What is multi-location review classification at scale — and what is the multi-label-classification times per-label-confidence-calibration times per-label-drift times per-vertical-taxonomy times Joint-Commission-Sentinel-Event times FDA-MedWatch problem distinctive to this skill?

A multi-location operator with 50-300 stores ships per-location per-review classification across 10-15 review platforms (Google Business Profile + Yelp + Apple Maps + Facebook + TripAdvisor + Booking.com + Better Business Bureau + Trustpilot + Glassdoor + Indeed). Per-week per-portfolio: 5,000-50,000 reviews requiring multi-label classification across sentiment + aspect + topic + intent + complaint-class + fraud + per-vertical regulatory-trigger. The four-skill bundle on the review-classification agent — Ingest, Classify, Calibrate, Audit — sits above the review-management + per-platform-review + LLM-classifier + NLP + Bayesian + warehouse substrate (Birdeye + Reputation.com + Podium + GatherUp + ReviewTrackers + OpenAI + Anthropic + Google Gemini + Mistral + Cohere + Meta Llama + Hugging Face Transformers + spaCy + NLTK + scikit-learn + XGBoost + PyMC + Stan) and writes a per-location per-review per-label canonical classification record. The operationally distinctive anchor: per-location per-review multi-label classification (per-review sentiment NPS-aligned + Plutchik emotion wheel + Russell circumplex valence-arousal + per-review aspect (service + product + price + staff + cleanliness + speed + accuracy + accessibility + wait-time + atmosphere + parking + location + value) + per-review topic (per-vertical topic taxonomy + LDA + BERTopic + Top2Vec + dynamic-topic-model) + per-review intent (complaint + compliment + question + suggestion + recall + warranty-claim + retention-signal + churn-signal) + per-review complaint-class (per-vertical regulatory-trigger + per-vertical legal-risk + per-vertical safety-incident + per-vertical professional-licensing-board reportable) + per-review fraud-detection (per-review fake-review signal via FTC Fake Review Rule 16 CFR Part 465 + per-Google fake-review policy + per-Yelp fake-review policy + per-reviewer-history) + per-vertical NPS-impact + per-vertical retention-impact + per-vertical CSAT impact + per-vertical CES Customer Effort Score impact) + zero-shot + few-shot + LLM-as-judge + RLHF + DPO + per-label confidence calibration (Brier + ECE + MCE + reliability diagram + isotonic + Platt + temperature scaling + conformal + Mondrian conformal + ACI) + per-label drift detection (PSI + KL divergence + Jensen-Shannon + Wasserstein + MMD) + per-vertical taxonomy (ICD-10 + SNOMED-CT + LOINC + RxNorm healthcare + NAICS + SIC + GICS + UNSPSC) + per-vertical incident reporting (Joint Commission Sentinel Event Database + FDA MedWatch adverse event MAUDE + DEA per-state pharmacy + alcohol TTB enforcement + state-board enforcement + OSHA incident + EPA toxic-release + per-state consumer protection).

Why do Birdeye + Reputation.com + Podium + Brandwatch + OpenAI + Hugging Face Transformers break at multi-location-multi-platform-multi-label-classification scale?

Each review-management + LLM-classifier + NLP vendor ships per-account flat single-label classification primitive at single-platform level. None coordinates per-location per-review multi-label classification (sentiment + aspect + topic + intent + complaint-class + fraud + per-vertical regulatory-trigger) + zero-shot + few-shot + LLM-as-judge + RLHF + DPO + per-label confidence calibration (Brier + ECE + isotonic + Platt + conformal) + per-label drift detection (PSI + KL + JS + Wasserstein + MMD) + per-vertical taxonomy + per-vertical incident reporting simultaneously. None handles Joint Commission Sentinel Event + FDA MedWatch adverse event MAUDE + DEA per-state pharmacy + alcohol TTB enforcement + state-board enforcement + OSHA incident reporting convergence. None gates against FTC Fake Review Rule + ABA Model Rule + HIPAA + FINRA + Tennessee ELVIS Act + per-state biometric. None enforces SOX 302/404/906 when public-company franchisor material. None writes a per-location per-review per-label WORM classification audit trail. The four-skill bundle Ingest + Classify + Calibrate + Audit sits above the review-management + per-platform-review + LLM-classifier + NLP + Bayesian + warehouse substrate — it does not replace it.

