Done-for-you offer · Fractional CMO with AI Swarm · rollup-reporting 4-skill bundle · rollup-reporting agent
Root-cause attribution diagnostics for DTC ecommerce, multi- location retail, multi-unit franchise, multi-location service brand, and PE-sponsored portfolio operators — Forecast + Diagnose + Surface + Attest 4-skill bundle on the rollup- reporting agent, under a 5-anchor compliance overlay anchored on causal-inference methodology + uncertainty + sensitivity, FTC Pfizer 1972 reasonable-basis substantiation, SEC Reg G + SOX + ASC + Reg S-K Item 303 MD&A, privacy + per-platform data-use, and per-vertical + EU AI Act
When a KPI moves unexpectedly, your marketing, finance, and executive teams need a defensible explanation — defensible at the next board meeting, the next SEC filing, the next FTC substantiation inquiry, the next state-AG inquiry, the next investor call. The diagnostic claim has to be supportable under FTC Pfizer 1972 reasonable-basis substantiation if it flows into marketing, SEC Regulation G + Item 10(e) of Regulation S-K reconciliation if it feeds non-GAAP measures, SEC Reg S-K Item 303 MD&A substantiation if it explains variance in management discussion and analysis, SOX Section 302/404 internal control evidence if it feeds financial reporting, and ASC 280 Operating Segments + ASC 606 revenue recognition if it informs segment reporting or revenue allocation. Causal-inference methodology imposes identifiability constraints — no unmeasured confounders, exchangeability, positivity, SUTVA, instrument validity when IV used, parallel trends when difference-in-differences used, common support when propensity score used — and claims that ignore those constraints are not defensible. Uncertainty quantification (Bayesian credible intervals or frequentist confidence intervals) and sensitivity analysis (Rosenbaum bounds, E-values, simulation-based sensitivity) belong in the output, never stripped before downstream consumption. When diagnostic-data joining occurs, CCPA Section 1798.140(ae) cross-context-behavioral-advertising opt-out + GDPR Articles 6 + 22 + 26 + Article 35 DPIA + state-comprehensive-privacy + per-platform data-use terms (Meta Conversions API + Aggregated Event Measurement + Limited Data Use + Google Enhanced Conversions + Restricted Data Processing + LiveRamp DPAs) apply. When diagnostic outputs inform high-stakes decisions affecting individuals, EU AI Act (Regulation 2024/1689) Articles 9 + 13 + 14 + 26 apply. The causal-inference, anomaly-detection, warehouse, BI, attribution, experimentation, statistical, and consent-management vendors below ship strong primitives. The orchestration above them — operator-counsel- and-data-science-team-approved DAG + identification strategy + uncertainty + sensitivity + replication + evidence-class taxonomy + per-recipient framing + FTC Pfizer substantiation chain + SEC Reg G + Item 10(e) + Reg S-K Item 303 MD&A + Reg S-K Item 1.05 + SOX + ASC 280 + ASC 606 coordination + CCPA cross-context propagation + GDPR Article 22 + DPIA + per- platform data-use + EU AI Act applicability evaluation + disclosure-committee coordination + audit trail — is operator-side architecture. You keep the causal-inference stack, the anomaly detection, the warehouse, the BI, the attribution, the experimentation, the statistical toolchain, the consent-management vendor, the DAG, the methodology, the FTC substantiation library, the SEC reconciliation, the WORM audit trail, the policy-as-code policies. You keep the ability to in-house at any time.
Published September 24, 2026
The real ecosystem this sits above
Causal inference + statistical toolchain
Causal inference: PyMC, Stan, Google CausalImpact, Microsoft DoWhy, Microsoft EconML, CausalNex, CausalLift, Pyro, R causal packages. Statistical: R, Python, statsmodels, scipy, sklearn, Stata. Each ships strong methodology primitives. Operator-counsel-and-data-science- team-approved DAG + identification strategy + uncertainty + sensitivity + replication above them is operator-side architecture.
Anomaly + warehouse + BI
Anomaly + RCA: Anodot, Datadog Watchdog, New Relic AI, Splunk ITSI, Dynatrace Davis AI, Honeycomb BubbleUp. Warehouse: Snowflake, Databricks, BigQuery, Redshift, ClickHouse, dbt. BI: Looker, Tableau, Power BI, Sigma, Hex, Mode, ThoughtSpot. Each ships strong primitives. Evidence-class taxonomy + per-recipient framing above them is operator-side architecture.
