Question 1

What does per-location multi-touch attribution model selection actually deliver, and how does the 4-skill bundle decompose?

Accepted Answer

An orchestration layer that sits above the operator multi-touch attribution + MMM + incrementality + warehouse + BI + experimentation + per-platform data-source + consent-management + policy-as-code + WORM-storage stack and produces per-location per-decision-class attribution outputs that are defensible against FTC Pfizer 1972 reasonable-basis substantiation, SEC Reg G + Item 10(e) non-GAAP reconciliation, Reg S-K Item 303 MD&A variance substantiation, and per-platform data-use enforcement — without quietly quoting last-click point estimates as "channel X drove $Y revenue" claims that are not defensible. The skill is a four-skill bundle on the walk-in-phone-attribution agent. Skill 1 — Select: choose the appropriate attribution method for each per-location per-decision-class question. Different methods suit different questions. Multi-touch attribution (MTA) — last-click + first-click + linear + time-decay + position-based + W-shaped + U-shaped + algorithmic Shapley + Markov-chain + Google Data-Driven Attribution DDA + Meta Attribution + Northbeam + Hyros + Triple Whale + Polar Analytics + Rockerbox + Adobe Customer Journey Analytics + Adobe Analytics — produces channel-credit estimates from observed user-level click + impression paths. MTA models have known identifiability limits: they cannot recover incrementality without an incremental experiment because they observe correlation not causation; they degrade severely when person-level tracking is unavailable (iOS 14.5 App Tracking Transparency April 2021, browser tracking-prevention, cookie deprecation reduce person-level signal); they are subject to the operator-counsel-approved cookie-consent and CCPA cross-context-behavioral-advertising opt-out boundary. MMM (Marketing Mix Modeling) — Meta Robyn + Google Meridian (open-sourced January 2024) + Uber Orbit + Google LightweightMMM + PyMC-Marketing + Recast + LiftLab + Mass Effect + Nielsen MMM — produces channel response curves from aggregate spend + outcome + context data without person-level tracking. MMM identifiability constraints (no unmeasured confounders, exchangeability, positivity, SUTVA) require operator-data-science-team-approved methodology specification; uncertainty quantification + sensitivity analysis required. Geo-experiment + incrementality testing — GeoLift (Meta) + Recast geo-experiment + Optimizely + LaunchDarkly + Statsig + Eppo + GrowthBook + Split + Amplitude Experiment — produces causal incrementality estimates from randomized or quasi-randomized intervention. Geo-experiments are the gold-standard causal validation. The Select skill applies operator-data-science-team-and-counsel-approved selection rules: high-stakes per-channel budget reallocation decisions require MMM + geo-experiment validation; tactical optimizations within a known-working channel may use MTA; causal incrementality claims for marketing communications require geo-experiment or randomized testing. Skill 2 — Fit: fit the selected method per operator-data-science-team-approved methodology specification with credible/confidence intervals + sensitivity analysis + replication checks. Skill 3 — Reconcile: cross-model triangulate when multiple methods address the same question (MTA + MMM + geo-experiment) and surface agreement vs disagreement. When agreement is strong, confidence is high; when disagreement is large, the operator data science team investigates (methodology drift, identification failure, data quality issue). Reconcile never silently picks the model with the largest channel attribution; the orchestration surfaces the disagreement to operator data science + counsel + marketing for review. Skill 4 — Attest: emit per-output per-recipient attestation (method used, identification strategy, uncertainty interval, sensitivity result, replication status, evidence-class taxonomy, FTC Pfizer substantiation chain when output may flow to marketing claims, SEC Reg G reconciliation when output feeds non-GAAP, counsel-policy-version) to the operator WORM audit trail. The attribution + MMM + incrementality + warehouse + BI + experimentation + per-platform data-source + consent vendors below ship strong primitives. The orchestration above them — operator-data-science-team-and-counsel-approved methodology selection + uncertainty quantification + cross-model triangulation + FTC substantiation chain preservation + SEC Reg G + SOX + ASC coordination + per-platform data-use enforcement + privacy posture + EU AI Act applicability evaluation + audit trail — is operator-side architecture.

Question 2

Where does single-vendor attribution stop compounding for DTC + multi-location operators in the post-iOS-14.5 post-cookie privacy-friendly era?