How does Ingest + Classify work?

Ingest runs per-platform per-location per-review ingestion across Google Business Profile + Yelp Fusion + Apple Maps Business Connect + Facebook + TripAdvisor + Booking.com + Better Business Bureau + Trustpilot + Glassdoor + Indeed with per-vendor DPA + per-API rate-limit + per-source ToS compliance. Per-review normalization + per-review per-platform metadata (per-reviewer-history + verified-purchase tag + review-photo + review-video). Classify runs per-location per-review multi-label classification: per-review sentiment (NPS-aligned -100 to +100 + Plutchik emotion wheel + Russell circumplex) + per-review aspect (service + product + price + staff + cleanliness + speed + accuracy + accessibility + wait-time + atmosphere + parking + location + value) via spaCy + NLTK + Stanza + Flair + AllenNLP + Hugging Face Transformers aspect-based sentiment analysis (ABSA). Per-review topic classification via LDA + BERTopic + Top2Vec + dynamic-topic-model. Per-review intent classification (complaint + compliment + question + suggestion + recall + warranty-claim + retention-signal + churn-signal) via LLM zero-shot + few-shot + LLM-as-judge + RLHF + DPO Direct Preference Optimization. Per-review complaint-class (per-vertical regulatory-trigger + per-vertical legal-risk + per-vertical safety-incident + per-vertical professional-licensing-board reportable). Per-review fraud-detection via FTC Fake Review Rule 16 CFR Part 465 signal + per-Google fake-review policy + per-Yelp fake-review policy + per-reviewer-history. Per-vertical taxonomy alignment (ICD-10 + SNOMED-CT + LOINC + RxNorm + NAICS + SIC + GICS + UNSPSC).

What does this skill connect to on the review-classification agent and across the swarm?

On the review-classification agent: per-location per-review multi-label classification + per-label confidence calibration + per-label drift detection + per-vertical incident reporting. Across the swarm: auto-publish gating for AI review-responses (#606 DOWNSTREAM consumer of canonical per-review classification) + per-location AI review-response drafting (#565 DOWNSTREAM consumer of per-review classification) + per-location AI-calibrated forecasting (#600 same per-label calibration substrate) + per-location per-cohort two-sigma anomaly detection (#608 same multi-comparison correction + replication-crisis discipline substrate) + per-vertical compliance overlay (#615 DOWNSTREAM consumer of per-vertical incident-reporting + per-vertical regulatory-trigger) + integration-drift-monitor agent (#562 + #569 + #570) + per-state-overlay-composer (#599 UPSTREAM canonical for ABA + per-state attorney advertising + HIPAA + FINRA per-state). Commercial-pillar parent: review-response-orchestration.

What does the 6-workstream pre-engagement-baseline reporting cycle look like for this skill?

Every two weeks during the Tier 3 Fractional Digital Marketing Lead engagement, six workstreams report against the pre-engagement baseline. Workstream 1: per-portfolio per-location per-review per-label review-classification coverage — locations covered + platforms ingested + reviews classified. Workstream 2: Ingest per-platform per-location per-review ingestion flow — per-vendor DPA + rate-limit + ToS compliance + per-review per-platform metadata + per-reviewer-history. Workstream 3: Classify per-location per-review multi-label classification flow — sentiment + aspect + topic + intent + complaint-class + fraud-detection + per-vertical regulatory-trigger + per-vertical taxonomy alignment. Workstream 4: Calibrate per-label confidence calibration + per-label drift detection flow — Brier + ECE + isotonic + Platt + conformal + PSI + KL + JS + Wasserstein + MMD + replication-crisis discipline. Workstream 5: Regulatory-defense audit coverage — multi-label classification + calibration + drift + per-vertical taxonomy + per-vertical incident reporting (Joint Commission + MedWatch + DEA + OSHA) + FTC Fake Review + HIPAA + ABA + ELVIS Act + EU AI Act Article 50 + SOX. Workstream 6: FBC feedback-loop pattern-learning — per-review per-label realized-vs-predicted classification + per-vertical regulatory-trigger enforcement retrospective + per-label drift retrospective.

review-classification agent

How to build multi-location review classification at scale

Reviews arrive faster than any team can label them, and an unlabeled review is signal you paid for and never used. This guide builds the classification layer that turns every review into a routed per-location label record; the platforms and methods below are its inputs.

