automation

From Upfronts to Remnants: How to Curate CTV Scatter into High‑Fidelity Programmatic Deals

A practical blueprint for SSPs and publishers to transform CTV scatter into premium programmatic inventory using curation, standards, and privacy‑first data.

From Upfronts to Remnants: How to Curate CTV Scatter into High‑Fidelity Programmatic Deals

Introduction

Upfronts still set the tone for premium TV budgets, but scatter is where strategy gets stress‑tested. Streaming has expanded premium content supply while fragmenting access, pricing, and quality signals. Buyers want flexibility, transparency, and data‑driven control. Sellers want predictability, fair pricing, and low operational drag. This is where curation wins. Curating CTV scatter into high‑fidelity programmatic deals lets supply teams assemble quality, context, and outcomes into packages that feel like TV, clear like programmatic, and ship with the trust buyers require. For SSPs and publisher networks, this is not just yield optimization. It is how you turn remnants into repeatable premium products that attract sustained spend. In this thought piece, we outline a sell‑side blueprint that connects market reality, IAB Tech Lab standards, and pragmatic data engineering. We will break down the components of high‑fidelity CTV curation, show example configs and schemas, and map the go‑to‑market motions that convert curated supply into booked deals. Streaming’s share of upfront dollars keeps rising, and programmatic guaranteed is increasingly central to negotiations, which heightens the importance of confident, packaging‑ready supply signals for CTV buyers :cite[fnk,eks]. At the same time, the open programmatic pipe is congested and selective, which pushes spend toward curated PMPs, PG, and seller‑driven marketplaces :cite[ch7,d9o]. In short, curated CTV is the new scatter, and the sellers who master the playbook will control the narrative and the margin :cite[enj].

The Market Backdrop: Why Curated CTV Deals Now

CTV is simultaneously premium and programmatic. Buyers want to apply audience science, control frequency, and prove incremental reach. Sellers want to protect brand, enforce pod integrity, and lock in CPMs that reflect real quality. The legacy linear contract model and pure open auction are both imperfect fits. A few realities are converging:

  • Streaming is absorbing a larger share of upfront budgets: Buyers follow audiences and content windows, and they want flexibility, which boosts programmatic constructs that preserve guarantees yet enable data‑driven execution :cite[fnk,eks].
  • Open programmatic CTV is constrained: Quality concerns, ambiguous inventory provenance, and IVT risks push budgets away from open exchange toward PMPs and PG where buyers gain transparency and predictable access :cite[d9o,ch7].
  • Sell‑side curation is maturing: SSPs and publisher coalitions are packaging inventory with layered data, attention, and brand safety to simplify buying and improve outcomes :cite[c78,d3g].
  • Standards favor high‑fidelity signals: OpenRTB 2.6 podding and content objects, sellers.json and SupplyChain, and SDA or curated audiences make quality more observable and enforceable :cite[c7c,ajm].

These shifts do not eliminate scatter. They make scatter a proving ground for curated, high‑signal supply that can be sold programmatically and renewed like TV.

What “High‑Fidelity” Means in Programmatic CTV

In programmatic, fidelity is the degree to which signals match the buyer’s decision needs and the realities of delivery. For curated CTV, high‑fidelity means:

  • Provenance: Clear authorized seller paths via app‑ads.txt or ads.txt, sellers.json, and SupplyChain objects, with daily validation to prevent spoofing or unauthorized resellers :cite[acc,dk9,ajm].
  • Context: Consistent content and distribution signals in OpenRTB 2.6, including channel, genre, network, content rating, and extended content identifiers when available :cite[c7c,ad7,cva].
  • Pod integrity: Accurate ad pod configuration, competitive separation, and duration constraints represented in bid requests and enforced in SSAI or player logic :cite[c7c,as7].
  • Privacy‑first audience options: Seller defined or curated audiences, PAIR‑based first‑party data matching, and regional privacy signaling via GPP :cite[csf,c5u,cte,dse].
  • Quality controls: Systematic SIVT protections tuned for CTV, including SSAI spoofing defense and device validation, with independent verification :cite[fkz,flz,dsw].
  • Commercial clarity: Deal type, priority, and rate cards that map to buyer needs, from PG to curated PMPs with defined flight windows and flexible add‑ons.

High‑fidelity does not mean maximal data. It means the minimal trusted set of signals that drive confidence and performance, delivered consistently and at scale.

