automation

Answers as Inventory: How SSPs and Publishers Can Build a Programmatic Supply Strategy for AI Assistants

AI assistants are creating a new supply surface: answers. Here is how SSPs and publishers can define, package, price, and measure “answers as inventory.”

Answers as Inventory: How SSPs and Publishers Can Build a Programmatic Supply Strategy for AI Assistants

Answers as Inventory: How SSPs and Publishers Can Build a Programmatic Supply Strategy for AI Assistants

AI assistants are rapidly changing how people discover, decide, and buy. The traditional canvas of supply has been pages, screens, and streams. The new canvas is the answer itself. When someone asks an assistant for the best running shoes, a trip plan, or how to configure a CTV device, the response is not a list of links. It is an actionable synthesis that can include citations, cards, tool calls, and suggested next steps. That synthesis is a high‑intent moment with measurable influence. In short, it is inventory. This piece lays out a pragmatic framework for SSPs, publishers, and supply‑side data platforms to treat answers as programmatic supply. We will define the surfaces, propose a bid protocol extension, outline measurement, quality, and privacy baselines, and suggest how Red Volcano’s publisher intelligence can operationalize this for web, app, and CTV. Our aim is practical and near term. No speculative sci‑fi. Just smart ways the supply side can turn assistant interactions into transparent, compliant, and high‑value media.

What Do We Mean By “Answers as Inventory”?

An answer is the AI assistant’s structured response to a user intent. It can be a paragraph, a set of steps, a local map suggestion, a streaming recommendation, a shoppable card, or a tool invocation like “book a table.” The answer is not a banner slot or pre‑roll. It is a compositional surface where content and utilities are arranged to satisfy an intent. Three properties make answers a distinctive supply surface:

  • Intent density: An answer is proximal to a user’s goal, often closer to decision and action than a search results page.
  • Composability: The assistant can place citations, callouts, and tool cards inline, which creates new creative types.
  • Contextual explainability: The assistant can state why a suggestion appears and how it was chosen, which improves transparency.

For the supply side, answers introduce new definitions for inventory units, viewability, suitability, and outcomes. The SSP that helps publishers capture and monetize this surface simply wins.

The Supply‑Side Mental Model

Think of an assistant session as a stream of answer units. Each unit is a candidate monetizable surface with attributes like topic, sensitivity, position, and interaction depth.

  • Answer Unit (AU): The smallest monetizable element. Examples include a product card, a “Try this tool” button, or a citation card.
  • Answer Surface (AS): The full response composition in a turn. It contains multiple AUs with a layout and ranking.
  • Session Context (SC): The user’s ongoing conversation topic, history, device, and any first‑party authenticated state with consent.
  • Assistant Context (AC): Model metadata, safety regime, and RAG sources the assistant used to generate content.

In this model, the auction can occur for the entire surface or for specific units. The assistant is the renderer. The publisher may be the content origin and brand context. The SSP is the market‑maker that packages, prices, and enforces policies for these surfaces.

Why This Matters For SSPs and Publishers

The shift from links to answers compresses discovery and selection. That changes the flow of value. If assistants satisfy more intent inline, classic visit‑driven monetization declines unless publishers participate in the answer layer. Supply‑side opportunities:

  • New revenue surfaces: Sponsored callouts, tool‑call placements, and commerce cards can command outcome‑aligned pricing.
  • Premium context: Editorial authority and first‑party data can be propagated into the answer with provenance signals that command higher CPMs and CPCs.
  • CTV and voice: Assistants on the big screen create hands‑free, intent‑rich surfaces for recommendations and shoppable prompts.
  • Measurement with meaning: Assistants can explain why a suggestion appeared, enabling transparent attribution models that buyers prefer.

The risk is clear too. If publishers do not assert policies, metadata, and monetization rights into assistant ecosystems, assistant platforms may intermediate the value with minimal publisher compensation.

