Campaign Evidence Object Schema for AI Creator Marketing

Why generic AI content workflows break in creator marketing

Most AI content stacks were built for marketers who own the entire content surface — blog posts, ad copy, product descriptions, internal documents. Creator campaigns are structurally different. The brand does not own the voice, the asset, the audience, or the rights. Five failure modes show up the moment a generic stack touches a real campaign.

• Unsupported claims — AI confidently echoes a phrase from a creator caption that was never approved as a brand claim. The line ships in outreach, a brief, an ad variant, and a board report before anyone notices.
• Missing usage rights — A great clip gets recommended into a paid placement, a PDP, or an AI derivative when the contract only covered an organic repost. The asset is good. The placement is illegal.
• Stale product detail — Pricing, ingredients, SKUs, and claim language change quarterly. AI keeps drafting against last quarter's facts because nothing tells it the source has expired.
• Recommendations without metric ownership — AI suggests an angle, a creator, a budget reallocation. Nobody can answer which metric it is trying to move or how the result will be measured.
• Reports that cannot be defended — A summary cites a 2.4x lift. The underlying dataset, time window, and filter are gone. The number is a vibe, not a measurement.

The fix is not better prompts. The fix is requiring that every AI artifact carry the evidence behind it. If the evidence is missing, the artifact does not ship.

What the schema describes

A Campaign Evidence Object is a small, typed record attached to any AI-generated artifact: a brief line, an outreach message, a caption suggestion, an ad variant, a number in a report. The schema does not store the artifact. It explains why the artifact is allowed to exist, what it points to, who approved it, and how it will be evaluated.

The structural contract is intentionally narrow: an artifact without a valid evidence object is treated as a draft, not a publishable output. That single rule is what keeps AI from outrunning the brand’s ability to defend its outputs.

Think of it the way a finance team treats a journal entry. Every line carries its source document, its account, its currency, its date, its approver, and its audit trail. Without those, the entry is not postable. Creator campaigns now operate at a surface area too wide for humans to hand-check every line; they need the same discipline.

The ten required fields

A minimum-viable Campaign Evidence Object carries ten fields. Each one closes a specific failure mode.

1. Source class

What kind of source backs this artifact. Common classes include approved product claim, lab or clinical document, brand-safe phrasing library, creator post, creator contract, customer review, support ticket, return reason, performance dataset, prior campaign report, internal SME quote, public press, and regulatory filing. The source class is what selects the review path: a regulated cosmetic claim takes a different lane than a sentiment quote.

2. Source URL or media ID

The exact pointer. A claim document with a version number. A creator post URL with platform and post ID. An internal asset ID with a revision. A query, dataset, and time window for performance numbers. If a reviewer cannot click through to the source in one step, the evidence is too weak.

3. Campaign, creator, and asset IDs

Every artifact is tied to the campaign it belongs to, the creator (if any), and any asset it depends on. These identifiers are what make it possible to expire evidence campaign-wide, creator-wide, or asset-wide when conditions change — without scanning every downstream artifact by hand.

4. Approval status

One of: draft, pending review, approved, conditionally approved, revoked. The status is a state, not a flag. A revoked source revokes every downstream artifact that depended on it. That cascade is what makes the schema useful when a claim or rights window changes mid-flight.

5. Rights scope

What the brand can do with the underlying source. Typical dimensions: channel (organic, paid, owned site, retail media, email), geography, language, duration, derivative or AI-assisted use, exclusivity, and talent approval requirements. Rights scope is not a free-text note — it is structured, so AI cannot recommend a placement outside the scope. See influencer usage rights pricing for how that scope is negotiated upstream.

6. Expiry or staleness rule

Evidence rots. Expiry can be a calendar date, a relative window (90 days), a version pointer (valid only for claim library v4), or a conditional rule (expires when SKU is reformulated). The system must be able to ask, at any moment, whether the evidence is still live and surface anything that has gone stale.

7. Citation pointer

The exact location inside the source. A claim ID in the approved library. A timecode in a creator video. A row ID in a performance dataset. The pointer must be machine-resolvable, not a human-friendly summary — that is what lets review tooling jump from any artifact to the underlying evidence in one click.

8. Confidence

A small, bounded score: high, medium, low, or speculative. AI sets an initial score; reviewers can override. High-confidence evidence routes to fast approval. Speculative evidence is never publishable without a human upgrade. Confidence is not a vibe rating — it controls routing.

9. Owner

The person or role accountable for keeping the evidence valid. Owners receive expiry alerts and revocation prompts. Without an owner, evidence drifts until someone gets paged by a regulator, a creator, or a customer.

10. Expected metric

Every recommendation declares the metric it is expected to move and the direction of the bet. This is what turns an AI suggestion into a measurable hypothesis. See influencer marketing ROI measurement for how expected metrics ladder into a campaign-level measurement plan.

How the schema flows across campaign surfaces

The same schema flows across five surfaces of a creator campaign. The artifacts differ; the evidence layer is identical. This is what makes the schema worth standardizing rather than rebuilding per tool.

Creator briefs

Each brief line — required shot, claim language, disclosure requirement, format spec — points to its evidence. A required claim points to the approved claim library entry. A required objection points to the shopper-voice dataset that surfaced it. A forbidden phrase points to the brand-safe rules. See the AI influencer brief generator workflow for how briefs absorb evidence objects in practice.

Outreach

Every outreach line is checked against its evidence object before send. Generic flattery is permitted; specific claims require provenance. A line like “our shoppers love your shade-match content” must point to the review pull that established it. See AI outreach preflight simulation for the pre-send validation step.