Birdeye + Reputation.com + Podium + GatherUp + Grade.us + ReviewTrackers + Yotpo + Trustpilot + Customer Lobby + NiceJob + Broadly + Swell + Reviewshake + Google Business Profile + Yelp Fusion + Apple Maps Business Connect + Facebook + TripAdvisor + Booking.com + Better Business Bureau + Glassdoor + Indeed + OpenAI + Anthropic + Google Gemini + Mistral + Cohere + Meta Llama + Hugging Face + sentence-transformers + InstructorEmbed + voyage AI + scikit-learn + XGBoost + LightGBM + Hugging Face Transformers + spaCy + NLTK + Stanza + Flair + Gensim + AllenNLP + AYLIEN + Lexalytics + MonkeyLearn + Brandwatch Iris + Sprout Social Listening + Talkwalker + PyMC + NumPyro + Stan + brms + Snowflake + BigQuery + Databricks ship per-account flat review-classification primitives. The Ingest + Classify + Calibrate + Audit skill bundle on the review-classification agent sits above the review-management + per-platform-review + LLM-classifier + NLP + Bayesian + warehouse substrate and writes a per-location per-review per-label canonical classification record with named regulatory anchors covering multi-label sentiment + aspect + topic + intent + complaint-class + fraud + per-label confidence calibration (Brier + ECE + isotonic + Platt + conformal) + per-label drift (PSI + KL + JS + Wasserstein + MMD) + per-vertical taxonomy (ICD-10 + SNOMED-CT + LOINC + RxNorm + NAICS + SIC + GICS + UNSPSC) + per-vertical incident reporting (Joint Commission Sentinel Event + FDA MedWatch + DEA + OSHA + TTB + state-board) + FTC Fake Review Rule + ABA Model Rule + HIPAA + FINRA + Tennessee ELVIS Act 2024 + EU AI Act Article 50 + SOX 302/404/906.

Published May 30, 2026 · 3,200 words

The 4-skill bundle on the review-classification agent

One agent. Four coordinated skills. The Ingest + Classify + Calibrate + Audit bundle runs above the review-management + per-platform-review + LLM-classifier + NLP + Bayesian + warehouse substrate and writes one canonical per-location per-review per-label classification record.

Ingest

Per-platform per-location per-review ingestion across Google Business Profile + Yelp Fusion + Apple Maps Business Connect + Facebook + TripAdvisor + Booking.com + Better Business Bureau + Trustpilot + Glassdoor + Indeed with per-vendor DPA + per-API rate-limit + ToS compliance. Per-review normalization + per-platform metadata (per-reviewer-history + verified-purchase tag + review-photo + review-video).

Classify

Per-location per-review multi-label classification: sentiment (NPS-aligned + Plutchik + Russell circumplex) + aspect (service + product + price + staff + cleanliness + speed + accuracy + accessibility + wait-time + atmosphere + parking + location + value) + topic (LDA + BERTopic + Top2Vec + dynamic-topic) + intent (complaint + compliment + question + suggestion + recall + warranty-claim + retention-signal + churn-signal) + complaint-class (per-vertical regulatory-trigger + safety-incident + professional-licensing-board reportable) + fraud-detection via FTC Fake Review Rule. Per-vertical taxonomy alignment (ICD-10 + SNOMED-CT + LOINC + RxNorm + NAICS + SIC + GICS).