Red Volcano’s Angle

Red Volcano specializes in publisher and tech stack intelligence across web, mobile, and CTV. Our data can help SSPs and publishers:

  • Map assistant‑addressable supply: Identify sites, apps, and channels where high‑intent questions occur and where assistant embeddings or SDKs are detectable.
  • Track technology signals: From schema.org markup to in‑app SDKs and CTV voice integrations that enable answer surfaces.
  • Monitor authenticity: Use ads.txt and sellers.json consistency, plus SDK lineage, to validate assistant supply chains.
  • Build outreach: Engage publishers and app owners to implement assistant‑ready metadata and policies.

From Pages To Answers: An Inventory Taxonomy

Let’s classify the units of answer inventory suppliers can standardize.

Core Answer Units

  • Inline Recommendation Card: A product or content suggestion with title, image, price, and rationale snippet.
  • Citation Card: A link to a source used by the assistant, with publisher branding and trust badges.
  • Action Tool: A button that triggers a tool call like “Book,” “Compare prices,” or “Play trailer.”
  • Plan/Itinerary Block: Structured steps with ordered items that can hold sponsored insertions.
  • Local Result Card: Location‑aware suggestion enriched with map and reviews.
  • Clarification Prompt: A follow‑up question that can be sponsored to steer toward a category or brand.

Suitability Classes

  • General: Entertainment, sports, cooking, travel planning.
  • Sensitive: Health, finance, political content. Requires stricter policies and labeling.
  • Safety‑critical: Legal, medical, crisis. Likely non‑monetizable or strictly regulated.

Interaction Levels

  • Passive view: The unit appears in the answer without user interaction.
  • Hover/Expand: The user opens details.
  • Click‑through/Tool call: The user engages or executes an action.
  • Conversion: The action results in a measurable outcome on‑site or within the assistant.

This taxonomy enables packaging and pricing rules, plus the reporting schema buyers need.

A Practical Auction Design For Answers

There are two viable auction moments.

  • Pre‑generation auction: The assistant calls the SSP before composing the answer. The SSP returns candidate sponsored units with constraints and explanations the assistant may incorporate.
  • Post‑generation auction: The assistant composes the organic answer, then calls the SSP for eligible sponsored augmentations that are appended or interleaved.

Pre‑generation creates more integrated creative possibilities because sponsorship can influence which sources are consulted or which tools are invoked. It also raises higher integrity requirements, since the assistant must balance ad influence with factual fidelity. Post‑generation preserves editorial independence but tends to create more banner‑like placements. Both models can coexist for different inventory classes.

Suggested OpenRTB Extension: ans

OpenRTB already supports native and video formats, plus supply chain transparency via sellers.json, ads.txt, and the SupplyChain object. To support answers, the industry can define an extension object ans for both request and response. Below is a conceptual JSON schema snippet for a bid request using OpenRTB 2.6 style with an ans object. This is illustrative for product and engineering teams.

{
"id": "req-789",
"tmax": 250,
"site": {
"domain": "examplepublisher.com",
"page": "https://examplepublisher.com/recipes/healthy-bowls",
"cat": ["IAB7-5"]
},
"device": {
"ua": "Assistant/1.0",
"ifa": null,
"lmt": 1
},
"user": {
"consent": "CPXxXxXxXxXxXxXxAEN",
"eid": []
},
"source": {
"fd": 1,
"tid": "trans-123",
"ext": {
"schain": {
"ver": "1.0",
"complete": 1,
"nodes": [
{"asi": "publisher-ssp.com", "sid": "12345", "hp": 1}
]
},
"ans": {
"model": "rv-assistant-1.0",
"rag_sources": ["https://examplepublisher.com"],
"safety": ["brand_suitability_garm_v1"],
"latency_budget_ms": 120
}
}
},
"ext": {
"ans": {
"surface": {
"type": "answer_surface",
"topic": "meal_planning",
"intent": "find healthy lunch recipes",
"sensitivity": "general"
},
"units": [
{
"slotid": "au-1",
"pos": 1,
"allowed_formats": ["text_callout", "inline_card", "tool_call"],
"max_chars": 180,
"context": {
"entities": ["salad", "protein", "meal_prep"],
"geo": "US"
}
}
],
"explainability": {
"required": true,
"max_tokens": 24
},
"policy": {
"ads_must_be_labelled": true,
"no_health_claims": true
}
}
}
}