A Five‑Layer CTV Curation Framework

Red Volcano’s focus is sell‑side intelligence. The framework below is written from an SSP and publisher network perspective, but is equally useful for large publishers with multi‑app portfolios.

Layer 1 — Provenance & Eligibility

Start with hygiene. If buyers cannot trust the path and the bundle, nothing else matters.

  • App‑ads.txt and ads.txt alignment: Validate that all authorized sellers are listed and that files are reachable and fresh. Verify DIRECT vs RESELLER accuracy :cite[acc,dk9].
  • Sellers.json & SupplyChain object: Ensure your sellers.json discloses intermediaries and that OpenRTB bid requests carry a well‑formed schain reflecting the real path :cite[ajm,b2w].
  • Bundle and channel controls: Lock delivery to intended app bundles and distribution channels, including FAST channels and OEM IDs, to avoid mis‑attribution.
  • SSAI transparency: Where SSAI is used, pass identifiers and rendering context consistently, and adopt verification tags designed for SSAI environments.

Example app‑ads.txt entries for a CTV app:

# app-ads.txt for tv.example.app
sspexample.com, 1234, DIRECT, a9f1bfa5e2d1
sspexample.com, 9876, RESELLER, a9f1bfa5e2d1
oemexchange.tv, 2211, DIRECT, 6a698e2ec386

Minimal sellers.json snippet for an SSP:

{
"seller_id": "1234",
"seller_type": "PUBLISHER",
"name": "Example TV Media",
"domain": "exampletvmedia.com",
"is_confidential": 0
}

Curated deals should only include supply that passes this baseline. Red Volcano’s ads.txt and sellers.json monitoring workflows are designed to enforce this layer continuously.

Layer 2 — Content & Context Enrichment

CTV buyers care deeply about where their ads run. OpenRTB 2.6 enables richer video placement and content descriptors. Use them.

  • Placement and pod fields: Populate video.placement, pod, and adslot fields per 2.6 guidance to express long‑form, pod position, and max durations :cite[c7c,cva].
  • Extended Content Identifiers: When available, pass channel, network, show, and EID values for granular context, even when episodic content is aggregated :cite[ad7].
  • Content rating and genre: Consistent content.context, rating, and cat arrays give buyers brand suitability confidence.
  • FAST and live: Distinguish live linear FAST channels from VOD to set expectations for pacing and frequency.

OpenRTB 2.6 bid request snippet with pod and extended content:

{
"id": "req-789",
"source": {
"ext": {
"schain": {
"complete": 1,
"nodes": [
{"asi":"sspexample.com","sid":"1234","hp":1}
],
"ver":"1.0"
}
}
},
"app": {
"bundle": "tv.example.app",
"name": "Example TV",
"ext": {
"channel": "ExampleTV",
"network": "Example Media Network"
}
},
"device": {"ifa":"RANDOM-IFA-REDUCTED","ip":"198.51.100.10"},
"user": {"ext":{"consent":{"gpp":"...","gpp_sid":[7,9]}}},
"imp": [{
"id": "1",
"video": {
"mimes": ["video/mp4","video/x-mpegURL"],
"minduration": 5,
"maxduration": 30,
"w": 1920, "h": 1080,
"placement": 1,
"podid": 101,
"podseq": 2,
"rqddurs": [15,30],
"maxseq": 6
},
"pmp": {"private_auction": 1, "deals": [{"id":"DEAL-PREMIUM-CTVSERIES","bidfloor":35.00}]},
"ext": {
"adpod": {"minduration": 5, "maxduration": 30, "minads": 4, "maxads": 6},
"content": {
"genre": ["Drama","Thriller"],
"network": "Example Media Network",
"channel": "ExampleTV",
"episode": "S03E04",
"contentrating": "TV-14"
}
}
}]
}

The goal is not to dump every field. It is to consistently pass the fields buyers rely on to decide and verify.

Layer 3 — Pod Architecture & Delivery Discipline

Buyers will pay premiums for pod integrity. Make it a product feature, not a promise.

  • Pod policy: Define acceptable durations, positions, and separation rules at the deal level. Enforce in the ad server or SSAI with auditable logs.
  • OpenRTB 2.6 podding: Use pod and adslot constructs to represent constraints that the DSP can plan against :cite[c7c].
  • Prebid and ad server configuration: For header bidding video or SSAI workflows, align Prebid video modules and ad server line items with pod policies :cite[aww,as7].
  • Competitive separation: Maintain category and brand separation rules, especially in long‑form and live pods.