Accepted Answer

Single-vendor attribution is solved at the vendor level. Northbeam, Hyros, Triple Whale, Polar Analytics, Rockerbox, Adobe Customer Journey Analytics ship strong attribution platforms. Meta Robyn + Google Meridian + Uber Orbit ship strong open-source MMM. Recast + LiftLab + Mass Effect ship strong commercial MMM. GeoLift + Optimizely + Statsig + Eppo + GrowthBook + Split + Amplitude Experiment ship strong experimentation. The compound case the walk-in-phone-attribution agent has to handle is the one where (a) iOS 14.5 App Tracking Transparency (April 2021) reduced person-level signal across iOS users; person-level MTA models that worked pre-iOS-14.5 increasingly under-represent the true incremental contribution of paid media channels, (b) browser tracking-prevention — Apple WebKit Intelligent Tracking Prevention has progressively limited third-party cookies and storage since 2017 with continued tightening, Firefox Enhanced Tracking Protection similarly, Google’s long-promised Chrome third-party-cookie deprecation went through multiple delays and in July 2024 Google announced a user-choice approach rather than unilateral deprecation but the broader signal-loss trajectory continues, (c) Apple Privacy Manifests + Required Reason API + Apple’s ongoing tightening of SDK requirements add friction to person-level identifier use, (d) Google Privacy Sandbox APIs (Topics + Protected Audience formerly FLEDGE + Attribution Reporting + Related Website Sets) provide privacy-preserving alternatives but with limited compatibility coverage and ongoing implementation evolution, (e) Meta CAPI + Aggregated Event Measurement + Limited Data Use, Google Enhanced Conversions + Restricted Data Processing, Apple SKAdNetwork + AdAttributionKit are the per-platform privacy-preserving alternatives that replace person-level tracking with aggregated and modeled signal — but each has its own terms of use restricting downstream-data-use scope, (f) state privacy laws (CCPA + 19+ state-comprehensive-privacy laws) and EU GDPR + ePrivacy continue to limit person-level tracking, (g) the cumulative effect is that MTA models have lost reliability in absolute attribution while MMM + geo-experimentation have gained relative reliability; operator data science teams need to combine multiple methods rather than rely on any single method, (h) when attribution outputs flow into marketing claims (CMO quotes "channel X drove $Y in revenue" in pitch deck or case study), FTC Pfizer 1972 reasonable-basis substantiation + Lanham + state UDAP apply — point-estimate single-method attribution claims are not defensible when methods disagree materially, (i) for public registrants, when attribution outputs feed non-GAAP measures (adjusted attribution-credited revenue, adjusted contribution margin), SEC Regulation G + Item 10(e) of Regulation S-K require reconciliation to closest GAAP measure with equal-or-greater prominence + prohibits liquidity-measure non-GAAP in some contexts. When attribution outputs explain MD&A variance, SEC Reg S-K Item 303 requires defensible discussion. When attribution affects revenue allocation across performance obligations, ASC 606 applies. When attribution informs marketing expense capitalization, ASC 350 may apply. SOX 302/404 internal control over financial reporting applies. Without an orchestration layer above the attribution + MMM + incrementality + warehouse + BI + experimentation + per-platform data-source + consent vendors, attribution methodology gets picked by which vendor a data team owns rather than by which method suits the question, FTC Pfizer substantiation chains break when outputs flow to marketing claims, SEC Reg G reconciliation gets missed when outputs feed non-GAAP, ASC 606 + 280 + 350 implications get missed when attribution affects financial reporting, per-platform data-use restrictions (Meta CAPI + AEM + Limited Data Use, Google Enhanced Conversions + RDP, LiveRamp DPA) get violated when data crosses scope, CCPA cross-context opt-out propagation breaks when attribution joins behavioral data, EU AI Act applicability gets unevaluated when attribution informs high-stakes decisions, and the audit trail of "which output + which method + which identification strategy + which uncertainty + which sensitivity result + which counsel-policy-version drove which decision" fragments. The orchestration above the vendors is what holds the cross-method + cross-recipient + cross-regulatory invariants.

Question 3

How does Skill 1 Select choose among multi-touch attribution + MMM + incrementality testing for each decision class without defaulting to the most familiar method?