And a compatible bid response with an answer unit:

{
"id": "req-789",
"seatbid": [
{
"seat": "ssp-42",
"bid": [
{
"id": "b-888",
"impid": "au-1",
"price": 2.5,
"adm": {
"ans": {
"format": "inline_card",
"title": "High‑protein Mediterranean bowl",
"desc": "Ready in 15 minutes. 30g protein. Dietitian reviewed.",
"cta": "View recipe",
"icon": "https://cdn.brand.com/icon.png",
"url": "https://brand.com/recipes/med-bowl",
"explain": "Suggested due to your interest in healthy bowls and prep speed.",
"disclosure": "Sponsored"
}
},
"adomain": ["brand.com"],
"cat": ["IAB7-5"],
"ext": {
"ans": {
"policy_tags": ["garm-safe"],
"outcome_pricing": {"model": "cpe", "value": 0.30},
"measurement": {"viewability": "position_weighted", "position": 1}
}
}
}
]
}
],
"cur": "USD"
}

Key principles embedded here:

  • Explainability: The answer unit carries a concise rationale.
  • Policy adherence: Suitability flags travel with the creative.
  • Outcome pricing: Support for CPC, CPE, or CPA in addition to CPM.
  • RAG transparency: The assistant can disclose which sources were consulted.

References for the standards mentioned: IAB Tech Lab OpenRTB, ads.txt, sellers.json, and SupplyChain Object are the relevant foundations IAB Tech Lab Standards Overview and ads.txt, sellers.json.

Creative Formats That Fit The Answer Surface

Creative must be native to the assistant canvas. Here are four formats that can be standardized and transacted programmatically.

  • Text Callout: One‑line recommendation with disclosure and rationale. Good for low‑latency contexts like voice assistants.
  • Inline Card: Rich card with title, image, rating, and price. Works for shopping and streaming recommendations.
  • Tool Call: Executable action that initiates a flow such as compare prices, book, or add to watchlist.
  • Citation Sponsorship: Brand pays to be the featured source card when the assistant cites it, without altering the organic content.

Every format should carry:

  • Clear labeling as Sponsored or Promoted.
  • Compact explanation that the assistant can read aloud or display.
  • Policy and suitability metadata aligned with buyers’ brand safety frameworks. See the GARM Brand Suitability framework for categories and risk tiers.

Reference: Global Alliance for Responsible Media brand suitability framework is widely used by buyers for categorization and risk management.

Measurement And Pricing: The Answer Funnel

Classic viewability needs redefinition for answers. Consider a position‑weighted visibility model plus interaction depth.

  • Eligible impression: The answer unit is present in the rendered surface for at least 1 second or was read by the assistant in voice mode.
  • Qualified view: The user hovered, expanded, or let the unit remain on screen for 5 seconds or more.
  • Engagement: Click‑through or tool call execution.
  • Outcome: On‑site conversion or in‑assistant action completion.

Pricing models to offer:

  • CPM with position weighting: Higher weights for earlier units or voice readouts.
  • CPC/CPE: For interactive units with strong intent capture.
  • CPA: Where tool calls can confirm outcomes like add to cart or subscription start.
  • Hybrid: A low CPM floor plus outcome kicker for balanced risk.

Example Analytics Event Schema

SSPs and assistants need a shared event model. Here is an example event stream for analytics.

[
{
"event": "ans_eligible",
"ts": 1730912801123,
"session": "s-44",
"ans": {"surface_id": "as-1001", "unit_id": "au-1", "pos": 1},
"meta": {"voice": false}
},
{
"event": "ans_view",
"ts": 1730912803123,
"session": "s-44",
"ans": {"surface_id": "as-1001", "unit_id": "au-1"},
"metrics": {"duration_ms": 5400, "scroll_pct": 100}
},
{
"event": "ans_engage",
"ts": 1730912804123,
"session": "s-44",
"ans": {"surface_id": "as-1001", "unit_id": "au-1"},
"action": {"type": "tool_call", "tool": "compare_prices"}
},
{
"event": "ans_outcome",
"ts": 1730912815123,
"session": "s-44",
"ans": {"surface_id": "as-1001", "unit_id": "au-1"},
"outcome": {"type": "purchase", "value": 54.90, "currency": "USD", "confidence": 0.86}
}
]

This event model supports position weighting, interaction depth, and confidence scoring where outcomes occur off‑assistant. Privacy rules must apply and any identity signals must be properly consented and minimized.

