Trusted Match Protocol

AdCP’s real-time execution layer — follow a cross-publisher frequency capping story from wasted budget to structural privacy, across every surface.

Priya is Director of Ad Products at StreamHaus, a CTV publisher. She designed how StreamHaus’s inventory looks to buyer agents — product catalogs, creative specs, pricing.

Sam’s Acme Outdoor campaign runs on StreamHaus, OutdoorNet, and PodTrail with a frequency policy: 5 impressions per week, minimum 2 hours between exposures. Sam chose this because spaced exposure outperforms concentrated repetition — the same ad every commercial break produces fatigue, not engagement.

The problem: each publisher counts independently.

Step 1: The problem — wasted budget

StreamHaus, OutdoorNet, and PodTrail each count independently. A viewer who watches hiking content on StreamHaus after dinner, then browses OutdoorNet on their phone 30 minutes later, gets the same ad again — well inside the 2-hour recency window Sam set.

Multiply across a week and the viewer gets 15 impressions instead of 5, concentrated instead of spaced:

Publisher	Impressions counted	Actual viewer experience
StreamHaus	5 (within cap)	Same ad every session
OutdoorNet	5 (within cap)	Same ad every session
PodTrail	5 (within cap)	Same ad every session
Total	15	3x over Sam’s cap

Every redundant impression is budget that could have reached someone new. Advertising works better with spacing — each exposure after the first few produces diminishing returns. Sam is buying frequency when he should be buying reach.

This isn’t a StreamHaus problem. It’s a structural problem. No single publisher can enforce a cross-publisher cap because no single publisher sees the full picture.

Step 2: Adding the TMP Router

Priya deploys the Trusted Match Protocol (TMP) Router — the piece that sits between her ad server and buyer agents, with structurally separate paths for context and identity. She configures Sam’s buyer agent (Pinnacle) as a TMP provider, alongside other buyers.

The router sits between StreamHaus’s ad server and the buyer agents. When a user loads a page, the router handles the real-time evaluation. Priya didn’t write surface-specific code — the same router handles StreamHaus’s website, their mobile app, and their CTV app.

What Priya configures

Priya registers Sam’s buyer agent (Pinnacle) as a TMP provider on the router:

{
  "providers": [
    {
      "name": "Pinnacle (Acme Outdoor)",
      "endpoint": "https://pinnacle.acme.example/tmp",
      "context_match": true,
      "identity_match": true,
      "properties": ["01J5A2B3C4-streamhaus-web", "01J5A2B3C5-streamhaus-ios"],
      "latency_budget_ms": 50,
      "priority": 1
    }
  ]
}

context_match: true means the router sends content context to Pinnacle for targeting. identity_match: true means it also sends opaque user tokens so Pinnacle can enforce frequency caps and audience eligibility. properties scopes which StreamHaus properties this provider serves — Pinnacle evaluates web and iOS, not CTV. latency_budget_ms sets a per-provider timeout; if Pinnacle consistently exceeds it, the router deprioritizes it.

The router fans out to all configured providers in parallel and merges their responses.

Step 3: Context Match — what’s on the page?

A viewer opens a StreamHaus article about hiking gear. StreamHaus’s properties are registered in the property governance with stable property_rid identifiers, so the buyer knows exactly which property this request came from. StreamHaus sends a Context Match request with the article’s content signals, placement, and geo.

Sam’s buyer agent evaluates: “This hiking content matches pkg-outdoor-display.” It responds with an offer that includes a creative manifest — the Trail Pro 3000 banner.

The key constraint: no user identity crosses this boundary. The buyer evaluates content, not people. It doesn’t know who is reading the article — only what the article is about.

Context Match request and response

Request from StreamHaus to Sam’s buyer agent:

{
  "type": "context_match_request",
  "request_id": "ctx-8f3a2b",
  "property_rid": "01916f3a-9c4e-7000-8000-000000000010",
  "property_type": "website",
  "placement_id": "article-sidebar",
  "artifact_refs": [
    { "type": "url", "value": "https://streamhaus.example/articles/hiking-gear-2026" }
  ],
  "context_signals": {
    "topics": ["596"],
    "taxonomy_source": "iab",
    "taxonomy_id": 7,
    "keywords": ["hiking gear", "outdoor equipment"]
  }
}

The publisher sends both artifact_refs (for buyers that crawl content directly) and context_signals (pre-classified topics and keywords as a fallback). The buyer agent already knows which packages are active for this placement — it set them up via create_media_buy. No package list needs to travel on the wire.