Attribution + experimentation
Attribution: Northbeam, Hyros, Polar Analytics, Triple Whale, Rockerbox, ChannelMix, Funnel.io. Experimentation: Optimizely, LaunchDarkly, Statsig, Eppo, GrowthBook, Split, Amplitude Experiment, GeoLift. Each ships strong primitives. Identification strategy alignment + sensitivity analysis + replication above them is operator-side architecture.
Consent + per-platform data-use
Consent: OneTrust, TrustArc, Ketch, Securiti, BigID. Per- platform: Meta Conversions API + Aggregated Event Measurement + Limited Data Use, Google Enhanced Conversions + Restricted Data Processing, LiveRamp DPAs, Snowflake Data Marketplace. Each ships strong primitives. CCPA cross-context propagation + per-platform data-use enforcement above them is operator-side architecture.
Policy-as-code + WORM + legal research
Policy-as-code: OPA Rego, AWS Cedar, Casbin, Cerbos, Oso. WORM: AWS S3 Object Lock, GCS retention, Azure Blob immutable, Snowflake Time Travel. Legal research: Westlaw, Lexis+, Bloomberg Law, Practical Law, Compliance.ai. Each ships strong primitives. The 5-anchor compliance gate that maps causal methodology + FTC Pfizer + SEC + privacy + per-vertical/EU AI Act onto an operator-counsel-approved policy bundle is operator-side architecture.
Frequently asked
What does root-cause attribution diagnostics actually deliver, and how does the 4-skill bundle decompose?
An orchestration layer that sits above the operator causal-inference + anomaly-detection + warehouse + BI + attribution + experimentation + statistical + consent-management + policy-as-code + WORM-storage stack and produces defensible diagnostic explanations when KPIs move unexpectedly — without quietly slipping unfounded causal claims into MD&A, marketing assets, or executive reporting. The skill is a four-skill bundle on the rollup-reporting agent. Skill 1 — Forecast: produce per-KPI per-segment forecasts using operator-data-science-team-approved methodology (Prophet, ARIMA, SARIMA, ETS, Holt-Winters, Bayesian state-space models, hierarchical Bayesian via PyMC and Stan — operator data-science team chooses subset). Forecasts emit with credible intervals (Bayesian) or confidence intervals (frequentist), never as point estimates. Forecast intervals provide the baseline against which observed values can be flagged as anomalies. Skill 2 — Diagnose: when observed values fall outside the forecast intervals, run a candidate-cause analysis through operator-counsel-and-data-science-approved causal-inference methodology. The methodology specifies (a) a directed acyclic graph (DAG) of plausible causal pathways approved by operator data science + operator counsel + operator marketing (the DAG is the operator-counsel-and-data-science-team-approved model of how outcomes can be affected); (b) the causal-inference method appropriate to the data pattern — difference-in-differences when treatment and control segments exist, synthetic control via CausalImpact (Google) or Recast geo-experiment when geographic treatment, propensity score matching when observational comparison, regression discontinuity when threshold-based intervention, instrumental variables when valid instrument available, randomized A/B when experiment data exists (Optimizely, LaunchDarkly, Statsig, Eppo, GrowthBook, Split, Amplitude Experiment — operator chooses); (c) uncertainty quantification with credible/confidence intervals around effect estimates; (d) sensitivity analysis for unmeasured confounding (Rosenbaum bounds, E-values, simulation-based sensitivity). Diagnose surfaces candidate causes with effect-estimate ranges + uncertainty + sensitivity, never as deterministic explanations. Skill 3 — Surface: emit diagnostic outputs to operator marketing + finance + executive consumers with operator-counsel-approved framing. Surface distinguishes (a) confirmed-causal findings (supported by experimental data with strong identification and confirmed by replication), (b) likely-causal findings (supported by quasi-experimental methods with reasonable identification but no replication), (c) plausible-causal hypotheses (consistent with the DAG and the data pattern but not yet identified), (d) operational-anomaly findings (pattern surfaced but causal explanation pending investigation). The taxonomy matters because FTC Pfizer 1972 reasonable-basis substantiation, SEC Reg G non-GAAP reconciliation, SEC Reg S-K Item 303 MD&A management discussion and analysis of variance, and Lanham 15 USC 1125(a) downstream marketing-claim defensibility all require evidence-class transparency. Skill 4 — Attest: emit per-diagnostic per-recipient attestation (which methodology applied, which DAG version, which identification strategy, which uncertainty interval, which counsel-policy-version, which evidence-class taxonomy) to the operator WORM audit trail. The audit trail is the chain of custody the operator relies on in an FTC substantiation inquiry, a state-AG inquiry, an SEC inquiry of MD&A variance explanation, a SOX 404 internal control review, an EU supervisory-authority inquiry when EU users involved, or a Lanham false-advertising challenge when diagnostics inform external claims. The causal-inference, anomaly-detection, warehouse, BI, attribution, experimentation, statistical, consent-management vendors below ship strong primitives. The orchestration above them — operator-counsel-and-data-science-team-approved DAG + methodology + uncertainty quantification + sensitivity analysis + evidence-class taxonomy + per-recipient framing + per-platform data-use enforcement + privacy posture + audit trail — is operator-side architecture.