Quality, Integrity, And Fraud Controls

Answer quality and ad integrity are existential. Buyers will only invest if they trust that sponsored content does not distort facts and that measurement is clean. Key controls:

  • Separation of concerns: The assistant composes organic content first. Sponsored content is additive and labeled. If pre‑generation influence occurs, record and disclose it.
  • Hallucination risk flags: Assistants should compute a confidence score for factual claims and suppress sponsorship when confidence is low.
  • IVT for assistants: Detect synthetic query farms and automated assistant calls with device attestations and behavior models.
  • Provenance: Maintain a provenance chain of sources used during RAG and expose hashes or signatures for audit. Content authenticity initiatives like C2PA are relevant for asset provenance.
  • Brand suitability: Apply GARM categories and risk tiers to both the answer topic and the landing context.

Standards to build on: IAB Tech Lab’s sellers.json and SupplyChain Object for transparent paths, ads.txt for authorization, and evolving industry guidance on AI disclosures. See sellers.json and SupplyChain Object background.

Privacy By Design For Assistant Inventory

The privacy bar must be higher for assistants than for pages because assistants can infer more context from smaller signals. Principles to adopt:

  • Minimize identifiers: Default to aggregate and on‑device signals. Use pseudonymous IDs only with explicit consent and valid business purpose.
  • First‑party preference: Prefer first‑party contextual and cohort signals over cross‑site tracking.
  • On‑device eligibility: Compute sensitive eligibility on device and send only non‑sensitive yes/no flags server side.
  • Data retention caps: Short windows for raw events and strict purpose limitation.
  • Regulatory compliance: Honor consent frameworks and regional rules. The IAB Europe TCF and US state privacy regimes are relevant, and W3C privacy principles are a good compass.

Relevant initiatives: Google Privacy Sandbox for web and Android introduces cohort and on‑device audiences like Topics, plus on‑device auctions via Protected Audience. See Privacy Sandbox. While assistant inventory does not map one‑to‑one, the on‑device ethos and consent handling patterns are applicable.

Forecasting, Packaging, And Yield

Assistant inventory must be forecasted differently than pageviews. Supply is a function of query distributions, topic seasonality, and interaction rates. Practical forecasting:

  • Query taxonomy: Classify assistant queries into categories like shopping, navigation, troubleshooting, and entertainment. Map to IAB content categories for buyer compatibility.
  • Seasonality modeling: Use publisher and app historical patterns. Travel spikes before holidays, CTV spikes during tentpoles.
  • Position curves: Estimate view and engagement probabilities by unit position and modality, including voice readouts.
  • Sensitivity filters: Remove or restrict categories where monetization is off limits.

Yield tactics:

  • Multi‑objective optimization: Balance revenue, user satisfaction, and publisher trust metrics. Penalize interventions that reduce satisfaction scores.
  • Bandit experimentation: Use contextual bandits to optimize creative selection and unit layout subject to policy constraints.
  • Outcome learning: Feed outcome signals back into ranking and pricing to shift from CPM to outcome‑weighted deals.

Example: Contextual Bandit Allocation Pseudocode

# Simplified bandit for selecting among eligible answer units
import random
class Arm:
def __init__(self, id):
self.id = id
self.success = 0
self.trials = 0
def ucb_score(arm, total, c=1.5):
if arm.trials == 0:
return float("inf")
import math
return (arm.success / arm.trials) + c * ( (2 * math.log(total)) / arm.trials ) ** 0.5
def select_arm(arms, total_trials):
scores = [(arm, ucb_score(arm, total_trials)) for arm in arms]
scores.sort(key=lambda x: x[1], reverse=True)
return scores[0][0]
def update(arm, outcome):
arm.trials += 1
arm.success += outcome
# Use
arms = [Arm("text_callout"), Arm("inline_card"), Arm("tool_call")]
total = 0
for t in range(1000):
total += 1
arm = select_arm(arms, total)
# Simulated binary outcome from engagement or CPA
outcome = 1 if random.random() < 0.2 else 0
update(arm, outcome)

This style of learning fits session‑level constraints and complements auction pricing.