Response from Sam’s buyer agent:

{
  "type": "context_match_response",
  "request_id": "ctx-8f3a2b",
  "offers": [
    {
      "package_id": "pkg-outdoor-display"
    }
  ]
}

Step 4: Identity Match — is this user eligible?

Separately, StreamHaus sends an Identity Match request: an opaque user token and ALL of Sam’s active package IDs across every publisher.

Sam’s buyer agent checks its exposure history: “This user saw 1 impression 45 minutes ago on OutdoorNet. The 2-hour recency window hasn’t elapsed. Not eligible.”

The key constraint: no page context crosses this boundary. The buyer checks eligibility, not content fit. It doesn’t know what the user is looking at — only whether this user should see more ads right now.

The recency check crosses publisher boundaries because Sam’s buyer agent maintains a shared exposure store. StreamHaus, OutdoorNet, and PodTrail all send Identity Match requests to the same buyer agent — so it knows the user’s total exposure across all three.

Identity Match request and response

Request from StreamHaus to Sam’s buyer agent:

{
  "type": "identity_match_request",
  "request_id": "id-7c9e1d",
  "user_token": "opaque-streamhaus-token-abc123",
  "uid_type": "uid2",
  "package_ids": [
    "pkg-outdoor-display",
    "pkg-outdoor-ctv",
    "pkg-outdoor-audio"
  ]
}

Response from Sam’s buyer agent:

{
  "type": "identity_match_response",
  "request_id": "id-7c9e1d",
  "eligible_package_ids": ["pkg-outdoor-audio"],
  "ttl_sec": 60
}

Only eligible packages are listed — pkg-outdoor-audio passes the buyer’s checks. The ttl_sec: 60 tells the router to cache this eligibility for 60 seconds.

The package_ids in the request includes ALL of Sam’s active packages — not just those relevant to the current page. Sending only the page-specific subset would let the buyer match package sets across Context Match and Identity Match, breaking the structural separation.

Step 5: The join — StreamHaus makes the decision

StreamHaus joins both responses locally:

• Context Match said: “Activate pkg-outdoor-display with this creative manifest.”
• Identity Match said: “Not eligible — recency window.”

Result: suppress the ad. A different advertiser’s campaign fills the slot. Sam’s budget is preserved for a better moment — one where the viewer hasn’t seen the ad recently and the impression will actually matter.

The buyer never saw user identity and page context together. Privacy isn’t a policy that could be violated — it’s structural. The two paths never share data, and the publisher (who already has both signals) makes the final decision.

Step 6: Three winners

The viewer had a normal evening across three platforms. They saw the Trail Pro ad during hiking content on StreamHaus — relevant, well-timed. When they browsed OutdoorNet 30 minutes later, a different ad appeared. No sense of being followed across the internet.

Sam got spaced exposure instead of concentrated repetition. His 5 weekly impressions land across different contexts and moments, each one more effective than the 6th or 7th impression would have been. And the budget freed by suppression reaches viewers who haven’t seen the ad yet — more unique reach for the same spend.

Priya differentiated StreamHaus. Buyers prefer publishers that support TMP because their frequency policies actually work. StreamHaus’s inventory is more valuable per impression because buyers know they’re not wasting budget on over-exposed viewers.

Before TMP	With TMP
Each publisher counts independently	Buyer agent tracks exposure across all publishers
15 impressions per viewer per week	5 impressions per viewer per week, properly spaced
Budget buys frequency	Budget buys reach
Concentrated repetition, ad fatigue	Spaced exposure, higher effectiveness per impression
Publishers compete on volume	Publishers compete on quality and buyer experience

Step 7: Same protocol, every surface

The same TMP Router handles StreamHaus’s website, their mobile app, their CTV app, and their AI assistant. Sam’s buyer agent works across all of them without surface-specific logic. The protocol handles the surface differences. Priya and Sam handle the business.

Go deeper

AI assistant mediation

A mediation protocol for AI assistants — how demand finds conversational AI when the context can’t be broadcast.

The execution gap

Why existing protocols fail at serve time and why TMP takes a matching approach instead of an auction approach.

Context and identity

Both operations with concrete examples, including catalog refinement and the publisher-side join.

Specification

Authoritative message types, field tables, and conformance requirements.

Privacy architecture

Structural separation, temporal decorrelation, and TEE attestation.

Router architecture

Deployment, fan-out, and provider configuration.

Surface guides

Web

AI assistants

Mobile

Retail media

CTV