Accepted Answer

Selection is operator-data-science-team-and-counsel-approved per decision class. Step 1 — decision-class characterization. Different decisions need different methods. (a) High-stakes per-channel budget reallocation — moving meaningful budget from one channel to another based on attribution claim — requires causal incrementality evidence; MMM provides aggregate-data privacy-friendly response curves with identifiability constraints; geo-experiment provides causal validation; MTA alone is generally insufficient absent geo-experiment validation because MTA observes correlation not causation. (b) Tactical optimization within a working channel — adjusting bid weights or audience definitions within a channel already validated through experimentation — may use MTA for fine-grained directional signal with operator-counsel-approved caveats. (c) Causal incrementality claim — "running this campaign generated $X incremental revenue" — requires geo-experiment or randomized testing; MTA cannot recover incrementality without experimental data. (d) Per-location performance assessment — "this location is over/under-performing on marketing efficiency" — combines MMM for aggregate-data measurement + per-location attribution + per-location context variables. (e) Variance explanation for MD&A — "Q3 revenue grew Y% — what drove the growth" — requires the same defensibility that any MD&A claim requires; attribution + MMM + geo-experiment + macro context together. Step 2 — method selection. The Select skill applies operator-data-science-team-approved selection rules from the decision class to a candidate method. Step 3 — data availability assessment. Some methods require specific data conditions. MTA needs reliable person-level click + impression paths (severely degraded post-iOS-14.5 + post-cookie-deprecation). MMM needs aggregate spend + outcome + context data with sufficient temporal variation. Geo-experiment needs treatment-control geographic structure + sufficient power. The Select skill verifies data conditions before fitting. Step 4 — methodology spec. Once method selected, the Select skill applies operator-data-science-team-and-counsel-approved methodology specification (priors, identifying restrictions, saturation curves for MMM, parallel-trends for difference-in-differences, common-support for propensity score, instrument validity for IV, randomization quality check for randomized inference). Step 5 — audit. Per-decision-class method selection + data-availability + methodology-spec writes to WORM audit trail with rule-citation evidence + counsel-policy-version. The Select skill does not autonomously promote MTA-only outputs for high-stakes causal claims; operator-counsel-approved policy specifies the substitution rules.

Question 4

How does Skill 3 Reconcile handle cross-model disagreement when MTA + MMM + geo-experiment produce different effect estimates for the same question?

Accepted Answer

Cross-model disagreement is operator-data-science-team-and-counsel-approved investigation, not silent model selection. Step 1 — model disagreement metric. Reconcile computes per-method point estimates + uncertainty intervals + identifies where intervals overlap (agreement) versus do not overlap (disagreement). Step 2 — agreement zones — high-confidence findings. When MTA + MMM + geo-experiment intervals overlap meaningfully for the same channel effect estimate, confidence is high; the finding earns a higher evidence-class tier (confirmed-causal when geo-experiment confirms; likely-causal when MMM + MTA agree without geo-experiment). Step 3 — disagreement zones — investigation triggers. When intervals do not overlap, the operator data science team investigates root causes. Common causes include (a) MTA over-attribution to bottom-funnel channels because last-click-style models over-credit the last touchpoint; (b) MMM under-attribution to last-touchpoint channels when MMM specification does not properly capture last-touch effects; (c) MMM mis-specification — priors are wrong, saturation curves are wrong, adstock decay is wrong, identifiability is violated; (d) geo-experiment quality issues — treatment-control contamination, insufficient power, treatment-effect heterogeneity ignored; (e) data quality issues — Meta CAPI deduplication, Google Enhanced Conversions matching, attribution-window mismatches across systems. Step 4 — operator-counsel-approved disclosure of disagreement. When the data science team cannot resolve disagreement through methodology refinement, the disagreement itself is the finding — operator-counsel-approved framing reports the range of estimates with caveats rather than silently picking one. Step 5 — escalation for material decisions. Material per-channel budget reallocation decisions where models disagree route to operator-CMO + operator-CFO + operator data-science leadership for decision under uncertainty with operator-counsel-approved caveats. Step 6 — audit. Per-question per-model agreement/disagreement + investigation status + counsel-policy-version writes to WORM audit trail.

Question 5

What compliance does the orchestration enforce, and how does it map to MTA methodology + FTC Pfizer + SEC Reg G/SOX/MD&A/ASC + privacy/per-platform/iOS 14.5 + per-vertical/EU AI Act?