Calibrate

Per-label confidence calibration: Brier + ECE + MCE + reliability diagram + isotonic + Platt + temperature scaling + conformal + Mondrian conformal + ACI. Per-label drift detection: PSI + KL divergence + Jensen- Shannon + Wasserstein + MMD. Per-vertical replication-crisis discipline (Brier-decomposition + proper scoring rules + Rosenbaum Γ + E-value + Bonferroni + BH FDR + Holm + Šidák). Per-review per-label severity P0-P4.

Audit

Per-location per-review per-label WORM classification record: per-review snapshot + per-label classification + per-label confidence calibration + per-label drift + per-vertical taxonomy alignment + per-vertical incident-report flag + per-anchor gate-pass + AI-ML provenance + EU AI Act FRIA. Retention: 7-year FTC + 7-year IRS + 7-year HIPAA + 7-year state bar + 6-year SEC + 3-year FINRA + 7-year SOX + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7/CC8.

The real ecosystem this sits above

Ingest + Classify + Calibrate + Audit does not replace review-management, per-platform review APIs, LLM- classifiers, NLP libraries, or Bayesian samplers. It sits above them and writes one canonical per-location per-review per-label classification record.

Review management + per-platform review

Birdeye + Reputation.com + Podium + GatherUp
ReviewTrackers + Yotpo + Trustpilot + Customer Lobby
Google Business Profile + Yelp Fusion + Apple Maps
Facebook + TripAdvisor + Booking.com + BBB + Trustpilot
Glassdoor + Indeed Employer review

LLM-classifier + NLP + embedding

OpenAI + Anthropic + Google Gemini + Mistral + Cohere
Meta Llama + Mistral + Qwen + DeepSeek
Hugging Face Transformers + spaCy + NLTK + Stanza + Flair
Gensim + AllenNLP + AYLIEN + Lexalytics + MonkeyLearn
sentence-transformers + InstructorEmbed + voyage AI Embed

Bayesian + warehouse + governance

PyMC + NumPyro + Stan + brms Bayesian
scikit-learn + XGBoost + LightGBM + CatBoost
Snowflake + BigQuery + Databricks + Redshift + ClickHouse
Iceberg + Hudi + Delta Lake time-travel
Apache Kafka + Confluent + AWS MSK + Azure Event Hubs

Compliance overlay

Five anchors run per-review per-label before any classification distributes to downstream decision systems. The first anchor is operationally distinctive: multi-label classification + per-label confidence calibration + per-label drift detection + per-vertical taxonomy + per-vertical incident reporting (Joint Commission Sentinel Event + FDA MedWatch + DEA + OSHA + TTB + state-board) converge on every per-review classification decision.

Anchor 1: Multi-label classification + per-label confidence calibration + per-label drift + per-vertical taxonomy + per-vertical incident reporting (operationally distinctive)

Per-location per-review multi-label classification (sentiment NPS-aligned + Plutchik + Russell circumplex + aspect service/product/price/staff/cleanliness/ speed/accuracy/accessibility/wait-time/atmosphere/ parking/location/value + topic LDA + BERTopic + Top2Vec + dynamic-topic-model + intent complaint/ compliment/question/suggestion/recall/warranty-claim/ retention-signal/churn-signal + complaint-class per-vertical regulatory-trigger + per-vertical legal-risk + per-vertical safety-incident + per-vertical professional-licensing-board reportable + fraud-detection per-review fake-review signal via FTC Fake Review Rule + per-Google + per-Yelp + per-reviewer-history + per-vertical NPS-impact + retention-impact + CSAT impact + CES impact). Zero-shot + few-shot + LLM-as-judge + RLHF + DPO Direct Preference Optimization. Per-label confidence calibration (Brier + ECE + MCE + reliability + isotonic + Platt + temperature scaling + conformal + Mondrian conformal + ACI). Per-label drift detection (PSI + KL divergence + Jensen-Shannon + Wasserstein + MMD). Per-vertical taxonomy (ICD-10 + SNOMED-CT + LOINC + RxNorm + NAICS + SIC + GICS + UNSPSC). Per-vertical incident reporting (Joint Commission Sentinel Event Database + FDA MedWatch adverse event MAUDE + DEA per-state pharmacy + alcohol TTB enforcement + state-board enforcement + OSHA incident + EPA toxic-release + per-state consumer protection). Per-vertical replication-crisis discipline (Brier-decomposition + proper scoring rules + Rosenbaum Γ + E-value + Bonferroni + BH FDR + Holm + Šidák).

