Documentation Governance at Scale

Contract-driven governance system to ensure edition-specific metadata consistency across Real-Time CDP documentation at scale.

Business Problem

Real-Time Customer Data Platform documentation spans multiple editions: B2B, B2C, and B2P, each with different feature availability, packaging, and eligibility. At this scale, metadata accuracy is not cosmetic; it directly affects discoverability, trust, and downstream consumption by both humans and AI systems.

The documentation footprint included:

~90 pages scoped specifically to Real-Time CDP
~1,970 files across the broader repository

Within that environment, three failure modes were consistently observed:

Missing or incorrect edition badges, causing features to appear available where they were not
Inconsistent product metadata across conceptually similar pages
Review gaps driven by scale, where full verification of edition correctness was not feasible during normal review cycles

The impact was not limited to internal quality standards. Customers increasingly rely on Adobe AI Assistant to answer product questions, and that capability depends on accurate, consistent documentation metadata to return correct information. When metadata is wrong or incomplete, confidently incorrect answers reach customers and trust erodes.

This was not a tooling problem. It was a governance problem under scale.

Assumptions

Several assumptions shaped the approach from the outset:

Metadata correctness is a governance responsibility, not an authoring preference
Human review time is the most constrained and expensive resource
Automated systems must surface signal, not take action
Trust collapses quickly if automation produces noise, false positives, or unreviewed changes

These assumptions were reinforced by hard constraints:

No automated edits to documentation
No automatic Jira ticket creation
No low-confidence or speculative flags
No changes unless a defined metadata contract is violated

Organizationally, additional constraints applied:

There was no mandate to change existing authoring workflows
No additional reviewer headcount was available
No centralized authority existed to police metadata consistency

Any solution that required behavioral change, increased coordination overhead, or continuous manual triage would fail to scale and would not be adopted.

Decision

The system was designed to support a single governance decision at scale: where human review effort should be applied for edition correctness, and where it should not.

Which documents require human review for edition correctness right now—and which do not—without reviewing every page.

Before this, that decision was effectively unavailable. Edition-specific correctness could not be assessed reliably across the corpus without manual sampling and spot checks. Reviewers relied on partial coverage and time-boxed inspection, which made it difficult to reason about risk accumulation across the platform.

The risk of inaction was clear:

Incorrect edition attribution would propagate into customer-facing guidance
Customers would receive confidently wrong answers when relying on downstream systems
Trust in documentation—and by extension the platform—would degrade over time

The goal was not to fix metadata automatically. The goal was to direct reviewers to the smallest possible set of pages that demonstrably require attention, using high-confidence signals and remaining silent when no action is required.

Intervention

The intervention was a governance support system designed to evaluate metadata correctness at scale and surface review-worthy signals only. It does not modify documentation, create work items, or attempt to "fix" issues automatically.

The system operates against a defined metadata contract that specifies required edition badges and product metadata for Real-Time Customer Data Platform content. That contract serves as the single source of truth for evaluation.

Each documentation run is processed as follows:

Documentation content is scanned in bulk against the metadata contract
Edition-specific requirements are evaluated deterministically
Signals are produced only when a contract violation is detected
Results are filtered to exclude low-confidence or ambiguous cases
When no violation is found, the system remains silent

This design produces fewer, higher-confidence signals rather than exhaustive lists of potential issues.

The system explicitly avoids:

Automated edits to documentation
Automatic Jira ticket creation
Broad "linting" or advisory warnings
Any output that would require interpretation or follow-up investigation

Human reviewers remain the decision-makers. The system’s role is to narrow the review surface area so effort is spent where it is most needed.

System flow (single diagram)

    Documentation repository
              ↓
    Edition metadata contract
              ↓
    Deterministic bulk scan
              ↓
    Confidence-gated signals
              ↓
    Targeted human review

By separating detection from action, the system supports governance at scale without introducing workflow disruption or false confidence.

Result

The primary outcome of this work was not improved metadata in isolation, but the introduction of a scalable governance capability for edition-specific documentation.

The system enabled reviewers and platform leadership to:

Focus human review effort on a small, well-defined subset of documents that demonstrably violate edition requirements
Treat silence as a signal of confidence, rather than absence of coverage
Reason about metadata risk across the Real-Time CDP documentation set without exhaustive manual inspection
Detect consistency issues early, before they propagate into customer-facing guidance and downstream AI responses

Just as importantly, the system established clear operational boundaries:

Automated evaluation without automated action
High-confidence signals without background noise
Governance support without workflow disruption

This shifted metadata review from a reactive, sample-based activity to a repeatable, contract-driven process that can be rerun as documentation evolves, editions change, or new content is introduced.

The result is not faster documentation production, but more reliable decision-making at scale, with human judgment preserved and trust maintained.