Prebid ad pod configuration example:

pbjs.setConfig({
s2sConfig: { enabled: true, bidders: ['bidderA','bidderB'] },
video: {
adPodDurationSec: 120,
adPodMaxAds: 6,
brandCategoryExclusion: true,
cache: { url: 'https://prebid.adnxs.com/pbc/v1/cache' }
}
});
// Define ad units for long-form video instream
var adUnits = [{
code: 'video1',
mediaTypes: {
video: {
context: 'instream',
playerSize: [1920,1080],
mimes: ['video/mp4'],
minduration: 5,
maxduration: 30,
protocols: [2,3,5,6],
placement: 1
}
},
bids: [
{ bidder: 'bidderA', params: { placementId: 123 } },
{ bidder: 'bidderB', params: { siteId: 456 } }
]
}];
pbjs.addAdUnits(adUnits);
pbjs.requestBids({
bidsBackHandler: function() {
// send to ad server once pods are assembled
}
});

The pod policy belongs in the product sheet for every curated deal. Treat it like a service level, with monitoring and reporting to prove compliance.

Layer 4 — Privacy‑First Audience Options

Buyers want audience precision, but privacy laws and platform changes require fresh patterns. Give buyers options that do not put your ecosystem at risk.

  • Curated or seller‑defined audiences: Package cohorts based on content consumption, contextual signals, and first‑party attributes using SDA or its curated successor to standardize attributes in the bidstream :cite[csf,c5u].
  • PAIR: Offer PAIR‑enabled activation so advertisers can match their first‑party data with your first‑party data without exposing user‑level identifiers, with growing industry support :cite[cte,a2e].
  • GPP signaling: Consistently indicate regional privacy strings and consent states for CCPA, GDPR, and other jurisdictions in user.ext :cite[dse].
  • Clean room and crosswalk integrations: For advanced buyers, support clean room deals or OEM data overlays through neutral partners, gated behind contracts and governance.

Compact SDA taxonomy example inside an OpenRTB extension:

{
"user": {
"ext": {
"sda": {
"tax": 600,
"segtax": 601,
"segtaxv": "1.0",
"segs": [
{"id": "ctv_binge_watchers", "name": "Binge Watchers", "source": "publisher"},
{"id": "sports_fans_live", "name": "Live Sports Fans", "source": "oem_partner"}
]
}
}
}
}

Keep audience value transparent. Buyers should know exactly what problem the cohort solves without needing to reverse engineer your graph.

Layer 5 — Quality Assurance & Verification

CTV fraud is different from display fraud. SSAI spoofing, device farms, and bundle impersonation are real. Assume buyers will ask for your playbook.

  • SIVT controls: Partner with verification vendors that can validate SSAI contexts and detect device spoofing at CTV scale. Share aggregate outcomes by deal :cite[fkz,flz].
  • App store integrity: Track app store delistings, category changes, and suspicious clones, especially for OEM ecosystems :cite[dsw].
  • Brand suitability: Apply deal‑level brand safety filters consistent with TV expectations and content ratings.
  • Transparency reporting: Provide per‑deal transparency summaries that include schain, top bundles, channels, and measured IVT rates.

Quality assurance is not a gate. It is a continuous process that earns renewals.

Deal Constructs That Convert Scatter to Spend

Curated CTV programs should meet buyers where they are in their risk and control preferences. Three constructs cover most needs:

  • Programmatic Guaranteed: Fixed CPM, impression guarantee, programmatic delivery. Ideal for tentpole windows, seasonal moments, or commitment‑driven categories. PG has become a focal point in TV negotiations because it balances control and automation :cite[eks,ek1].
  • Curated PMP: Private auction with curated supply and controls. Ideal for flexible budgets and test‑and‑learn phases. Many buyers are shifting to PMPs and PG for CTV due to transparency and control benefits :cite[d9o,asi].
  • Biddable Curated Deals: Persistent curated pipes that buyers can bid into at their own pace. These resemble a modern scatter lane for programmatic, which is gaining traction in CTV :cite[enj].

A healthy portfolio offers all three, with clear upgrade paths from biddable to PMP to PG as buyers gain confidence.

Packaging and Naming: Make Quality Legible

Names should convey value at a glance. Pair them with one‑page specifications that answer 90 percent of buyer questions.