Where does single-vendor anomaly detection stop compounding for defensible root-cause diagnostics?
Single-vendor anomaly detection is solved. Anodot ships strong time-series anomaly detection. Datadog Watchdog + New Relic AI + Splunk ITSI + Dynatrace Davis AI ship strong observability anomaly detection. Honeycomb BubbleUp ships strong attribute-level anomaly drilldown. Northbeam + Hyros + Triple Whale + Polar Analytics + Recast ship strong attribution + diagnostic-oriented analytics. The compound case the rollup-reporting agent has to handle is the one where (a) when a KPI moves unexpectedly, the operator marketing + finance + executive teams need an explanation that is defensible at the next quarterly board meeting, the next SEC filing, the next FTC substantiation inquiry, the next state-AG inquiry, the next investor call — not a point-estimate finger-pointing to a single cause when the data does not support single-cause identification, (b) diagnostic outputs often flow into MD&A variance explanations for public registrants (SEC Reg S-K Item 303 MD&A requires management to discuss material year-over-year changes in revenue, expenses, and other line items) — diagnostic claims that feed MD&A must satisfy operator-counsel-approved Reg S-K substantiation and disclosure committee review, (c) when diagnostic outputs feed non-GAAP financial measures (adjusted attribution-credited revenue, adjusted contribution margin) SEC Regulation G + Item 10(e) of Regulation S-K require reconciliation to the closest GAAP measure with equal-or-greater prominence and prohibitions on confusingly similar titles, (d) when diagnostic outputs flow into marketing claims (a CMO who quotes "ad spend in channel X drove $Y in revenue" in an outbound pitch deck) 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 reviews appear in marketing) apply, (e) causal-inference methodology imposes identifiability assumptions (no unmeasured confounders, exchangeability, positivity, SUTVA, instrument validity when IV used, parallel trends when DID used, common support when propensity score used) — claims that ignore identification failure are not defensible; reviewer-side independence (Rosenbaum bounds + E-values + sensitivity analysis for unmeasured confounding) is operator-data-science-team standard practice, (f) when diagnostic-data joining behavioral + transactional + identity data occurs, CCPA Section 1798.140(ae) cross-context-behavioral-advertising opt-out + GDPR Articles 6 + 22 + 26 + Article 35 DPIA + state-comprehensive-privacy + per-platform data-use terms (Meta Conversions API + Aggregated Event Measurement + Limited Data Use; Google Enhanced Conversions + Restricted Data Processing; LiveRamp DPAs) apply, (g) when diagnostic outputs inform high-stakes decisions affecting individuals (employment-adjacent marketing, credit-adjacent marketing, housing-adjacent marketing) EU AI Act (Regulation 2024/1689) Articles 9 + 13 + 14 + 26 apply, (h) when diagnostic outputs flow into per-vertical marketing FDA OPDP + DEA + DISCUS + per--regulator + state insurance + state real-estate apply. Without an orchestration layer above the causal-inference + anomaly + warehouse + BI + attribution + experimentation + consent vendors, methodology drifts (the data team picks a single anomaly-detection tool + treats its top-flagged cause as the deterministic explanation), uncertainty quantification disappears (claims get quoted as point ROAS or single-cause attribution without intervals), sensitivity analysis gets skipped, FTC Pfizer substantiation chains are not preserved when outputs flow into marketing claims, SEC Reg G reconciliation gets missed when outputs feed non-GAAP measures, Reg S-K Item 303 MD&A substantiation drifts, CCPA cross-context opt-out propagation breaks when joins to attribution data, per-platform data-use restrictions get violated, and the audit trail of "which forecast + which anomaly + which causal-inference method + which DAG + which identification strategy + which uncertainty interval + which sensitivity-analysis result + which counsel-policy-version drove which recipient finding" fragments. The orchestration above the vendors is what holds the cross-method + cross-recipient + cross-regulatory invariants.
