the fictional brand Olvexa. Cleared before MLR with 72% reuse, then one indication-overreach drift caught on ChatGPT after launch. Illustrative.

Where Juncture fits in your stack

A layer, not another workflow to move to.

The honest answer to the question every team asks. Juncture is a pre-MLR pre-check and Share-of-Answer layer that sits alongside your MLR system of record, not a replacement for it. Reviewers consume its findings in the system they already use, and the accountable decision and the final sign-off stay with your people. That is the point, and it is the reason switching cost is low.

The Juncture layer
Inside

Pre-MLR pre-check

Holds every claim, figure and visual to the label before the asset enters MLR.

Outside

Share of Answer

Reads how the AI engines answer about the brand after launch, against the same label.

Inside

Findings and trail

Clause-cited findings and a Part 11-supporting trail you attach to your MLR submission.

Findings down, decision stays below
Your MLR system of record, unchanged
  • Veeva PromoMats / Vault
  • Aprimo
  • Zinc
  • Adobe Experience Manager

The system that holds the accountable record keeps holding it. The reviewer opens Juncture's findings, or consumes them where they already work, and signs off where they always have.

What Juncture does

  • Pre-checks the asset against the label before MLR
  • Surfaces clause-cited findings for the reviewer to consume
  • Tracks Share of Answer and off-label drift after launch
  • Hands back an audit-ready report you attach to a submission

What stays yours

  • The accountable MLR decision
  • The final e-signature sign-off
  • The system of record and the audit record
  • The review workflow your team already runs

Low switching cost

Juncture connects these controls through one approved source of truth, without replacing the systems you already use to create, review or publish content. There is nothing to migrate and no workflow to rip out. You point it at the label and the claims you have already approved, run a brand and an asset through it, and read the findings in the system you already trust. The decision was always yours, and it stays yours.

Integrations

Fits the stack you already run.

Juncture sits alongside the MLR systems of record you already run. It takes your label, your claims library and your approved modules as inputs, accepts a manual upload when an asset is not yet in a connected system, signs people in through your identity provider, and hands back an audit-ready report you attach to a PromoMats submission. What is available today and what is on the roadmap are stated plainly, never mixed.

Complements your systems of record

Veeva PromoMats / Vault

MLR system of record

Aprimo

Content operations

Zinc

Review and approval

Adobe Experience Manager

DAM and delivery

CLM / CRM

Field and channel delivery

Juncture reads from these as inputs and hands findings back to them. It complements them, it does not replace them.

Available today

Input

Claims library and approved modules

Ingest your approved claims and approved content modules so pre-check measures every block against what you have already cleared. Input, available today.

Input

Label and source ingestion

Bring in the approved label and supporting sources. They become the single reference both pre-check and Answer Monitor are measured against. Input, available today.

Upload

Manual asset upload

Drop an asset in directly when it does not yet sit in a connected system. A file drop is all it takes to run a pre-check, no integration required.

Identity

SSO via Microsoft Entra

Sign in through Microsoft Entra with SAML or OIDC, scoped to a role. Available today.

Output

Audit-ready report for PromoMats

Export an audit-ready report and attach it to your PromoMats or MLR submission. The checks and the trail travel in one document, available today.

Method, today

Today the connection is ingestion and manual upload in, and an audit-ready report out. Live connectors and a public API are on the roadmap below, not yet shipped.

On the roadmap

Roadmap

Veeva Vault connector

A live connector to route a cleared asset and its findings straight into Veeva Vault PromoMats, instead of a file drop and an attached report. In development, not yet generally available.

Roadmap

DAM and asset-registry pull

Pull assets straight from your DAM or asset registry, including Adobe Experience Manager, so a reviewer opens to the work, not to a file hunt. In development, not yet generally available.

Roadmap

Public API

A public API to wire pre-check and Answer Monitor into your own pipelines, CLM and dashboards. Planned, not shipped today.

Roadmap

OpenEvidence integration

OpenEvidence, the clinician-facing engine, is a planned integration for Answer Monitor. Not part of the engine set monitored today.