  • By content: “Prime Series & Awards Season Long‑Form,” “Premium FAST News Prime,” “Sunday Sports Shoulder Programming.”
  • By audience: “Household Decision Makers, CTV,” “In‑Market Auto, Premium Sports VOD,” “Parents with Teens, ACR‑Enriched.”
  • By outcome: “High Attention CTV, 30s pods,” “Low Frequency Reach Builder,” “New‑to‑Brand Video Completion Booster.”

Each package should disclose:

  • Inventory scope: App bundles, channels, long‑form vs live, geo, and language.
  • Pod policy: Durations, positions, and competitive separation.
  • Identity & privacy: SDA or curated audience availability, PAIR readiness, and GPP coverage.
  • Verification: Measurement partners, brand safety parameters, and SIVT approach.
  • Commercials: Deal type, CPM range or fixed rate, flight windows, and cancellation terms.

An Operational Blueprint: From Inventory to Curated Deals

The workflow below is designed for SSPs and publishers running a curated marketplace. It assumes your data platform can ingest ads.txt, sellers.json, app metadata, ad logs, and verification signals daily. 1) Ingest and normalize:

  • Collect: App bundles, OEM channel IDs, ads.txt and app‑ads.txt, sellers.json, OpenRTB logs, VAST beacons, verification summaries.
  • Normalize: Map apps to publishers and networks, resolve reseller paths, and standardize genres and content ratings.
  • Validate: Daily checks for ads.txt and sellers.json parity and schain completeness.

2) Enrich and score:

  • Context: Apply extended content identifiers, network mapping, live vs VOD classification.
  • Quality: Compute IVT risk scores, attention proxies, viewability likelihood, and completion rates by pod slot.
  • Supply stability: Flag delivery volatility risk and fill patterns to inform PG confidence.

3) Assemble packages:

  • Define templates: Content‑first, audience‑first, and outcome‑first templates with default pod policies.
  • Apply filters: Eligibility constraints, genre, rating, region, language, and brand suitability.
  • Price: Dynamic floors for PMPs, fixed CPMs for PG, with tiered add‑ons for audience overlays and attention signals.

4) Activate and measure:

  • Create deals: Generate deal IDs with descriptive metadata and share buyer‑friendly one‑sheets.
  • Instrument: Attach verification macros and pod integrity monitors. Ensure GPP strings and consent signals are flowing.
  • Report: Weekly per‑deal transparency summaries and optimization recommendations.

Data Engineering Patterns That Keep You Fast

Curation is a data product. A few practical patterns help:

  • Separation of concerns: Maintain distinct layers for raw ingestion, normalized entities, quality metrics, and curated deal views.
  • Idempotent daily builds: Rebuild curated sets daily so ads.txt or sellers.json changes reflect quickly, with diffs logged.
  • Schema contracts: Treat OpenRTB 2.6, sellers.json, and SDA payloads as contracts. Validate and quarantine variances automatically.

Example table design and SQL for a curated FAST News package:

-- Entity tables
CREATE TABLE app_dim (
app_id STRING PRIMARY KEY,
bundle STRING,
name STRING,
publisher_id STRING,
network STRING,
is_fast BOOL,
genre STRING,
rating STRING
);
CREATE TABLE supply_path (
bundle STRING,
seller_id STRING,
relationship STRING, -- DIRECT or RESELLER
exchange STRING,
last_seen_date DATE
);
CREATE TABLE quality_metrics_daily (
bundle STRING,
date DATE,
ivt_rate FLOAT,
vcr_30 FLOAT,
attention_index FLOAT,
pod_integrity_score FLOAT
);
-- Build curated view
CREATE VIEW curated_fast_news AS
SELECT
a.bundle,
a.name,
a.network,
q.ivt_rate,
q.vcr_30,
q.attention_index,
q.pod_integrity_score
FROM app_dim a
JOIN (
SELECT bundle, MAX(date) AS date
FROM quality_metrics_daily
GROUP BY bundle
) last ON a.bundle = last.bundle
JOIN quality_metrics_daily q
ON q.bundle = last.bundle AND q.date = last.date
WHERE a.is_fast = TRUE
AND LOWER(a.genre) LIKE '%news%'
AND q.ivt_rate < 0.02
AND q.pod_integrity_score >= 0.9
AND EXISTS (
SELECT 1 FROM supply_path sp
WHERE sp.bundle = a.bundle
AND sp.relationship = 'DIRECT'
AND sp.last_seen_date >= CURRENT_DATE() - INTERVAL '3' DAY
);

Expose curated views as materialized datasets that your deal creation service reads from when generating deal IDs and buyer documentation.