Anchor 2: FTC Fake Review + Endorsement + FDD + Lanham

FTC Fake Review Rule 16 CFR Part 465 + FTC Endorsement Guides 16 CFR Part 255 + Section 5 + Pfizer 1972 + MARS + Health Products + CFPB UDAAP + Lanham + USPTO + Robinson-Patman + FDD Item 12 + 15-state franchise.

Anchor 3: ABA + HIPAA + FINRA + per-vertical

ABA Model Rule 7.1-7.5 + 1.18 + per-state attorney advertising 50-state. HIPAA 45 CFR 164.502/504/514 Safe Harbor when health/wellness review + state medical board. FINRA Rule 2210 + SEC Regulation FD + per-state professional licensing + FDA OPDP + DEA + alcohol + tobacco.

Anchor 4: ELVIS Act + EU AI Act + per-state biometric

Tennessee ELVIS Act 2024 right-of-publicity when AI- classified voice/likeness + per-state right-of-publicity. EU AI Act Article 50 transparency when AI- ML review classification + Article 13/14/15 + Annex III when AI-ML review classification drives escalation routing + Article 6/27 FRIA + DSA + DMA. GDPR Article 6/7/22/28/30 + LGPD + DPDP + PIPEDA + Quebec Law 25 + CCPA + CPRA + 18-state + per-state biometric (BIPA + CUBI + Washington biometric).

Anchor 5: Accessibility + Section 230 + SOX + WORM retention

WCAG 2.2 AA + ARIA + EAA + ADA Title III + Section 508 + Section 230 + DMCA Section 512. SOX 302/404/906 when public-company franchisor material + COSO + Exchange Act 13(b)(2) + SEC Reg S-K. NIST AI RMF + ISO 42001 + ISO 27001 + SOC 2 Type II. Per-vendor LLM zero-retention + per-source DPA + per-API rate-limit. Storage: AWS S3 Object Lock + Azure Blob immutable + GCS + Wasabi WORM. Retention: 7-year FTC + 7-year IRS + 7-year HIPAA + 7-year state bar + 6-year SEC + 3-year FINRA + 7-year SOX + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7/CC8.

6-workstream reporting cycle

Every two weeks during a Tier 3 Fractional Digital Marketing Lead engagement, six workstreams report against the pre-engagement baseline. No classification accuracy claims. Process commitments only.

1. Per-portfolio per-location per-review per-label review-classification coverage. Locations covered + platforms ingested + reviews classified.
2. Ingest per-platform per-location per-review ingestion flow. Per-vendor DPA + rate-limit + ToS + per-review per-platform metadata + per-reviewer-history.
3. Classify per-location per-review multi-label classification flow. Sentiment + aspect + topic + intent + complaint-class + fraud-detection + per-vertical regulatory-trigger + per-vertical taxonomy.
4. Calibrate per-label confidence calibration + per-label drift detection flow. Brier + ECE + isotonic + Platt + conformal + PSI + KL + JS + Wasserstein + MMD + replication-crisis discipline.
5. Regulatory-defense audit coverage. Multi-label classification + calibration + drift + per-vertical taxonomy + per-vertical incident reporting (Joint Commission + MedWatch + DEA + OSHA) + FTC Fake Review + HIPAA + ABA + ELVIS Act + EU AI Act Article 50 + SOX.
6. FBC feedback-loop pattern-learning. Per-review per-label realized-vs-predicted classification + per-vertical regulatory-trigger enforcement retrospective + per-label drift retrospective.