How does Skill 2 Diagnose handle the DAG + causal-inference method selection + uncertainty + sensitivity analysis without drifting into deterministic single-cause explanations?
The Diagnose skill operates against operator-counsel-and-data-science-team-approved methodology with explicit identification constraints. Step 1 — operator-counsel-and-data-science-team-approved DAG. The DAG specifies the plausible causal pathways between operator-marketing-and-finance-team-approved variables. The DAG is maintained as documented operator-side artifact; changes route through operator counsel + operator data science + operator marketing review with documented rationale before merge. Step 2 — methodology selection driven by the data pattern + identification requirements. When randomized A/B test data is available, randomized inference + intent-to-treat estimation applies; when geographic treatment-control structure exists, difference-in-differences + synthetic control via Google CausalImpact + GeoLift apply; when propensity-matched observational comparison is possible, propensity score matching + inverse probability weighting apply with common-support verification; when threshold-based intervention occurred, regression discontinuity applies with bandwidth selection + manipulation testing; when a valid instrument is available, instrumental variables apply with relevance + exclusion + monotonicity validation. Operator data science team selects method by data pattern; the orchestration enforces operator-counsel-approved selection rules. Step 3 — uncertainty quantification. Bayesian methods produce posterior credible intervals; frequentist methods produce confidence intervals + bootstrap variants. The Diagnose skill never strips intervals before downstream consumption. Step 4 — sensitivity analysis. Rosenbaum bounds (how strong would unmeasured confounding need to be to overturn the conclusion) + E-values (Rubin sensitivity index) + simulation-based sensitivity (vary unmeasured confounding strength + re-estimate) provide robustness signal. Strong-effect findings that survive sensitivity testing earn higher evidence-class tier; weak-effect findings or findings sensitive to plausible unmeasured confounding earn lower evidence-class tier. Step 5 — replication check. When data permits, attempt independent replication on holdout data or independent population. Replicated findings earn higher evidence-class tier. Step 6 — evidence-class taxonomy assignment. The Diagnose skill assigns each finding to one of the operator-counsel-approved evidence-class tiers (confirmed-causal, likely-causal, plausible-causal-hypothesis, operational-anomaly-pending-investigation). The taxonomy controls how the finding may be used downstream — confirmed-causal findings may flow into marketing claims with FTC Pfizer substantiation; likely-causal findings may flow into internal operational decisions with documented caveats; plausible-causal-hypotheses route to additional investigation before any external use; operational-anomaly findings route to operator data science + operator counsel investigation. The Diagnose skill emits the evidence class + the methodology + the uncertainty intervals + the sensitivity-analysis result + the replication status + the DAG version + the counsel-policy-version to the WORM audit trail. The skill does not autonomously promote findings up the evidence-class tier; operator data science + operator counsel approve promotions with documented justification.
How does Skill 3 Surface handle SEC Reg G + Item 10(e) + Reg S-K Item 303 MD&A when diagnostic outputs feed financial reporting + non-GAAP measures + variance discussion?