Pricing and Commercial Design

Price is a function of scarcity, signal quality, and outcome confidence. For CTV curated deals, a pragmatic model looks like:

  • Base CPM: Reflects content quality and pod policy. Premium long‑form, prime evening, and strict separation command a higher base.
  • Add‑ons: +X for SDA or curated audiences, +Y for PAIR activation, +Z for additional brand suitability or stricter frequency caps.
  • Tiering: Silver for flexible PMP, Gold for curated PMP with stronger guarantees, Platinum for PG with locked pricing and pod SLAs.
  • Outcome credits: Offer make‑good logic or outcome credits for under‑delivery on agreed completion rate or pod integrity thresholds.

Record these decisions. The commercial logic is part of what buyers evaluate, and consistency builds trust.

Measurement, Verification, and the SSAI Question

Measurement in CTV requires alignment across SSAI, player telemetry, and verification partners.

  • SSAI tag consistency: Pass macros and identifiers reliably. Avoid swapping macros per vendor unless contractually required.
  • Pod beaconing: Emit pod‑level events with position and duration so you can prove separation and completion against the agreed policy.
  • Independent IVT coverage: Share partner attestations and aggregate outcomes by deal. Buyers want to see improvement over time :cite[fkz,flz].

Most of this is unglamorous instrumentation. It pays off when buyers ask hard questions halfway through the quarter.

Identity, Privacy, and Data Collaboration

CTV is first‑party rich and cookie poor, which makes privacy‑preserving collaboration central to curation.

  • PAIR: Position PAIR deals as a standard way to scale first‑party match without hand‑offs of raw identifiers. Many large platforms are leaning into this pattern :cite[cte,a2e].
  • GPP: Adopt GPP throughout your stack so buyers can demonstrate compliance and you can adjust per region :cite[dse].
  • Curated cohorts: Keep cohorts stable over the flight, document sources and refresh cadence, and avoid overly narrow segment definitions that risk leakage or low scale.

The winning posture is privacy first and option rich. Offer standard curated cohorts out of the box, and invite deeper collaboration through clean rooms for strategic accounts.

Go‑to‑Market: How to Sell Curated CTV

Curated deals sell themselves when the packaging is clear and the proof is simple.

  • One‑sheet per package: Inventory scope, pod policy, audience options, verification approach, and pricing tier on one page.
  • Side‑by‑side transparency: Show a before‑and‑after view of the open exchange vs curated deal on IVT, VCR, and completion rate.
  • Upgrade pathways: Offer a 30 day PMP trial that upgrades to PG with price lock if performance thresholds are hit.
  • Agency curation: Give agencies tools or managed services to assemble their own curated variants on top of your eligible pool :cite[c78,d3g].

Operationalize it with buyer‑ready documentation and APIs. Make re‑ordering easy.

How Red Volcano Can Help

Red Volcano specializes in sell‑side intelligence across web, app, and CTV. The following capabilities map directly to the curation framework above:

  • Publisher discovery and vetting via Magma Web to identify premium CTV apps, FAST channels, and network portfolios with verified provenance.
  • Technology stack and SSAI mapping to understand which players, SSAI vendors, and measurement tags are present by publisher.
  • ads.txt, app‑ads.txt, and sellers.json monitoring to enforce eligibility, detect unauthorized resellers, and prove SupplyChain integrity.
  • CTV data platform that unifies app metadata, delivery logs, and verification outcomes to score inventory quality and stability.
  • SDK intelligence to detect app integrations that affect measurement, consent, or playback behavior on CTV.
  • Sales outreach services to assemble and pitch curated packages with sourcing notes and operational readiness.

This is not a new product for Red Volcano, it is an orchestration of existing strengths applied to the CTV curation challenge.

A Practical Checklist: Are You Ready to Curate?

Use this quick pass before you go to market.