Accepted Answer

Five anchors. Anchor 1 — Multi-touch attribution methodology defensibility + cross-model triangulation + uncertainty + sensitivity. MTA models (last-click + first-click + linear + time-decay + position-based + W-shaped + U-shaped + algorithmic Shapley + Markov-chain + Google Data-Driven Attribution DDA + Meta Attribution) with operator-data-science-team-approved identification limitations + post-iOS-14.5 + post-cookie signal-loss accommodation. MMM (Meta Robyn + Google Meridian open-sourced January 2024 + Uber Orbit + Google LightweightMMM + PyMC-Marketing + Recast + LiftLab + Mass Effect + Nielsen MMM) with operator-data-science-team-approved methodology specification + uncertainty quantification + sensitivity analysis. Geo-experiment + incrementality testing (GeoLift Meta + Recast geo-experiment + Optimizely + Statsig + Eppo + GrowthBook + Split + Amplitude Experiment) for causal validation. Cross-model triangulation pattern. Anchor 2 — FTC Section 5 + FTC Pfizer (1972) reasonable-basis substantiation doctrine + Lanham Act 15 USC 1125(a) + state UDAP + FTC Endorsement Guides 2024 + FTC Fake Review Rule 16 CFR Part 465 (effective October 2024) when attribution outputs drive marketing claims (channel ROAS + payback period + incrementality + per-channel-credited revenue claims in pitch decks + case studies + public statements + paid media + franchise sales). Substantiation chain preserved through FTC limitations period (5-year typical) plus operator-counsel-set tail. Anchor 3 — SEC Regulation G + Item 10(e) of Regulation S-K non-GAAP measures + SEC Reg S-K Item 303 MD&A + SOX Section 302/404 internal control + ASC 606 revenue recognition + ASC 280 Operating Segments + ASC 350 intangible asset accounting + Statement on Auditing Standards 99 (AU-C 240). When attribution outputs feed non-GAAP measures (adjusted attribution-credited revenue, adjusted contribution margin) Reg G reconciliation applies. When attribution explains MD&A variance, Reg S-K Item 303 applies. When attribution affects revenue allocation across performance obligations, ASC 606 applies. When attribution informs marketing expense capitalization (rare but possible for customer-acquisition-cost capitalization in some accounting policies), ASC 350 may apply. SOX internal control applies throughout. Anchor 4 — Privacy + per-platform data-use + iOS 14.5 + cookie deprecation + Apple/Google privacy frameworks. iOS 14.5 App Tracking Transparency (April 2021). Apple WebKit ITP + Apple Privacy Manifests + Required Reason API + Apple SKAdNetwork + Apple AdAttributionKit. Firefox ETP. Google Chrome Privacy Sandbox (Topics + Protected Audience + Attribution Reporting + Related Website Sets) + Google Enhanced Conversions + Restricted Data Processing + Floodlight. Meta Conversions API + Aggregated Event Measurement + Limited Data Use. LiveRamp Data Processing Agreements + Snowflake Data Marketplace dataset licenses + per-platform Terms of Service. CCPA Section 1798.120 + Section 1798.121 sensitive PI + Section 1798.140(ae) cross-context-behavioral-advertising opt-out propagation through operator consent-management vendor + state-comprehensive-privacy patchwork. GDPR Articles 6 + 9 + 22 + 26 + Article 35 DPIA + ePrivacy. UK GDPR + UK PECR. Anchor 5 — Per-vertical regulator + EU AI Act. FDA Office of Prescription Drug Promotion DTC pharma + DEA controlled substances + DISCUS + TTB + per-state liquor + per--regulator + FDA CTP tobacco + state insurance + state real-estate when attribution outputs feed vertical marketing claims. EU AI Act (Regulation 2024/1689) Articles 9 risk management + 13 transparency + 14 human oversight + 26 deployer obligations when attribution informs high-stakes decisions affecting individuals (employment-adjacent + credit-adjacent + housing-adjacent + healthcare-adjacent marketing); Annex III high-risk framework when attribution operates as part of consequential decision system. Broader gate also enforced: COPPA + California AADC + DSA Article 28 when minor audiences + HIPAA when healthcare-vertical PHI + Washington MHMDA when health-context data + ADA Title III + WCAG 2.2 AA for output dashboards via policy-as-code (OPA Rego + AWS Cedar + Casbin + Cerbos + Oso). WORM audit trail (AWS S3 Object Lock + GCS retention + Azure Blob immutable + Snowflake Time Travel) with per-statute retention (FTC 7yr + state-AG variable + GDPR 6yr + CCPA 3yr + SOX 7yr + SEC Reg G/S-K 5yr + IRS 7yr + EU AI Act 10yr + HIPAA 6yr) per operator counsel policy.

