Skip to content

Content Intelligence

Your approved content, as a system of record.

Content Intelligence is the middle of the three Juncture products: a modular content library and a PromoMats-shaped claims library, with reuse scoring and AI Pickup. It is the approved core that Pre-check checks against before MLR and Answer Monitor measures in the wild after launch, all against the same approved label.

00/Content Intelligence · the complete set

The approved-content estate, made measurable.

Content Intelligence turns a folder of approved files into a system: claims and modules modeled as reusable objects, a where-used ledger, reuse scoring on every new asset, claim coverage across markets, and AI Pickup that shows which approved content the models actually repeat.

Modular content library

Approved content as reusable modules, not one-off files, each carrying its approval provenance.

PromoMats-shaped claims library

Claims modeled as objects with variations, supporting references, and a where-used ledger across assets.

Semantic where-used

Find every asset a claim is used in, including paraphrases and translations an exact-match search misses.

Reuse scoring

Each new asset broken into blocks tagged exact-match, light-edit or new, matched back to approved modules.

Claim coverage

Which approved claims and themes your content carries across markets, and where a gap is left open.

AI Pickup

How much of your approved content the AI engines echo, split into a surfacing set and an invisible set.

Brand-level rollup

A Content Health Score, coverage and AI Pickup rolled into one brand picture, exported and attachable.

One approved label

The same approved claim set that Pre-check checks against and Answer Monitor measures in the wild.

Part 11-supporting provenance

Time-stamped, attributed records on every claim and module. You validate it for Part 11 under your SOPs.

01/The system of record

A claims library, not a folder of files.

Content Intelligence models your approved content as objects, not documents. Each claim carries its variations, the references that support it, and a ledger of every asset it is used in. Each module is a reusable, approved block. The approved core becomes something you can search, reuse and measure, instead of re-finding it in a drive.

Illustrative view, fictional brand Varigel. Approved modules and claims as first-class objects, each with its references and a where-used count across markets and assets.

Claims as objects

Each approved claim carries its variations and the references that support it, so it can be reused, not re-litigated.

Where-used ledger

Every asset a claim appears in is tracked, including paraphrases and translations, so a change propagates with its evidence.

Provenance intact

A reused block travels with the record of the clause it was cleared against, so reuse never loses its approval trail.

02/Reuse scoring

Measure how much you are reusing, block by block.

Every new asset is broken into content blocks and matched back to the approved modules and claims it reuses, tagged exact-match, light-edit or new. A high reuse score means most of the asset carries no new claim, so only the genuinely new copy needs fresh review. Reuse is the tangible payoff of a managed claim estate.

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

03/Claim coverage

Which approved claims your content actually carries.

Coverage is the other half of reuse: which approved claims and themes your content carries across markets, and which it leaves on the table. Content Intelligence maps the estate to your approved claim set, so a gap is visible before launch and rolls up into the brand picture rather than hiding in a single asset.

Illustrative view, fictional brand Varigel. Each row an approved message theme, each cell a market, scored covered, partial or gap, with a coverage percent per theme.

04/AI Pickup

See which approved content the machines actually repeat.

AI Pickup is where Content Intelligence meets the outside world. It measures how much of your approved content the AI engines echo back to HCPs, and splits the estate into a surfacing set the models repeat and an invisible set no model is using. The modules the machines repeat are the ones earning their MLR effort; the invisible ones are the gap to close.

Illustrative view, fictional brand Varigel. Approved modules split into a surfacing set the engines echo and an invisible set, measured per engine across ChatGPT, Gemini, Perplexity, Google AI Overviews and Claude.

Surfacing set

The approved modules and claims the engines actually repeat to HCPs, the content earning its MLR effort.

Invisible set

Approved content no model is using, the gap to close before a competitor or an unmanaged source fills it.

Per engine

Measured across ChatGPT, Gemini, Perplexity, Google AI Overviews and Claude, so the gap is engine-specific.

05/The brand rollup

One approved core, three products, one label.

Content Intelligence holds the approved core. Pre-check clears a new asset against it before MLR; Answer Monitor measures how much of it the engines repeat after launch. Because all three read the same approved label and claim set, an AI Pickup gap or an answer that drifts traces back to a specific approved clause, not a free-floating opinion.

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

Juncture is the only pharma content intelligence platform that joins a pre-MLR asset check, an approved-content system of record, and AI answer monitoring, all measured against the same approved label. The same approved core you reuse inside is the core the machine learns to repeat outside.

Enterprise-ready · for procurement

  • 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

Content Intelligence · questions

Questions about Content Intelligence.

Plain answers to what teams ask about the approved-content system of record.

What is Content Intelligence in Juncture?
Content Intelligence is the approved-content system of record: a modular content library plus a PromoMats-shaped claims library (claims, variations, references and a where-used ledger), with reuse scoring and AI Pickup. It is the middle of the three Juncture products. Pre-check checks an asset against this approved core before MLR, and Answer Monitor measures how much of it the AI engines repeat after launch, all against the same approved label.
How is this different from a DAM or Veeva PromoMats?
A DAM stores files and PromoMats routes them through MLR and holds the approved asset. Content Intelligence is the layer that makes that approved content measurable: it models claims and modules as reusable objects, scores how much new work reuses them, and tracks where each claim is used. It does not replace your DAM or MLR tool; it reads the approved core and connects it to the pre-MLR check and the post-launch AI answer measurement.
What is a claims library and why does it matter for AI?
A claims library is the set of approved claims modeled as first-class objects: each claim with its variations, the references that support it, and a ledger of every asset it is used in. It matters for AI because the same small approved core you recombine inside is the core the models learn to repeat outside. A claim that is well structured and widely reused is one an answer engine can surface accurately; an unmanaged claim estate produces drift.
What is reuse scoring?
Reuse scoring breaks a new asset into content blocks and matches each one back to the approved modules and claims it reuses, tagging every block exact-match, light-edit or new. A high reuse score means most of an asset carries no new claim, so only the genuinely new copy needs fresh MLR review. It turns the approved-content estate from a folder of files into a measurable, recombinable system.
What is AI Pickup?
AI Pickup is the share of your approved content that the AI engines actually echo back to HCPs. It splits your approved modules and claims into a surfacing set (content the models repeat) and an invisible set (approved content no model is using), per engine. It turns approved content from a cost into a measurable asset: the modules the machines repeat are the ones earning their MLR effort.
How does Content Intelligence connect to Pre-check and Answer Monitor?
They share one approved core. Content Intelligence holds the modular and claims library; Pre-check clears a new asset against it before MLR and scores reuse; Answer Monitor measures how much of it the engines repeat, and whether accurately, after launch. Because all three read the same approved label and claim set, a gap or a drift on the outside traces back to a specific approved clause on the inside.
Does Content Intelligence support 21 CFR Part 11?
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 across the content estate. You validate it for Part 11 use under your own SOPs. The claims and modules carry their approval provenance, so a reused block travels with the record of the clause it was cleared against.

See it on your brand

Map your approved content.

Bring one brand and its approved claim set. We will model the claims library, score reuse on a recent asset, and show you which approved content the AI engines are already repeating, and which is invisible.

Book a demoSee Answer Monitor