CTV: Voice And Shoppable Answers On The Big Screen

CTV assistants are already guiding viewers to what to watch, how to set up devices, and where to subscribe. The answer unit on TV can be a spoken recommendation, a rail highlight with disclosure, or a QR‑linked card on screen. Considerations:

  • Latency constraints: Voice interactions demand sub‑300 ms decisions for sponsored callouts.
  • Remote interactions: QR codes and second‑screen actions are necessary for outcomes like trial starts.
  • Tile sponsorship: Programmatic promotion of rows and tiles aligned to the assistant’s recommendation rail.
  • Household privacy: Strict limits on identity and robust consent are mandatory for the lean‑back context.

Red Volcano can detect voice assistant SDKs in CTV apps and track which publishers expose recommendation rails amenable to sponsorship. That can feed SSP packaging and sales narratives.

Mobile App Assistants: In‑App Utility As Supply

Mobile apps increasingly embed assistant features for search, troubleshooting, and commerce flows. The answer unit here might trigger an in‑app tool call, making CPA‑style pricing compelling. Best practices:

  • SDK governance: Use sellers.json lineage and SDK signature checks to ensure the monetization path is authorized.
  • Cross‑promotion: Sponsored assistant callouts can cross‑sell app features like premium trials or bundles.
  • Offline caching: Pre‑cache eligible answer units for low latency in weak connectivity zones.

Red Volcano’s SDK intelligence helps SSPs and publishers vet which apps are ready and safe to onboard for assistant inventory.

Supply Chain Transparency For Answers

We need clarity about who is selling what. Ads.txt and app‑ads.txt have proven valuable. The supply side can extend this ethos to assistant surfaces. Proposal components:

  • assistants.txt: A publisher‑hosted file listing which assistant surfaces and SSPs are authorized to monetize answer units associated with the publisher’s content or app.
  • answers.json: An optional manifest declaring suitability policies, prohibited categories, and pricing floors for answer units.
  • SupplyChain ans extension: Add assistant model, RAG summary, and safety regime metadata to the existing schain node ext so buyers know which assistant produced the surface.

These can be developed through IAB Tech Lab working groups. The spirit is consistent with sellers.json and the SupplyChain object: make the path explicit and auditable. References: app‑ads.txt and sellers.json provide the baseline design patterns.

Publisher Policy And Editorial Controls

Publishers must retain editorial control and trust. Suggested controls to standardize in contracts and in protocol:

  • Organic first: Organic answer composition should not be suppressed or materially distorted by sponsorship.
  • Disclosure: Sponsored units must be labeled and the assistant should be able to explain the rationale.
  • Sensitive category protection: Create hard blocks and require additional checks for health, finance, and political topics.
  • Attribution fairness: When a citation sponsorship appears, include the underlying source link prominently.

These controls align with advertiser expectations for responsible media and with publisher brand safety.

A Data Model For Red Volcano To Power This Market

Red Volcano can operationalize answers as inventory by building reusable data assets. Data sets:

  • Assistant‑addressable map: Domains, apps, and CTV channels with assistant integration signals, schema.org markup, and content taxonomies that map to answer surfaces.
  • Technology stack catalog: Detection of assistant SDKs, voice libraries, and content schema in use.
  • Policy registry: Parsed ads.txt/app‑ads.txt and sellers.json joined with proposed assistants.txt to validate authorized monetization paths.
  • Suitability index: GARM‑aligned labeling of publisher sections to enable safe packaging.
  • Outcome likelihood models: Category‑level engagement priors to help SSPs price CPC and CPA deals.