  • Eligibility: All participating apps have fresh app‑ads.txt entries and appear as DIRECT or approved RESELLER paths in sellers.json for your SSPs.
  • Signals: OpenRTB 2.6 fields for placement, pod, and content are consistently populated, with extended content identifiers where supported.
  • Pod SLAs: Pod policy is configured in ad serving and SSAI, with monitoring and audit logs.
  • Privacy: GPP signals are present, curated or SDA cohorts are documented, and PAIR activation is tested.
  • Quality: Verification partners are instrumented for SSAI and CTV, and deal‑level IVT benchmarks are ready to share.
  • Commercials: Pricing tiers, upgrade paths, and cancellation terms are standardized across packages.
  • Documentation: One‑sheets, deal IDs, and API endpoints are ready for buyers and trading teams.

Common Pitfalls and How to Avoid Them

Curated CTV deals can fail not because the idea is wrong, but because the execution is fuzzy.

  • Inconsistent schain: Mixed or missing SupplyChain objects sabotage trust. Automate validation and quarantine broken paths :cite[ajm].
  • Poor pod governance: If pod rules are not enforced in SSAI, buyers will notice. Treat pod integrity like uptime.
  • Audience opacity: If cohorts are not well defined, legal and brand teams will stall approvals. Standardize taxonomies and documentation :cite[csf,c5u].
  • Under‑resourced reporting: Without clean weekly transparency, buyers assume the worst. Automate dashboards per deal and make them shareable.
  • Open auction leakage: If the same supply leaks to the open market at lower prices, your curated premium erodes. Control paths and set consistent floors.

What Good Looks Like: A Week‑One Buyer Experience

Imagine an auto brand buys “Premium FAST News Prime, Curated PMP, US, 6‑week flight.” Week 1 playbook:

  • Day 1: Deal ID provisioned with one‑sheet. Buyer receives a cURL check to validate the deal, plus attention to frequency cap defaults.
  • Day 2: Test delivery with a 24 hour canary budget. Verification tags observe SSAI contexts. Any macro mismatches addressed immediately.
  • Day 3: Buyer dashboard live, showing top bundles, channels, schain disclosure, and early VCR signals compared to open exchange baseline.
  • Day 5: Optimization note issued, suggesting a shift in prime dayparts and a PAIR overlay for in‑market auto, with projected CPM and reach impact.

By the end of week one, the buyer feels like they are in a controlled TV environment with programmatic velocity. That is the standard you want to set.

Conclusion

CTV scatter is not the junk drawer of TV. It is the laboratory for premium programmatic. When sellers curate with provenance, pod integrity, and privacy‑first audience options, buyers move budgets out of open uncertainty into structured, high‑fidelity pipes. The programmatic constructs are ready, the standards are maturing, and the market is asking for this exact combination of flexibility and assurance. For SSPs and publishers, the opportunity is to turn curation into a product discipline. Package quality simply. Prove it weekly. Offer upgrade paths from curated PMP to PG. And use your data platform to make it repeatable. The sellers who do this will not just monetize scatter, they will redefine what premium means in programmatic TV. If your team is ready to build, Red Volcano can supply the discovery, hygiene, and data backbone that makes curated CTV work at scale. The rest is disciplined execution.

Appendix: Helpful Snippets

Deal metadata payload example for internal cataloging:

{
"deal_id": "CTV-FAST-NEWS-PRIME-US-GOLD-2025Q4",
"name": "Premium FAST News Prime, US, Gold",
"type": "PMP",
"region": ["US"],
"flight": {"start": "2025-10-01", "end": "2025-12-15"},
"inventory": {
"bundles": ["tv.example.app","tv.anothernews.app"],
"live": true,
"long_form": true
},
"pod_policy": {"min_ads": 4, "max_ads": 6, "durations": [15,30], "competitive_sep": true},
"audience": {"curated_cohorts": ["news_enthusiasts","affluent_households"], "pair_available": true},
"privacy": {"gpp_supported": ["US-STATE","EU-TCF"], "consent_required": true},
"verification": {"partners": ["dv","ias"], "ssai": "vendor-x"},
"pricing": {"base_cpm": 32.0, "tier": "Gold", "audience_addon": 3.0}
}

Simple health check response from a validation endpoint:

{
"deal_id": "CTV-FAST-NEWS-PRIME-US-GOLD-2025Q4",
"ads_txt_alignment": "PASS",
"sellers_json_alignment": "PASS",
"schain_completeness": 1.0,
"openrtb_26_fields": ["video.placement","imp.ext.adpod","app.ext.channel"],
"gpp_coverage": 0.98,
"ivt_last7d": 0.008,
"pod_integrity_last7d": 0.95
}

References

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