When the operator is a public registrant or controlled subsidiary, Surface routes diagnostic outputs through SEC-specific compliance enforcement. SEC Regulation G + Item 10(e) of Regulation S-K govern non-GAAP financial measures. Reg G requires that any non-GAAP measure be reconciled to the most directly comparable GAAP measure with equal-or-greater prominence given to the GAAP measure; non-GAAP titles must not be confusingly similar to GAAP titles; reconciliations must explain reconciling items. Item 10(e) extends additional requirements for SEC filings including prohibitions on liquidity-measure non-GAAP presentations. When diagnostic outputs feed non-GAAP measures (adjusted attribution-credited revenue, adjusted contribution margin, adjusted ROAS) Surface verifies that the non-GAAP usage includes the operator-counsel-approved Reg G reconciliation. SEC Reg S-K Item 303 MD&A requires management to discuss material year-over-year and quarter-over-quarter changes in revenue + expenses + operating income + other line items. Diagnostic outputs that feed MD&A variance explanations must satisfy operator-counsel-approved Reg S-K substantiation — the discussion must reflect management’s actual understanding of the underlying drivers, not a single-cause attribution that ignores identification limits. SEC Reg S-K Item 1.05 Material Cybersecurity Incidents (effective December 18, 2023) imposes a four-business-day Form 8-K disclosure obligation when an incident is material — diagnostic outputs that surface a cybersecurity-incident-class anomaly route to the operator disclosure committee. SOX Section 302 (CEO/CFO certification) + Section 404 (internal control over financial reporting) apply when diagnostic outputs feed financial-reporting line items. ASC 280 Operating Segments applies when diagnostic outputs inform per-segment reporting. ASC 606 revenue recognition applies when diagnostic attribution affects revenue allocation across performance obligations. Statement on Auditing Standards 99 (now AU-C 240) on fraud considerations applies in audit context. Surface routes diagnostic outputs through the operator disclosure committee + operator counsel + CFO before any outbound communication to investors, board, regulators, or external parties. The disclosure committee operates as the operator-counsel-and-CFO-and-CEO-approved gate; Surface does not autonomously approve communications. Per-Surface per-recipient attestation records the recipient class + scope filter + Reg G non-GAAP reconciliation status + Reg S-K Item 303 substantiation status + disclosure committee approval status + counsel-policy-version + evidence-class taxonomy to the WORM audit trail.
What compliance does the orchestration enforce, and how does it map to causal methodology + FTC Pfizer substantiation + SEC Reg G/SOX/MD&A + privacy/platform data-use + per-vertical/EU AI Act?
Five anchors. Anchor 1 — Causal-inference methodology defensibility + uncertainty + sensitivity. Pearl Causal Hierarchy (association, intervention, counterfactual) + Rubin Causal Model + DAG specification + operator-data-science-team-approved identification strategy (difference-in-differences, synthetic control, propensity score matching, regression discontinuity, instrumental variables, randomized inference) + uncertainty quantification (Bayesian credible intervals, frequentist confidence intervals, bootstrap variants) + sensitivity analysis (Rosenbaum bounds, E-values, simulation-based sensitivity) + replication check + posterior predictive checks (Bayesian). Anchor 2 — FTC Section 5 + FTC Pfizer (1972) reasonable-basis substantiation doctrine + Lanham Act 15 USC 1125(a) false advertising + state UDAP + FTC Endorsement Guides 2024 + FTC Fake Review Rule 16 CFR Part 465 (effective October 2024) when root-cause findings drive marketing claims (the operator quotes diagnostic findings in pitch decks + case studies + public statements + paid media + franchise sales). Substantiation chain must exist in operator possession before claim runs and be retained 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 management discussion and analysis + SEC Reg S-K Item 1.05 Material Cybersecurity Incidents (effective December 18, 2023) + SOX Section 302 + 404 + ASC 280 Operating Segments + ASC 606 revenue recognition + Statement on Auditing Standards 99 (now AU-C 240). When diagnostic outputs feed financial reporting + non-GAAP measures + variance discussion + segment reporting for public registrants. Anchor 4 — Privacy + per-platform data-use. CCPA Section 1798.120 + Section 1798.121 sensitive PI + Section 1798.140(ae) cross-context-behavioral-advertising opt-out + state-comprehensive-privacy patchwork (Texas DPSA + Virginia CDPA + Connecticut CTDPA + Colorado CPA + Utah CPA + Oregon CPA + Tennessee IPA + Maryland Online Data Privacy Act + Washington MHMDA + Florida DBR + Delaware PDPA + Indiana CDPA + Iowa CDPA + Montana CDPA + Nebraska Data Privacy Act + Rhode Island DTPPA + Minnesota MCDPA). GDPR Articles 6 (lawful basis) + 9 (special category) + 22 (solely automated decisionmaking) + 26 (joint controller) + 30 (records of processing) + Article 35 DPIA when diagnostic-data joining is high-risk + ePrivacy. UK GDPR + UK PECR. Per-platform data-use terms — Meta Conversions API + Aggregated Event Measurement + Limited Data Use + Google Enhanced Conversions + Restricted Data Processing + LiveRamp Data Processing Agreements + Snowflake Data Marketplace dataset licenses + per-platform Terms of Service. Anchor 5 — Per-vertical regulator + EU AI Act. FDA Office of Prescription Drug Promotion DTC pharma when diagnostic findings feed pharma claims + DEA when controlled-substance prescribers + DISCUS Code + TTB + per-state liquor-board when alcohol + per--regulator when + FDA CTP when tobacco + state insurance-commissioner when insurance + state real-estate-commission when real estate. EU AI Act (Regulation 2024/1689) Articles 9 risk management + 13 transparency + 14 human oversight + 26 deployer obligations when diagnostics inform high-stakes decisions affecting individuals (employment-adjacent + credit-adjacent + housing-adjacent + healthcare-adjacent marketing); Annex III high-risk framework when diagnostics operate as part of consequential decision system. Broader gate also enforced: COPPA + California AADC + DSA Article 28 when minor audiences + ADA Title III + WCAG 2.2 AA for output dashboards + HIPAA when healthcare-vertical PHI + Washington MHMDA when health-context data 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.