Question 6

What does the engagement look like across Tier 1 → Tier 2 → Tier 3, and what does the Tier 3 reporting cycle commit to?

Accepted Answer

Tier 1 AI Readiness Assessment (2-3 weeks, diagnostic): audits the operator current per-location attribution posture against the 4-skill bundle + 5-anchor compliance overlay + per-vendor MTA + MMM + incrementality + warehouse + BI + experimentation + per-platform data-source + consent state; deliverable is a gap-pack report identifying which decision classes use mismatched methods (MTA-only for high-stakes causal claims), which outputs flow into marketing claims without FTC Pfizer substantiation chain, which non-GAAP measures lack SEC Reg G reconciliation, which MD&A variance explanations lack Reg S-K Item 303 substantiation, whether ASC 606 + 280 + 350 + SOX coordination is wired when attribution affects financial reporting, whether per-platform data-use restrictions (Meta CAPI + AEM + Limited Data Use, Google Enhanced Conversions + RDP + Apple SKAdNetwork + Apple Privacy Manifests, LiveRamp DPAs) are enforced, whether CCPA cross-context propagates from CDP through attribution, whether GDPR Article 22 + Article 35 DPIA workflow is wired, whether EU AI Act applies when attribution informs high-stakes decisions, and a recommended remediation sequence for Tier 2. Tier 2 AI Swarm Setup Sprint (4-8 weeks): builds the 4-skill bundle on the walk-in-phone-attribution agent, wires MTA + MMM + incrementality + warehouse + BI + experimentation + per-platform data-source + consent + policy-as-code + WORM-storage vendors (operator-chosen subset), configures the operator-data-science-team-and-counsel-approved decision-class methodology selection rules + cross-model triangulation protocol + uncertainty quantification + sensitivity + FTC Pfizer substantiation chain + SEC Reg G + Item 10(e) + Reg S-K Item 303 MD&A + ASC 606 + 280 + 350 + SOX coordination + per-platform data-use enforcement + privacy posture + EU AI Act applicability evaluation, runs 30-day shadow + canary period before flipping to enforce-mode. Tier 3 Fractional CMO with AI Swarm (6-month minimum, 1-2 days/wk embedded): continues operating with weekly/monthly per-decision-class Select + Fit + Reconcile + Attest + monthly cross-model triangulation review + quarterly per-platform data-use policy review against platform terms updates (Meta CAPI + AEM + Limited Data Use + Google Enhanced Conversions + RDP + Apple SKAdNetwork + Apple AdAttributionKit + Google Privacy Sandbox + LiveRamp DPA + Snowflake Data Marketplace) + quarterly compliance evidence packages. Tier 3 reporting is a 6-workstream pre-engagement-baseline reporting cycle (per-decision-class methodology-fit + cross-model triangulation coverage + FTC substantiation chain preservation + SEC Reg G + Reg S-K Item 303 substantiation + per-platform data-use compliance + WORM audit-trail completeness) measured against the operator’s pre-engagement baseline. Each workstream surfaces trend direction and the gap to operator-defined targets. Reporting carries explicit caveats: vendor SLA + Meta CAPI + AEM + Limited Data Use term updates + Google Enhanced Conversions + RDP term updates + Apple SKAdNetwork + Apple AdAttributionKit + Apple Privacy Manifests + Required Reason API updates + Google Privacy Sandbox roadmap + LiveRamp DPA updates + Snowflake Data Marketplace license updates + iOS App Tracking Transparency evolution + browser third-party-cookie roadmap + FTC Endorsement Guides + Fake Review Rule + Pfizer doctrine interpretive guidance + SEC interpretive guidance + GDPR + ePrivacy + CCPA + state-comprehensive-privacy implementing rules + EU AI Act implementing acts sit outside Completions control. Attorney-client privilege preservation across operator-data-science-team-and-counsel-approved methodology + FTC substantiation chain library + per-platform data-use policy + CCPA cross-context opt-out records + GDPR DPIA records + SEC Reg G reconciliation records + EU AI Act records is maintained per operator counsel policy.

Question 7

Who owns the attribution stack, the methodology specification, the FTC substantiation library, the SEC reconciliation, and the audit trail?