FAQ

What is multi-location review classification at scale — and what is the multi-label-classification times per-label-confidence-calibration times per-label-drift times per-vertical-taxonomy times Joint-Commission-Sentinel-Event times FDA-MedWatch problem distinctive to this skill?: A multi-location operator with 50-300 stores ships per-location per-review classification across 10-15 review platforms (Google Business Profile + Yelp + Apple Maps + Facebook + TripAdvisor + Booking.com + Better Business Bureau + Trustpilot + Glassdoor + Indeed). Per-week per-portfolio: 5,000-50,000 reviews requiring multi-label classification across sentiment + aspect + topic + intent + complaint-class + fraud + per-vertical regulatory-trigger. The four-skill bundle on the review-classification agent — Ingest, Classify, Calibrate, Audit — sits above the review-management + per-platform-review + LLM-classifier + NLP + Bayesian + warehouse substrate (Birdeye + Reputation.com + Podium + GatherUp + ReviewTrackers + OpenAI + Anthropic + Google Gemini + Mistral + Cohere + Meta Llama + Hugging Face Transformers + spaCy + NLTK + scikit-learn + XGBoost + PyMC + Stan) and writes a per-location per-review per-label canonical classification record. The operationally distinctive anchor: per-location per-review multi-label classification (per-review sentiment NPS-aligned + Plutchik emotion wheel + Russell circumplex valence-arousal + per-review aspect (service + product + price + staff + cleanliness + speed + accuracy + accessibility + wait-time + atmosphere + parking + location + value) + per-review topic (per-vertical topic taxonomy + LDA + BERTopic + Top2Vec + dynamic-topic-model) + per-review intent (complaint + compliment + question + suggestion + recall + warranty-claim + retention-signal + churn-signal) + per-review complaint-class (per-vertical regulatory-trigger + per-vertical legal-risk + per-vertical safety-incident + per-vertical professional-licensing-board reportable) + per-review fraud-detection (per-review fake-review signal via FTC Fake Review Rule 16 CFR Part 465 + per-Google fake-review policy + per-Yelp fake-review policy + per-reviewer-history) + per-vertical NPS-impact + per-vertical retention-impact + per-vertical CSAT impact + per-vertical CES Customer Effort Score impact) + zero-shot + few-shot + LLM-as-judge + RLHF + DPO + per-label confidence calibration (Brier + ECE + MCE + reliability diagram + isotonic + Platt + temperature scaling + conformal + Mondrian conformal + ACI) + per-label drift detection (PSI + KL divergence + Jensen-Shannon + Wasserstein + MMD) + per-vertical taxonomy (ICD-10 + SNOMED-CT + LOINC + RxNorm healthcare + NAICS + SIC + GICS + UNSPSC) + per-vertical incident reporting (Joint Commission Sentinel Event Database + FDA MedWatch adverse event MAUDE + DEA per-state pharmacy + alcohol TTB enforcement + state-board enforcement + OSHA incident + EPA toxic-release + per-state consumer protection).
Why do Birdeye + Reputation.com + Podium + Brandwatch + OpenAI + Hugging Face Transformers break at multi-location-multi-platform-multi-label-classification scale?: Each review-management + LLM-classifier + NLP vendor ships per-account flat single-label classification primitive at single-platform level. None coordinates per-location per-review multi-label classification (sentiment + aspect + topic + intent + complaint-class + fraud + per-vertical regulatory-trigger) + zero-shot + few-shot + LLM-as-judge + RLHF + DPO + per-label confidence calibration (Brier + ECE + isotonic + Platt + conformal) + per-label drift detection (PSI + KL + JS + Wasserstein + MMD) + per-vertical taxonomy + per-vertical incident reporting simultaneously. None handles Joint Commission Sentinel Event + FDA MedWatch adverse event MAUDE + DEA per-state pharmacy + alcohol TTB enforcement + state-board enforcement + OSHA incident reporting convergence. None gates against FTC Fake Review Rule + ABA Model Rule + HIPAA + FINRA + Tennessee ELVIS Act + per-state biometric. None enforces SOX 302/404/906 when public-company franchisor material. None writes a per-location per-review per-label WORM classification audit trail. The four-skill bundle Ingest + Classify + Calibrate + Audit sits above the review-management + per-platform-review + LLM-classifier + NLP + Bayesian + warehouse substrate — it does not replace it.