What does the engagement look like across Tier 1 → Tier 2 → Tier 3, and what does the Tier 3 reporting cycle commit to?
Tier 1 AI Readiness Assessment (2-3 weeks, diagnostic): audits the operator current root-cause attribution posture against the 4-skill bundle + 5-anchor compliance overlay + per-vendor causal-inference + anomaly + warehouse + BI + attribution + experimentation + consent state; deliverable is a gap-pack report identifying which KPIs lack operator-data-science-team-approved forecast methodology + uncertainty intervals, which anomaly detections lack DAG + identification-strategy + sensitivity analysis, which diagnostic findings are quoted as point estimates without intervals, whether FTC Pfizer substantiation chains are preserved when diagnostic findings flow into marketing claims, whether SEC Reg G non-GAAP reconciliation is wired when diagnostic outputs feed non-GAAP measures + Reg S-K Item 303 MD&A substantiation, whether Reg S-K Item 1.05 disclosure-committee gate is wired when anomalies surface material cybersecurity incidents, whether SOX 302/404 + ASC 280 + ASC 606 coordination is wired with operator finance, whether CCPA cross-context propagation + GDPR Article 22 + Article 35 DPIA + per-platform data-use restrictions are wired when diagnostic-data joining occurs, whether EU AI Act applies when diagnostics inform 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 rollup-reporting agent, wires causal-inference + anomaly + warehouse + BI + attribution + experimentation + statistical + consent + policy-as-code + WORM-storage vendors (operator-chosen subset), configures the operator-counsel-and-data-science-team-approved methodology specification + DAG + identification strategies + uncertainty quantification + sensitivity analysis + replication + evidence-class taxonomy + FTC Pfizer substantiation chain library + SEC Reg G + Item 10(e) + Reg S-K Item 303 MD&A + Reg S-K Item 1.05 + SOX + ASC 280 + ASC 606 coordination + CCPA cross-context propagation + GDPR Article 22 + DPIA + per-platform data-use library + 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 Forecast + Diagnose + Surface + Attest cycles + monthly methodology + DAG review with operator counsel + data science + quarterly disclosure-committee coordination + quarterly compliance evidence packages. Tier 3 reporting is a 6-workstream pre-engagement-baseline reporting cycle (per-KPI forecast + uncertainty completeness + Diagnose methodology defensibility against operator-counsel-and-data-science-approved spec + evidence-class taxonomy compliance + FTC substantiation chain preservation + SEC Reg G + Reg S-K Item 303 substantiation + 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: causal-inference + anomaly + warehouse + BI + attribution + experimentation + consent vendor SLA + Meta CAPI + AEM + Limited Data Use term updates + Google Enhanced Conversions + RDP term updates + LiveRamp DPA updates + FTC Endorsement Guides + Fake Review Rule + Pfizer doctrine interpretive guidance + SEC interpretive guidance + Reg S-K Item 1.05 evolving guidance + GDPR + ePrivacy + CCPA + state-comprehensive-privacy implementing rules + EU AI Act implementing acts sit outside Completions control. Attorney-client privilege preservation across operator-counsel-and-data-science-team-approved methodology + DAG + FTC substantiation chain library + SEC Reg G reconciliation records + Reg S-K Item 303 substantiation records + privacy + DSA + EU AI Act compliance records is maintained per operator counsel policy.
Who owns the causal-inference stack, the DAG, the methodology, the FTC substantiation library, the SEC reconciliation, and the audit trail?
