Done-for-you offer · Fractional CMO with AI Swarm · mlops 4-skill bundle · mlops agent
Response-shape drift detection for multi-unit franchise, multi-location retail, multi-location service brand, DTC ecommerce, B2B SaaS, and PE-sponsored portfolio operators — Sample + Compare + Decide + Attest 4-skill bundle on the mlops agent, under a 5-anchor compliance overlay anchored on NIST AI RMF + ISO 42001 + EU AI Act Article 9-17 + Article 26 + Article 50 + Article 72 post- market monitoring, model versioning + canary + shadow + champion-challenger + drift detection (PSI + KL + Wasser- stein + KS + Jensen-Shannon) + Model Cards + Datasheets + NIST AI 100-2, SOX 404 + SEC Reg S-K Item 1.05 + Item 106 + DTSA, per-vertical (HIPAA + FCRA + GLBA + FDA 21 CFR Part 11 + SaMD + CDS + GMLP + per-vertical regulator), and privacy + per-vendor LLM zero-retention + Colorado AI Act + NYC LL144 + EEOC + DSA
Your AI swarm runs 10-100 per-agent + per-skill model variants concurrently in production with continuous LLM + classifier + ranker + retriever response generation. Response-shape drift detection is operator-side architecture. NIST AI RMF (NIST AI 100-1) Map + Measure + Manage + ISO/IEC 42001 Clause 8 + EU AI Act (Regulation 2024/1689) Article 9 risk management + Article 10 data governance + Article 11 technical documentation + Article 12 record-keeping + Article 13 transparency + Article 14 human oversight + Article 15 accuracy + robustness + cybersecurity + Article 17 quality management system + Article 26 deployer + Article 50 generative-content marking + Article 72 post-market monitoring impose high- risk obligations. Model versioning + canary + shadow + champion-challenger deployment patterns + per-model drift detection (PSI Population Stability Index + KL divergence + Wasserstein distance + Kolmogorov-Smirnov + Jensen- Shannon + per-token logprob shift) + Model Cards for Model Reporting (Mitchell et al, FAT* 2019) + Datasheets for Datasets (Gebru et al, 2018/2021) + NIST AI 100-2 adversarial ML taxonomy + ISO/IEC TR 24028 trustworthiness + ISO/IEC 23053 ML framework apply. SOX Section 404 internal controls + SEC Reg S-K Item 1.05 Material Cybersecurity Incidents (effective December 18, 2023) + Item 106 annual cybersecurity disclosure apply when drift affects financial reporting or model integrity. DTSA 18 USC 1836 applies when model weights + training data + fine-tuning configurations constitute trade-secret. Per- vertical regulator (HIPAA 45 CFR 164.312 + FCRA + GLBA + FDA 21 CFR Part 11 + FDA SaMD + CDS + GMLP + per-vertical) applies per vertical. Colorado AI Act SB 24-205 (effective February 1, 2026) + NYC Local Law 144 (effective July 5, 2023) + EEOC algorithmic discrimination + Mobley v Workday class certification 2024 + EEOC 4/5ths rule apply. Per-vendor LLM zero-retention + CCPA + GDPR Article 22 + DSA + COPPA + AADC apply broadly. The model monitoring, LLM observability, MLOps platform, model registry, feature store, drift detection, and experimentation vendors below ship strong primitives. The orchestration above them is operator-side architecture. You keep all subscriptions, posture libraries, drift methodologies, and audit trail. You keep the ability to in-house at any time.
Published September 24, 2026
The real ecosystem this sits above
Model monitoring + LLM observability + drift detection
Model monitoring: Arize AI, Fiddler AI, WhyLabs, Evidently AI, Aporia, Censius, Datadog ML, New Relic AI, Galileo, Truera, Mona Labs. LLM observability: LangSmith, LangFuse, Helicone, Arize Phoenix, Weights & Biases Weave, Datadog LLM Observability, Confident AI, DeepEval. Drift detection: Evidently, Alibi Detect, River, NannyML. Each ships strong primitives. Per-monitor drift methodology + threshold + Model Cards + Datasheets + NIST AI 100-2 above them is operator-side architecture.