Product modules:

  • Magma Web: Answer Inventory Explorer: Visualize answer‑ready surfaces by category, region, and tech stack. Export target lists and go‑to‑market briefs.
  • SDK Intelligence: Assistant Signals: Flag apps with in‑app assistants and estimate potential answer unit volume.
  • CTV Voice Atlas: Catalog CTV environments with voice features and surface types ready for sponsorship.
  • Compliance Watch: Monitor ads.txt, sellers.json, and assistants.txt alignment and alert on anomalies.

Engineering Considerations For SSPs

To stand up an answer supply product, SSP engineering teams need a pragmatic plan. Architecture components:

  • Answer metadata API: Define the ans extension for requests and responses and publish SDKs for assistant partners.
  • On‑device SDK: Lightweight module to prefetch eligibility and render disclosure where appropriate. Privacy‑first by default.
  • Ranking and policy engine: Enforce suitability, disclosure, and publisher policies alongside revenue optimization.
  • Measurement pipeline: Event ingestion specialized for answer units with position and interaction semantics.
  • Fraud detection: Behavioral models to detect synthetic queries and automated interactions.

Data contracts:

  • Latency SLOs: Define strict budgets like 150 ms for pre‑gen requests and 80 ms for post‑gen inserts.
  • Consent propagation: Carry consent strings and region tags, with server‑side enforcement and logging.
  • Explainability fields: Require each creative to declare a user‑friendly rationale plus a machine‑readable influence graph if pre‑generation shape was allowed.

Example SQL: Position‑Weighted Views

WITH weights AS (
SELECT 1 AS position, 1.0 AS w UNION ALL
SELECT 2, 0.7 UNION ALL
SELECT 3, 0.5 UNION ALL
SELECT 4, 0.3
),
views AS (
SELECT unit_id, position, COUNT(*) AS impressions
FROM events
WHERE event = 'ans_view'
GROUP BY unit_id, position
)
SELECT v.unit_id,
SUM(v.impressions * w.w) AS weighted_impressions
FROM views v
JOIN weights w
ON v.position = w.position
GROUP BY v.unit_id;

This yields a defensible impression metric aligned to assistant reading order.

Go‑To‑Market For SSPs And Publishers

How to launch in quarters, not years: Phase 1 - Discovery and Standards Alignment

  • Inventory audit: Use Red Volcano data to identify publishers and apps with assistant‑ready signals.
  • Partner design: Collaborate with 2 assistant platforms to agree on ans fields and SLOs.
  • Policy pack: Align on a common policy template with disclosure, suitability, and sensitive category rules.

Phase 2 - Pilot And Measurement

  • Category pilots: Run in 3 high‑intent categories like home improvement, recipes, and streaming recommendations.
  • Pricing experiments: Test CPM plus CPC hybrids and outcome kickers with 10 anchor advertisers.
  • Measurement validation: Third‑party verification for position‑weighted views and engagement rates.

Phase 3 - Scale And Programmatic Packaging

  • Private marketplaces: Curate PMPs by category and suitability tier with guaranteed position floors.
  • Programmatic guaranteed: Offer fixed volumes for assistant surfaces with SLAs.
  • CTV expansion: Onboard voice‑enabled channels with rail sponsorship products.

What Buyers Will Ask And How To Answer

Common buyer questions and supply‑side responses:

  • How do I know the assistant is not distorting facts? Organic content is generated first. Sponsorship is additive, labeled, and explainable. Confidence thresholds and safety checks are enforced.
  • Can I buy outcomes, not just impressions? Yes. We support CPC and CPA pricing with privacy‑aligned outcome signals and confidence scoring.
  • Is this transparent? The supply chain is declared via sellers.json and an ans extension to schain. Publisher and assistant identities are visible.
  • What about sensitive categories? We align with GARM, enforce stricter thresholds, and offer opt‑out categories where monetization is not permitted.

Provide buyers with a short technical addendum that includes the ans fields, measurement definitions, and policy statements. That builds trust quickly.

SEO To AEO: Preparing Publishers For Assistant Surfaces

Publishers can act now to make their content assistant‑ready.