How does Ingest + Classify work?: Ingest runs per-platform per-location per-review ingestion across Google Business Profile + Yelp Fusion + Apple Maps Business Connect + Facebook + TripAdvisor + Booking.com + Better Business Bureau + Trustpilot + Glassdoor + Indeed with per-vendor DPA + per-API rate-limit + per-source ToS compliance. Per-review normalization + per-review per-platform metadata (per-reviewer-history + verified-purchase tag + review-photo + review-video). Classify runs per-location per-review multi-label classification: per-review sentiment (NPS-aligned -100 to +100 + Plutchik emotion wheel + Russell circumplex) + per-review aspect (service + product + price + staff + cleanliness + speed + accuracy + accessibility + wait-time + atmosphere + parking + location + value) via spaCy + NLTK + Stanza + Flair + AllenNLP + Hugging Face Transformers aspect-based sentiment analysis (ABSA). Per-review topic classification via LDA + BERTopic + Top2Vec + dynamic-topic-model. Per-review intent classification (complaint + compliment + question + suggestion + recall + warranty-claim + retention-signal + churn-signal) via LLM zero-shot + few-shot + LLM-as-judge + RLHF + DPO Direct Preference Optimization. Per-review complaint-class (per-vertical regulatory-trigger + per-vertical legal-risk + per-vertical safety-incident + per-vertical professional-licensing-board reportable). Per-review fraud-detection via FTC Fake Review Rule 16 CFR Part 465 signal + per-Google fake-review policy + per-Yelp fake-review policy + per-reviewer-history. Per-vertical taxonomy alignment (ICD-10 + SNOMED-CT + LOINC + RxNorm + NAICS + SIC + GICS + UNSPSC).
What does Calibrate + Audit do?: Calibrate runs per-label confidence calibration: per-label Brier score + Expected Calibration Error (ECE) + Maximum Calibration Error (MCE) + reliability diagram + isotonic regression + Platt scaling + temperature scaling + conformal prediction intervals + Mondrian conformal (per-label bin) + Adaptive conformal inference (ACI). Per-label drift detection: per-label Population Stability Index (PSI) + KL divergence + Jensen-Shannon divergence + Wasserstein distance + Maximum Mean Discrepancy (MMD). Per-vertical replication-crisis statistical discipline: per-classifier Brier-decomposition (reliability + resolution + uncertainty) + per-classifier proper scoring rules (logarithmic + Brier + spherical + RPS + CRPS + quantile + pinball) + per-classifier Rosenbaum sensitivity (Γ) + E-value (VanderWeele Ding 2017) + Bonferroni + Benjamini-Hochberg FDR + Holm-Bonferroni + Šidák multi-comparison correction. Per-review per-label severity classification: P0 per-vertical regulatory-trigger (Joint Commission Sentinel Event + FDA MedWatch adverse-event + DEA pharmacy violation + OSHA incident + alcohol TTB enforcement + state-board enforcement) (immediate alert + escalation) + P1 ABA Model Rule violation + per-state attorney advertising 72-hour + P2 fraud-class fail (FTC Fake Review Rule violation) 7-day + P3 per-label calibration drift 30-day + P4 docs-only. Gate runs 5 anchors per-review per-label before any classification distributes to downstream decision systems. (1) Multi-label classification + per-label confidence calibration + per-label drift detection + per-vertical taxonomy + per-vertical incident reporting (Joint Commission + MedWatch + DEA + OSHA + TTB + state-board) + replication-crisis discipline. (2) FTC Fake Review Rule 16 CFR Part 465 + FTC Endorsement Guides + Section 5 + Pfizer 1972 + CFPB UDAAP + Lanham + USPTO + Robinson-Patman + FDD Item 12. (3) ABA Model Rule 7.1-7.5 + 1.18 + per-state attorney advertising 50-state + HIPAA 45 CFR 164.502/504/514 Safe Harbor when health/wellness review + state medical board + FINRA Rule 2210 + SEC Regulation FD + per-state professional licensing + FDA OPDP + DEA + alcohol + tobacco. (4) Tennessee ELVIS Act 2024 right-of-publicity when AI-classified voice/likeness + per-state right-of-publicity + EU AI Act Article 50 transparency when