Accepted Answer

Operator owns every artifact. The multi-touch attribution subscriptions (Northbeam, Hyros, Polar Analytics, Triple Whale, Rockerbox, ChannelMix, Funnel.io, Adobe Analytics, Adobe Customer Journey Analytics, Salesforce Marketing Cloud Intelligence — operator chooses) run under operator billing on operator-controlled accounts. The MMM toolchain (Meta Robyn, Google Meridian open-sourced January 2024, Uber Orbit, Google LightweightMMM, PyMC-Marketing, Recast, LiftLab, Mass Effect, Nielsen MMM — operator chooses) runs on operator-controlled compute or under operator billing. The incrementality + experimentation subscriptions (GeoLift, Recast geo-experiment, Optimizely, LaunchDarkly, Statsig, Eppo, GrowthBook, Split, Amplitude Experiment, Google Search Ads 360 incrementality — operator chooses) run under operator billing. The data warehouse (Snowflake, Databricks, BigQuery, Redshift, ClickHouse, dbt — operator chooses) runs under operator cloud account. The BI tools (Looker, Tableau, Power BI, Sigma, Hex, Mode, ThoughtSpot — operator chooses) run under operator billing. The per-platform data-source integrations (Meta CAPI/AEM/Limited Data Use, Google Enhanced Conversions/RDP/Floodlight/Search Ads 360, Apple SKAdNetwork/AdAttributionKit/Privacy Manifests, LiveRamp DPAs, Snowflake Data Marketplace — operator chooses) run under operator credentials. The consent-management vendor (OneTrust, TrustArc, Ketch, Securiti, BigID — operator chooses) runs under operator account. The operator-data-science-team-and-counsel-approved decision-class methodology selection rules + DAG + identification-strategy library + uncertainty-quantification protocol + sensitivity-analysis protocol + replication protocol + cross-model triangulation protocol + evidence-class taxonomy + FTC Pfizer substantiation chain library + per-platform data-use policy + CCPA Section 1798.140(ae) cross-context propagation records + GDPR Article 22 + Article 35 DPIA records + SEC Reg G + Item 10(e) non-GAAP reconciliation library + SOX 302/404 + ASC 606 + 280 + 350 + Reg S-K Item 303 MD&A coordination workflow + EU AI Act compliance records all live in operator data-science + counsel + finance repo. The Select + Fit + Reconcile + Attest skill code lives in operator code repo. The policy-as-code policies (OPA Rego + AWS Cedar + Casbin + Cerbos + Oso) live in operator code repo, counsel-aligned. The WORM audit trail lives on operator-controlled cloud storage (AWS S3 Object Lock + GCS retention + Azure Blob immutable + Snowflake Time Travel) with per-statute retention enforcement. The FTC + SEC + SOX + ASC + GDPR + CCPA + state-comprehensive-privacy + per-platform data-use + EU AI Act compliance evidence records are operator-counsel-and-CFO-maintained. Completions owns the orchestration knowledge — how to design the per-decision-class methodology selection rules against the operator’s actual decision mix, how to wire cross-model triangulation between MTA + MMM + geo-experiment, how to wire uncertainty quantification + sensitivity analysis + replication without losing actionability, how to preserve FTC Pfizer substantiation chains when outputs flow into marketing claims, how to enforce per-platform data-use restrictions across the Apple + Google + Meta + LiveRamp + Snowflake ecosystem, how to propagate CCPA cross-context opt-out, how to wire GDPR Article 22 + DPIA, how to wire SEC Reg G + Item 10(e) + Reg S-K Item 303 MD&A + ASC 606 + 280 + 350 + SOX coordination with operator finance + disclosure committee, how to evaluate EU AI Act applicability — and that knowledge transfers under the Tier 3 transition path (30-60 days at engagement end with full hand-off of the methodology selection playbook, cross-model triangulation runbook, uncertainty + sensitivity runbook, FTC substantiation chain library, per-platform data-use enforcement playbook, CCPA cross-context propagation playbook, GDPR Article 22 + DPIA playbook, SEC Reg G + SOX + ASC coordination playbook, EU AI Act applicability playbook, and the compliance evidence-package generation playbook). Completions credentials revoke on engagement-end.

The real ecosystem this sits above

Multi-touch attribution

MMM + incrementality + experimentation

Warehouse + BI + per-platform data sources

Consent management

Policy-as-code + WORM + legal research

Frequently asked