MLOps platform + model registry + feature store + experimentation
MLOps platform: MLflow, Weights & Biases, Comet, Neptune.ai, DVC, Determined AI, ClearML, Polyaxon. Model registry: MLflow, W&B, Comet, AWS SageMaker Model Registry, Vertex AI Model Registry, Azure ML Model Registry. Feature store: Feast, Tecton, Hopsworks, Featureform, AWS SageMaker Feature Store. Experimentation: Optimizely, GrowthBook, LaunchDarkly, Split.io, Statsig, Eppo, AB Tasty. Each ships strong primitives. Canary + shadow + champion-challenger deployment patterns + per-version baseline registration 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: Westlaw, Lexis+, Bloomberg Law, Practical Law. Each ships strong primitives. The 5-anchor compliance gate is operator-side architecture.
Frequently asked
What does response-shape drift detection deliver, and how does the 4-skill bundle decompose?
An orchestration layer above the operator model monitoring + LLM observability + MLOps platform + model registry + feature store + drift detection + experimentation + policy-as-code + WORM-storage stack that continuously samples per-agent + per-skill response shapes, compares them against operator-counsel-approved per-version baselines, decides per-detection action (alert + canary rollback + shadow + champion-challenger + paused + escalation), and attests every decision to operator WORM audit trail — under operator-counsel-and-AI-governance-team-approved NIST AI RMF + ISO 42001 + EU AI Act Article 9-17 + Article 72 + model versioning + SOX 404 + SEC Item 1.05 + DTSA + per-vertical regulator + privacy + per-vendor LLM zero-retention gates. Skill 1 — Sample: continuously sample per-agent + per-skill response shapes from operator model monitoring (Arize AI + Fiddler AI + WhyLabs + Evidently AI + Aporia + Censius + Datadog ML + New Relic AI + Galileo + Truera + Mona Labs — operator chooses) and operator LLM observability (LangSmith + LangFuse + Helicone + Arize Phoenix + Weights & Biases Weave + Datadog LLM Observability + Confident AI + DeepEval — operator chooses) at operator-counsel-approved sampling rate. Sample captures input features + output distributions + per-token logprobs (when available) + per-response confidence + per-response classifier outputs + per-response refusal/hedge rates + per-response groundedness scores + per-response citation completeness. Skill 2 — Compare: compare current sample against operator-counsel-approved per-version baseline using drift detection methodology — Population Stability Index (PSI), Kullback-Leibler divergence (KL), Wasserstein distance (earth mover’s), Kolmogorov-Smirnov test, Jensen-Shannon divergence, jensen-shannon for distribution-shape drift; sequence-level edit-distance + per-classifier-output frequency shift + per-token logprob distribution shift + per-classifier-output-frequency shift for response-shape drift. Compare reads from operator MLOps platform (MLflow + Weights & Biases + Comet + Neptune.ai + DVC + Determined AI + ClearML + Polyaxon — operator chooses) + operator model registry + operator feature store (Feast + Tecton + Hopsworks + Featureform + AWS SageMaker Feature Store — operator chooses). Skill 3 — Decide: per-detection decision under operator-counsel-and-AI-governance-team-approved per-class action matrix — alert at operator-counsel-approved threshold + canary rollback via operator experimentation platform (Optimizely + GrowthBook + LaunchDarkly + Split.io + Statsig + Eppo + AB Tasty — operator chooses) + shadow-mode re-promotion + champion-challenger re-evaluation + paused-mode with operator-counsel routing + escalation to operator AI-governance committee. Decide respects EU AI Act Article 14 human oversight modalities (continuous monitoring + on-call human override + scheduled human review checkpoints + automated kill-switches with human-approved triggers) when high-risk classification applies. Skill 4 — Attest: emit per-detection per-decision attestation (sample + drift methodology + drift magnitude + baseline version + per-class action + canary/shadow/champion-challenger version + human-review evidence + EU AI Act Article 14 oversight evidence + EU AI Act Article 50 generative-content marking + counsel-policy-version + AI-governance-policy-version) to the operator WORM audit trail.
Where does single-vendor model monitoring tooling stop compounding for response-shape drift detection at AI-swarm scale?