Enterprise-ready

  • SSO via Microsoft Entra
  • SAML and OIDC
  • Role-based access control
  • Encryption in transit and at rest
  • Customer-controlled retention
  • No customer content used to train models
  • EU region available
  • DPA available

09/Questions

The platform, answered.

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

How is the Juncture platform built?
As one architecture: inputs in, one engine, outputs out. The inputs are the label, the claims library, approved assets and visuals, and the questions HCPs ask. The engine is three readers of that single label: pre-check, reuse scoring and Answer Monitor. The outputs are an MLR-ready asset, drift alerts, Share of Answer and one audit trail across both halves.
How are Pre-check and Answer Monitor connected?
They read the same label. Pre-check uses the label to clear what you ship. Answer Monitor uses the same label to judge what the machine says about it. Because both halves measure against one source of truth, a finding on either side traces back to the same clause, and a label change moves both halves together.
What is the seam, in architecture terms?
The seam is the structural join between the inside engine that clears your content and the outside engine that reads the AI answer. MLR governs the asset and nobody governs how an AI assistant paraphrases the brand, so the two have always lived apart. Juncture wires them to one label, which makes the seam a measurable part of the architecture rather than a gap.
Where does content reuse fit in the architecture?
Reuse scoring is one of the three engine readers. It sits alongside the pre-check on the inside, matching each block of an asset back to already-approved content. The reuse score is an input to the MLR-ready output, so reviewers see what is genuinely new before they open the asset. The full reuse mechanics live on the Pre-check page.
Does Juncture replace MLR review?
No. The pre-check backs the reviewer and does not replace them. Juncture provides the technical controls for 21 CFR Part 11: a time-stamped, tamper-evident audit trail, e-signature sign-off, and role-based access control. You validate it for Part 11 use under your own SOPs. The accountable MLR decision stays with the human reviewer.
Which AI models does the platform cover?
Answer Monitor, the outside engine, tracks the assistants HCPs actually use, including ChatGPT, Gemini, Perplexity, Google AI Overviews and Claude. It measures Share of Answer per model and flags off-label drift and missing claims, tracing each finding back to the label clause. OpenEvidence, the clinician-facing engine, is a planned integration, not part of the set monitored today.
What does Juncture integrate with?
Juncture takes your approved label and sources, and your claims library and approved modules, as inputs, and signs people in through Microsoft Entra with SAML or OIDC. On the way out, it hands back an audit-ready report you attach to a PromoMats submission, available today. An asset registry or DAM pull, a live Veeva Vault connector, a public API, and an OpenEvidence integration are on the roadmap, in development and not yet generally available, and the page states plainly which items are available today and which are roadmap.
What happens after an alert is found?
A drift alert traces back to the exact label clause it was measured against, so the team can see whether the AI answer is off-label, weakened or missing a claim. The finding routes back into the loop as a content fix: you correct or strengthen the approved content, the asset runs through Pre-check again, and the corrected message is what gets republished. The accountable decisions stay with your people. Juncture supplies the trace and the record.
Is the platform enterprise-ready and secure?
Yes, on the controls that are in place today. Juncture is GDPR compliant, customer content is never used to train models, it runs SSO via Microsoft Entra with SAML or OIDC, role-based access control, encryption in transit and at rest, an EU region, customer-controlled retention and deletion, audit logs, and a DPA is available. For 21 CFR Part 11 it provides supporting technical controls, including a time-stamped, tamper-evident audit trail and e-signature sign-off, that you validate under your own SOPs. SOC 2 is in progress and not yet complete.

10/For the evaluator

The diligence questions, answered straight.

The questions a digital, MLR, security and procurement team asks before a pilot. Where it fits your MLR stack, the Part 11 posture, how the rules configure, which models run, where the data lives, and what a pilot measures. Current fact only, roadmap labelled as roadmap.