AI-ML review classification + Article 13/14/15 + Annex III when AI-ML review classification drives escalation routing + Article 6/27 FRIA + DSA + DMA + GDPR Article 6/7/22/28/30 + LGPD + DPDP + PIPEDA + Quebec Law 25 + CCPA + CPRA + 18-state + per-state biometric (BIPA + CUBI + Washington biometric). (5) WCAG 2.2 AA + ARIA + EAA + ADA Title III + Section 508 + Section 230 + DMCA Section 512 + SOX 302/404/906 + COSO + Exchange Act 13(b)(2) + SEC Reg S-K. Audit writes a per-location per-review per-label WORM classification record: per-review snapshot + per-label classification + per-label confidence calibration + per-label drift + per-vertical taxonomy alignment + per-vertical incident-report flag + per-anchor gate-pass + AI-ML provenance + EU AI Act FRIA. Retention: 7-year FTC + 7-year IRS + 7-year HIPAA + 7-year state bar + 6-year SEC + 3-year FINRA + 7-year SOX + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7/CC8.
What does this skill connect to on the review-classification agent and across the swarm?: On the review-classification agent: per-location per-review multi-label classification + per-label confidence calibration + per-label drift detection + per-vertical incident reporting. Across the swarm: auto-publish gating for AI review-responses (#606 DOWNSTREAM consumer of canonical per-review classification) + per-location AI review-response drafting (#565 DOWNSTREAM consumer of per-review classification) + per-location AI-calibrated forecasting (#600 same per-label calibration substrate) + per-location per-cohort two-sigma anomaly detection (#608 same multi-comparison correction + replication-crisis discipline substrate) + per-vertical compliance overlay (#615 DOWNSTREAM consumer of per-vertical incident-reporting + per-vertical regulatory-trigger) + integration-drift-monitor agent (#562 + #569 + #570) + per-state-overlay-composer (#599 UPSTREAM canonical for ABA + per-state attorney advertising + HIPAA + FINRA per-state). Commercial-pillar parent: review-response-orchestration.
What does the 6-workstream pre-engagement-baseline reporting cycle look like for this skill?: Every two weeks during the Tier 3 Fractional Digital Marketing Lead engagement, six workstreams report against the pre-engagement baseline. Workstream 1: per-portfolio per-location per-review per-label review-classification coverage — locations covered + platforms ingested + reviews classified. Workstream 2: Ingest per-platform per-location per-review ingestion flow — per-vendor DPA + rate-limit + ToS compliance + per-review per-platform metadata + per-reviewer-history. Workstream 3: Classify per-location per-review multi-label classification flow — sentiment + aspect + topic + intent + complaint-class + fraud-detection + per-vertical regulatory-trigger + per-vertical taxonomy alignment. Workstream 4: Calibrate per-label confidence calibration + per-label drift detection flow — Brier + ECE + isotonic + Platt + conformal + PSI + KL + JS + Wasserstein + MMD + replication-crisis discipline. Workstream 5: Regulatory-defense audit coverage — multi-label classification + calibration + drift + per-vertical taxonomy + per-vertical incident reporting (Joint Commission + MedWatch + DEA + OSHA) + FTC Fake Review + HIPAA + ABA + ELVIS Act + EU AI Act Article 50 + SOX. Workstream 6: FBC feedback-loop pattern-learning — per-review per-label realized-vs-predicted classification + per-vertical regulatory-trigger enforcement retrospective + per-label drift retrospective.

Engage Completions

Two ways to engage. The Tier 1 Marketing Operations Audit maps the review-management + per-platform-review + LLM- classifier + NLP + Bayesian + warehouse substrate + multi-label classification + per-label calibration + per-label drift + per-vertical taxonomy + per-vertical incident reporting surface against the Ingest + Classify + Calibrate + Audit bundle. The Tier 3 Fractional Digital Marketing Lead with AI Swarm embeds 1-2 days per week for 6+ months and runs the bundle end-to-end against the review-classification agent across the swarm.

Tier 1 Marketing Operations Audit Tier 3 Fractional Digital Marketing Lead Not sure which tier? Take the 3-question quiz