Operator owns every artifact. The causal-inference toolchain (PyMC, Stan, Google CausalImpact, Microsoft DoWhy, Microsoft EconML, CausalNex, CausalLift, Pyro, R causal packages — operator chooses) runs on operator-controlled compute. The anomaly-detection vendor (Anodot, Datadog Watchdog, New Relic AI, Splunk ITSI, Dynatrace Davis AI, Honeycomb BubbleUp — operator chooses) runs under operator billing. The data warehouse (Snowflake, Databricks, BigQuery, Redshift, ClickHouse, dbt — operator chooses) runs under operator cloud account. The BI subscriptions (Looker, Tableau, Power BI, Sigma, Hex, Mode, ThoughtSpot — operator chooses) run under operator billing. The attribution + MMM-hybrid subscription (Northbeam, Hyros, Polar Analytics, Triple Whale, Rockerbox, ChannelMix, Funnel.io — operator chooses) runs under operator billing. The experimentation vendor (Optimizely, LaunchDarkly, Statsig, Eppo, GrowthBook, Split, Amplitude Experiment + GeoLift — operator chooses) runs under operator billing. The statistical toolchain (R, Python, statsmodels, scipy, sklearn, Stata — operator chooses) runs on operator-controlled compute. The consent-management vendor (OneTrust, TrustArc, Ketch, Securiti, BigID — operator chooses) runs under operator account. The operator-counsel-and-data-science-team-approved methodology specification + DAG + identification-strategy library + uncertainty-quantification protocol + sensitivity-analysis protocol + replication protocol + evidence-class taxonomy + FTC Pfizer substantiation chain library + SEC Reg G + Item 10(e) non-GAAP reconciliation library + Reg S-K Item 303 MD&A substantiation library + Reg S-K Item 1.05 materiality-assessment records + SOX 302/404 internal control evidence + ASC 280 + ASC 606 coordination records + per-platform data-use policy + CCPA cross-context propagation records + GDPR Article 22 + Article 35 DPIA records + EU AI Act compliance records + disclosure-committee coordination policy all live in operator counsel + CFO + data science repo. The Forecast + Diagnose + Surface + Attest skill code lives in operator code repo. 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 policy-as-code policies (OPA Rego + AWS Cedar + Casbin + Cerbos + Oso) live in operator code repo, counsel-aligned. 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 operator-data-science-team-and-counsel-approved DAG against the operator’s actual decision architecture, how to wire identification strategy selection against the data patterns the operator encounters, how to wire uncertainty quantification + sensitivity analysis + replication without losing diagnostic actionability, how to preserve FTC Pfizer substantiation chains when diagnostic findings flow into marketing claims, how to wire SEC Reg G + Item 10(e) + Reg S-K Item 303 MD&A + Reg S-K Item 1.05 + SOX + ASC 280 + ASC 606 coordination with operator finance + disclosure committee, how to enforce per-platform data-use restrictions when diagnostic-data joining occurs, how to propagate CCPA cross-context opt-out, how to wire GDPR Article 22 + DPIA, 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 + DAG maintenance playbook, the identification-strategy library, the uncertainty-quantification + sensitivity-analysis runbook, the evidence-class taxonomy playbook, the FTC Pfizer substantiation chain library, the SEC Reg G + Reg S-K Item 303 + Item 1.05 + SOX + ASC coordination playbook, the privacy + DSA + EU AI Act playbook, and the compliance evidence-package generation playbook). Completions credentials revoke on engagement-end.
Engage Completions
Start with the AI Readiness Assessment (Tier 1, 2-3 weeks): audit of operator current root-cause attribution posture against the 4-skill bundle + 5-anchor compliance overlay + per-vendor causal-inference + anomaly + warehouse + BI + attribution + experimentation + consent state. Hand off to Tier 2 AI Swarm Setup Sprint (4-8 weeks): build the 4-skill bundle on the rollup-reporting agent, wire causal-inference + anomaly + warehouse + BI + attribution + experimentation + statistical + consent + policy-as-code + WORM-storage, configure operator-counsel- and-data-science-team-approved methodology + DAG + identification strategies + uncertainty + sensitivity + replication + evidence-class taxonomy + FTC Pfizer substantiation chain + SEC Reg G + Item 10(e) + Reg S-K Item 303 MD&A + Reg S-K Item 1.05 + SOX + ASC 280 + ASC 606 coordination + CCPA cross-context propagation + GDPR Article 22 + DPIA + per-platform data-use + EU AI Act applicability evaluation, run 30-day shadow + canary before flipping to enforce-mode. Continue under Tier 3 Fractional CMO with AI Swarm (6-month minimum, 1-2 days/wk embedded).