How do you fit our existing MLR stack (Veeva, Aprimo, Zinc)? Are you a workflow system or a layer?
A layer, not a workflow system. Juncture is a pre-MLR pre-check and a Share-of-Answer layer that complements your MLR system of record, whether that is Veeva PromoMats or Vault, Aprimo, Zinc or AEM. It does not replace your MLR workflow. Reviewers run an asset through the pre-check, see the findings against the label with each clause cited, and consume those findings in your own system. The accountable MLR decision and the final sign-off stay with your people and your system of record. Today you bring assets by ingesting your claims library and approved modules, ingesting the label and sources, or uploading an asset, and you hand back an audit-ready report you attach to a PromoMats or MLR submission. A live Veeva Vault connector and a DAM or asset-registry pull are on the roadmap, in development and not yet generally available. Because the system of record does not change, switching cost is low.
What is your 21 CFR Part 11 / GxP and validation approach?
Juncture is Part 11-supporting decision support, not a validated GxP system of record. It provides the supporting technical controls Part 11 calls for: a time-stamped, tamper-evident audit trail, e-signature sign-off, and role-based access control. It backs the human reviewer rather than replacing them. You validate Juncture for Part 11 use under your own SOPs and run it inside your own quality framework. We say Part 11-supporting on purpose, never Part 11 compliant, validated or certified, because that determination belongs to you. SOC 2 is in progress and on the roadmap, not yet complete.
How configurable are the off-label rules by region and product?
The rules are tied to the approved label and are configurable per product and per market, so you can codify your own label interpretations and risk tolerances for each affiliate. Claim extraction is AI-assisted, and the rule evaluation itself is deterministic: every verdict cites the controlling label clause it was measured against, so a finding is traceable rather than a black box. A label change moves the rules with it, and the same label drives both the pre-check on the inside and the answer monitoring on the outside.
Which AI models do you use, and are our prompts or content used to train shared models?
Inference runs on Azure OpenAI, GPT-class models, and your content is never used to train models, shared or otherwise. For answer monitoring, Juncture tracks the five assistants HCPs actually use: ChatGPT, Gemini, Perplexity, Google AI Overviews and Claude. Those probes use only public, HCP-style questions, never your proprietary data sent into a model. OpenEvidence is a planned roadmap integration, not an engine monitored today.
Where is data hosted, and how is our tenant isolated? Is there an EU region?
Juncture is multi-tenant SaaS on Microsoft Azure with per-tenant data isolation, and an EU region is available for customers who require EU data residency. Identity is SSO via Microsoft Entra with SAML or OIDC, with role-based access control across admin, contributor and viewer. Data is encrypted in transit and at rest, the platform is GDPR compliant, audit logs are kept, retention and deletion are customer-controlled, and a DPA is available.
Do you handle PHI or PII?
No PHI or PII is required. Juncture works on promotional content, the approved label, and public HCP-style questions. It does not ingest patient data. The Answer Monitor probes use only public questions, so no proprietary or patient data is sent into a model. This keeps the data footprint small and the security review straightforward.
What does a pilot look like, and how do you measure impact?
A pilot is deliberately small: bring one brand, one asset, and a public question set, and we stand it up in weeks. We measure impact on your own data rather than quoting numbers from elsewhere. The signals are the pre-check catch-rate before MLR (issues caught before a reviewer opens the asset), the effect on MLR cycle-time, off-label drift caught in the AI answer, and the change in Share of Answer run over run. You see those measured on your brand during the pilot, not as a generic claim.
Do you provide quantitative benchmarks?
Not as published client results, because there are none to quote yet, and we will not invent them or restate competitors numbers as ours. A Share-of-Answer Benchmark built on real data is planned. What we do today is measure impact on your own brand in a pilot: pre-check catch-rate before MLR, MLR cycle-time, off-label drift caught, and Share-of-Answer change run over run. That gives you a defensible number from your own assets rather than a borrowed one.

See the platform run

See both halves, joined, on your brand.

Bring an asset and a brand. We will pre-check the asset against the label, show how much of it reuses approved content, and show how the machine answers about the brand today, in one view.

Book a demoSee how it works