By the time a reviewer opens the proof, the work has already started

Why it matters

Most review processes start blank. A reviewer opens a file, finds page one, and begins. They read until they find something that needs attention, then they comment. They keep reading. They finish. They make a decision.

This works for short, predictable files. It does not work at scale, and it does not work for content that mixes routine material with high-risk components. A reviewer reading a 300-page document linearly spends most of their time on content that does not need them. The components that do need them get less attention than they deserve, because attention is finite.

AI Pre-Analysis changes the starting state. Reviewers do not enter blank. They enter briefed. The components that need their attention are already flagged. The routine material is already cleared. The reviewer's time goes to judgment, which is the work only a human can do.

The math is straightforward. If pre-analysis surfaces the eight components in a 300-page document that warrant specialist attention, the reviewer reads those eight components carefully and verifies the rest at the level of "no flags, looks fine." That is not a small efficiency gain. That is a different kind of review.

The pre-analysis pattern

The pattern for this AI document intelligence is built from three pieces.

1. A trigger at the front of the workflow. The most common configuration is an "Execute AI prompt" Action bound to project creation or file upload. The moment the file is processed by Aproove, the pre-analysis Agents fire.

2. One or more Agents tuned to your review needs. Pre-analysis is not always a single Agent. Many customers configure a sequence of specialist Agents, each with its own prompt, model, and reference material. A regulatory Agent. A brand Agent. A spelling Agent. The full scan happens before any human is involved.

3. Findings written back to the file at the component level. Each Agent's Tags and Notes land on the specific paragraph, image, or section it analyzed. By the time the file enters the human review queue, the briefing is the file. There is no separate report to read.

The reviewer who picks up the file sees, at a glance:

• Which pages have flags and which are clean.
• Which components carry which kind of risk (regulatory, brand, claim, language).
• The specific issue each Agent identified.
• The Agent's suggested resolution.
• Their own next step.

What AI looks at

Pre-analysis can run at multiple levels of granularity, depending on what your team needs from AI document intelligence.

• File-level scans look at the whole document or asset and flag overall risk patterns. Useful for regulatory category checks and disclosure presence.
• Section-level scans run page-by-page or section-by-section, useful for catching localized issues in long-form content.
• Component-level scans drill to specific paragraphs, images, or regions, useful for brand checks, claim validation, and granular regulatory review.

A complete pre-analysis pass typically combines these. A regulatory Agent might run a file-level pass for required disclosures, a brand Agent might run a section-level pass for visual consistency, and a claim Agent might run a component-level pass on every marketing claim in the document.

Different Agents can use different AI models, optimized for what each does best. A complex regulatory reasoner can run on a frontier model. A spell-check Agent can run on a cost-efficient model. The mix is configurable.

What reviewers see when they open the file

The single biggest change pre-analysis makes is to the reviewer's first impression of the file.

Without pre-analysis: the reviewer opens a clean proof. They start at page one. They figure out what they are looking at. They read until they find something. They decide.

With pre-analysis: the reviewer opens a proof that already carries Tags and Notes. The flagged components are visible from the proof index. They navigate directly to the first flag. They read what the Agent found. They confirm or override. They move to the next flag. When they have addressed every flag, the file is ready for their decision.

For high-risk files, pre-analysis turns review into triage. For low-risk files, it turns review into verification. Either way, the reviewer's time goes to judgment, not to scanning.

Configuration patterns

Three common configurations:

The intake pre-screen. A single Agent runs on every file at upload, doing a broad first-pass risk scan. Cheap, fast, catches obvious issues. Files clean of flags move forward. Files with flags route to specialists. Good for high-volume, mixed-sensitivity programs.

The specialist battery. A configured sequence of Agents (regulatory, brand, claim, language) runs at intake. Each Agent has its own prompt, model, and reference material. Reviewers inherit a comprehensive briefing covering every dimension your review process cares about. Good for regulated industries where multiple specialist reviews are mandatory.

The conditional pre-analysis. Trigger-level metadata evaluation lets you configure pre-analysis to run only when conditions match (file type, project category, regulatory framework, or any other metadata field). A campaign for an OTC product might trigger one set of Agents. The same campaign for a regulated pharma product triggers a different set. Same workflow shell, different AI shape.

Benefits

• Reviewers start where it matters. Pre-analysis flags the components that need attention. Reviewers go there first, instead of reading everything in sequence.
• Specialist review time is protected. Your most expensive reviewers (legal, regulatory, senior brand) see only the components flagged for them, not every page of every file.
• Volume becomes manageable. Catalog-scale or campaign-scale review where humans alone cannot read every page. Agents fan across the assets in parallel, humans review the flagged components.
• The first human eyes are informed eyes. Reviewers enter with context, with Agent suggestions to consider, with the AI's first-pass already on the page.
• Multiple specialist Agents can run together. A pre-analysis battery can cover regulatory, brand, claim, and language dimensions in one pass.
• Conditional execution keeps it relevant. Metadata-driven triggers ensure pre-analysis runs when it is the right tool, not on every file regardless.‍
• The audit trail captures it all. Pre-analysis activity (Agent identity, findings, model, cost) is part of the project audit trail, alongside the human decisions that follow.

Who it's for

• Compliance and regulatory teams managing high-volume review pipelines where every file needs a baseline scan.
• Brand governance teams ensuring consistency across markets, channels, and partners.
• Operations leaders trying to scale review throughput without expanding specialist headcount.
• Marketing and creative teams who want to deliver pre-screened files to internal reviewers, reducing back-and-forth.
• Production teams running campaign-scale work where human-only review is impractical.

Under the hood

AI Pre-Analysis is implemented as one or more workflow Actions of type "Execute AI prompt," bound to triggers that fire at the front of a workflow (project creation, file upload, or initial workflow step transition). Each Action references an AI Prompt Template configured with an "Action" context, optionally with metadata-evaluated conditions that gate execution. Multiple Agents can run in sequence as a chain of Actions, or in parallel where the underlying AI provider supports parallel API calls. Agents work against the structured file representation produced by the Processing Agent at upload, accessing text content, metadata, image data, and pixel-level dimensions through system tags (currentProofsRawTextContent, currentProofsBigThumb, and others). Findings are written back to the proof as Notes (prefixed [AI GENERATED] and attributed to the workflow-associated user) and Tags (severity, category) at the component level. Generation Jobs records every Agent invocation including model, prompt, cost, and full API call. Pre-analysis activity is captured in the audit trail alongside subsequent human decisions, providing a complete chronological record of what AI saw and what humans decided.