  • Structured data: Use schema.org for FAQs, HowTo, Product, and Review. See [FAQPage markup](https://schema.org/FAQPage). This helps assistants form precise answer units.
  • Editorial summaries: Add concise verdict paragraphs that assistants can cite cleanly.
  • Provenance signals: Maintain bylines, publication dates, and update logs for trustworthy citations.
  • Policy files: Publish assistants.txt to declare monetization policies and authorized sellers once available.

Red Volcano can track adoption of structured data and policy files to guide publisher outreach and readiness scoring.

Risk Map And Mitigations

Risks to manage from day one:

  • Regulatory scrutiny: Assistants that influence decisions in health and finance will be scrutinized. Mitigation: opt out or enforce medical and financial content standards and disclosures.
  • User trust erosion: Poor labeling or intrusive sponsorship damages trust. Mitigation: standardized disclosure, light‑touch defaults, and user controls.
  • Assistant platform dependency: Over‑reliance on a single assistant creates platform risk. Mitigation: multi‑assistant integrations and standards‑based contracts.
  • Measurement disputes: New viewability definitions can create friction. Mitigation: third‑party validation and open metrics.
  • Publisher pushback: Fear of cannibalization. Mitigation: prove incremental revenue and fair attribution through controlled pilots.

What To Build Together: A Near‑Term Standards Set

The industry can converge on a small set of practical standards without boiling the ocean.

  • ans OpenRTB extension: Define surface, units, explainability, and policy fields as an open spec.
  • assistants.txt: Mirror ads.txt for assistant monetization authorization.
  • Answer Measurement 1.0: Position‑weighted viewability and engagement definitions with reference code.
  • GARM mapping for answers: Shared taxonomy aligning answer topics to risk tiers.

These follow the proven playbook of OpenRTB and ads.txt, giving buyers a familiar foundation. See OpenRTB and ads.txt.

The Red Volcano Roadmap

How Red Volcano can accelerate the market for our SSP and publisher customers:

  • Assistant Inventory Map: A Magma Web module that scores domains, apps, and CTV channels for assistant‑readiness, with tech stack signals and estimated answer unit volume.
  • Policy & Integrity Watch: Continuous monitoring of ads.txt, app‑ads.txt, sellers.json, and assistants.txt with alerts and visualizations.
  • Standards Lab: An open GitHub repo hosting the ans extension drafts, reference analytics schema, and sample adapters.
  • Sales Outreach Packs: Auto‑generated briefs for each target publisher or app with a readiness score, incremental revenue estimates, and suggested implementation steps.
  • CTV Voice Discovery: Dedicated CTV catalog of voice‑capable surfaces and recommended sponsorship packages.

These modules compound Red Volcano’s core strengths: discovery, verification, and actionable intelligence for the supply side.

Conclusion: The Answer Layer Is The Next Premium Surface

We are at the start of a shift in supply definition. Assistants compress discovery and decision into a single surface. That surface is measurable, explainable, and deeply contextual. It is also monetizable if the supply side acts with clarity and integrity. SSPs and publishers that treat answers as inventory will unlock new revenue streams and protect their role in the value chain. The path is clear: standardize the unit, protect the user, declare the chain, and measure what matters. Red Volcano is ready to help map the terrain and provide the intelligence to move from concept to commercial reality. The next great supply frontier will not be a page or a pre‑roll. It will be the answer.

References and Further Reading

These resources provide foundations and patterns relevant to the approaches described:

  • IAB Tech Lab OpenRTB: Protocol baseline for programmatic trading. https://iabtechlab.com/standards/openrtb/
  • ads.txt and app‑ads.txt: Seller authorization for web and app. https://iabtechlab.com/ads-txt/ and https://iabtechlab.com/app-ads-txt/
  • sellers.json: Supply side transparency. https://iabtechlab.com/sellers-json/
  • Privacy Sandbox: On‑device ads and measurement concepts. https://privacysandbox.com/
  • schema.org: Structured data for FAQ, HowTo, Product. https://schema.org/
  • GARM Brand Suitability: Industry framework for content suitability.