Content reviews

When a draft creator post or AI-derived cutdown enters review, every claim, statistic, demo, and visual is checked against its evidence object. Approvals are granted at the line level, not the asset level. A strong asset with one revoked claim becomes a conditional approval with a required edit — not a hard rejection of the whole asset.

Paid reuse and AI derivatives

Before any creator asset is recommended into paid social, retail media, a PDP, an email, or an AI-derived variant, the rights scope on its evidence object is checked. If the scope does not cover the placement, the asset is filtered out automatically. AI never has to guess whether a placement is safe.

Reports

Every number in a campaign report carries the evidence object behind it: dataset, time window, filter, and calculation pointer. A reader can click through from any KPI to its underlying source. Reports stop being summaries and start being audits — defensible six months later when the same campaign is being reviewed for renewal.

Approval gates: observe, propose, draft, approve, execute

The schema is most useful when it is plugged into a five-stage pipeline. AI advances artifacts through the stages; humans control the gates between them.

Observe

AI ingests signals: PDP content, claim library, reviews, creator assets, contracts, performance data. It emits candidate evidence objects without committing to any artifact yet.

Propose

AI clusters candidate evidence into recommendations — brief themes, outreach openings, ad variant directions, report findings. Each recommendation carries the evidence objects that justify it. Nothing advances without provenance.

Draft

AI turns approved recommendations into draft artifacts. Drafts inherit the evidence objects; any new claim or asset reference must attach its own evidence before the draft is reviewable.

Approve

Reviewers approve at the evidence-object level, not the artifact level. A draft brief with twenty lines may pass eighteen and require edits on two; the system tracks state per line and reassembles the final artifact when all lines clear.

Execute

Only artifacts with fully approved, in-date evidence objects are allowed to send, publish, or be passed to a creator. The system fails closed: a missing or revoked evidence object blocks execution rather than letting a weak artifact slip through. See influencer content approval workflow for how this maps onto a brand-side review loop.

Measurement loop: tie every recommendation to a metric

Evidence objects also make measurement honest. Because every recommendation declares its expected metric and direction up front, the campaign is evaluated against the bets it actually made, not the story told in retrospect.

• Each approved recommendation is logged with its expected metric and direction.
• After execution, the actual metric movement is attached to the same evidence object.
• Win/loss results feed back into the claim library, creator selection rules, and brief templates.
• Recommendations with consistently low realization are downgraded in confidence.
• Recommendations with consistently high realization graduate into reusable plays.

That feedback loop is what turns a creator program into a learning system instead of a content factory. See creator campaign memory for the long-term memory layer that compounds the results across campaigns.

Reference schema

A minimal evidence object can be expressed as JSON. Treat this as a starting shape, not a fixed contract — the value sets behind each field are meant to evolve with the brand.

{
  "evidence_id": "ev_2026_05_21_00187",
  "artifact_ref": {
    "type": "outreach_line",
    "id": "out_q2_skincare_demo_l03"
  },
  "source_class": "approved_product_claim",
  "source_ref": {
    "system": "claim_library",
    "id": "clm_barrier_repair_v3",
    "pointer": "section_2.4",
    "version": "2026-Q2"
  },
  "campaign_id": "cmp_q2_skincare_demo",
  "creator_id": "crt_jihye_kim",
  "asset_id": null,
  "approval_status": "approved",
  "approved_by": "brand_review_lead",
  "approved_at": "2026-05-19T10:14:00Z",
  "rights_scope": {
    "channels": ["organic", "paid_social"],
    "geo": ["US", "CA"],
    "languages": ["en"],
    "duration_days": 180,
    "ai_derivatives": true,
    "exclusivity": false,
    "talent_approval_required": false
  },
  "expiry": {
    "type": "version",
    "valid_for_versions": ["2026-Q2"]
  },
  "confidence": "high",
  "owner": "brand_ops_lead",
  "expected_metric": {
    "kpi": "outreach_response_rate",
    "direction": "increase",
    "magnitude_hint": "small_to_medium"
  }
}

Most teams will not expose this JSON directly. It lives behind the approval UI, the brief editor, the outreach composer, and the reporting layer. The operator sees structured fields and review states; the schema does the connective work underneath.

Where Storika fits

Storika is built as a campaign operator and evidence layer for AI-assisted creator marketing. The platform attaches Campaign Evidence Objects across the entire campaign surface: discovery, outreach, briefs, content review, paid reuse, and reporting. AI accelerates the work; the evidence layer keeps it accurate, rights-cleared, and measurable.

That is how the same workflow that lets a small team ship ten campaigns a quarter also stands up under board, legal, and platform scrutiny. The evidence is already in the record — not reconstructed after the fact.

Mistakes to avoid when adopting the schema

Mistake 1: Free-text provenance

A note in a doc saying “sourced from the creator’s caption” is not evidence. It is a story. Source references must be structured and machine-resolvable; otherwise the schema becomes a form-filling exercise instead of an enforcement layer.

Mistake 2: Approving artifacts instead of evidence

Approving a whole brief or a whole ad makes it impossible to revoke a single claim later. Approve at the evidence-object level so that revocation is surgical.

Mistake 3: Skipping expiry

Evidence without an expiry rule is evidence that will eventually be wrong. Every object must answer how long it is valid for, and under what conditions it expires.

Mistake 4: One evidence object per asset

Real artifacts have many claims, many sources, and many rights conditions. One evidence object per artifact hides the structure that makes approvals and revocations useful. Granularity is the point.

Mistake 5: No expected metric

A recommendation without a metric is a vibe. Recommendations without expected metrics should not be allowed past the propose stage; otherwise the learning loop has nothing to grade against.

FAQ

The schema that keeps AI creator marketing defensible