Single-vendor model monitoring is solved. Arize AI + Fiddler AI + WhyLabs + Evidently AI + Aporia + Censius + Datadog ML + New Relic AI + Galileo + Truera + Mona Labs ship strong managed model monitoring. LangSmith + LangFuse + Helicone + Arize Phoenix + Weights & Biases Weave + Datadog LLM Observability + Confident AI + DeepEval ship strong LLM observability. MLflow + Weights & Biases + Comet + Neptune.ai + DVC + Determined AI + ClearML + Polyaxon ship strong MLOps platform. Feast + Tecton + Hopsworks + Featureform + AWS SageMaker Feature Store ship strong feature stores. Evidently + Alibi Detect + River + NannyML ship strong drift detection. Optimizely + GrowthBook + LaunchDarkly + Split.io + Statsig + Eppo + AB Tasty ship strong experimentation. The compound case the mlops agent has to handle is the one where (a) the operator runs an AI swarm with 10-100 per-agent + per-skill model variants concurrently in production with continuous LLM + classifier + ranker + retriever response generation, (b) NIST AI RMF (NIST AI 100-1) Map + Measure + Manage functions impose continuous risk-management obligations including drift detection + post-deployment monitoring, (c) ISO/IEC 42001 Clause 8 Operation imposes AI management system requirements + monitoring + measurement + analysis + evaluation, (d) EU AI Act (Regulation 2024/1689) Article 9 risk management + Article 10 data governance + Article 11 technical documentation + Article 12 record-keeping + Article 13 transparency + Article 14 human oversight + Article 15 accuracy + robustness + cybersecurity + Article 17 quality management system + Article 26 deployer obligations + Article 72 post-market monitoring impose high-risk obligations when AI system is high-risk under Annex III, (e) model versioning + canary + shadow + champion-challenger deployment patterns + per-model drift detection methodology (PSI Population Stability Index + KL divergence + Wasserstein distance + Kolmogorov-Smirnov + Jensen-Shannon) + Model Cards for Model Reporting (Mitchell et al, FAT* 2019) + Datasheets for Datasets (Gebru et al, 2018/2021) + NIST AI 100-2 adversarial ML taxonomy + ISO/IEC TR 24028 trustworthiness + ISO/IEC 23053 ML framework, (f) SOX Section 404 internal controls over financial reporting when drift affects revenue recognition + ASC 606 + SEC Reg S-K Item 303 MD&A + SEC Reg S-K Item 1.05 Material Cybersecurity Incidents (effective December 18, 2023) four-business-day Form 8-K when model-integrity incident is material + Item 106 annual cybersecurity disclosure, (g) DTSA 18 USC 1836 + state Uniform Trade Secrets Act when model weights + training data + fine-tuning configurations constitute trade-secret, (h) per-vertical regulator — HIPAA 45 CFR 164.312 technical safeguards when AI processes PHI + FCRA 15 USC 1681 when consumer-report data + GLBA Safeguards Rule when financial data + FDA 21 CFR Part 11 electronic records and signatures when regulated computerized systems + FDA Software as a Medical Device (SaMD) guidance + FDA Clinical Decision Support guidance + per-vertical FDA OPDP + DEA + DISCUS + per--regulator + FDA Center for Tobacco Products + state insurance + state medical-board, (i) Colorado AI Act SB 24-205 (effective February 1, 2026) imposes developer + deployer obligations for high-risk AI systems making consequential decisions + risk-management program + algorithmic-discrimination prevention + impact assessments + consumer notice + right-to-explanation, (j) NYC Local Law 144 (effective July 5, 2023) imposes bias audit + annual audit + summary publication + candidate notice when AI used for employment decisions, (k) EEOC algorithmic discrimination guidance + EEOC v Workday + Mobley v Workday class certification 2024 + EEOC 4/5ths rule, (l) per-vendor LLM zero-retention attestation chain + CCPA cross-context + GDPR Article 22 automated individual decision-making + Article 25 privacy by design + Article 28 processor + Article 32 + Article 35 DPIA + DSA Article 16 + Article 28. Without an orchestration layer above the model monitoring + LLM observability + MLOps + drift detection + feature store + experimentation vendors, response-shape drift goes undetected, NIST AI RMF + ISO 42001 + EU AI Act Article 9-17 + Article 72 wiring fragments, SOX 404 internal-controls evidence breaks when drift affects financial reporting, SEC Item 1.05 materiality assessment for model-integrity incidents goes unprepared, DTSA exposure compounds, per-vertical regulator-specific monitoring fragments, Colorado AI Act + NYC LL144 + EEOC posture goes unmaintained, EU AI Act Article 14 human oversight modalities fragment. The orchestration above the vendors is what holds the cross-agent + cross-skill + cross-version + cross-vertical invariants.
How does Skill 2 Compare handle drift detection methodology + Model Cards + Datasheets + NIST AI 100-2 + ISO/IEC TR 24028?
Drift detection methodology is operator-counsel-and-AI-governance-team-approved per-monitor. Population Stability Index (PSI) measures distribution shift between baseline and current; PSI under 0.1 is typically considered insignificant + 0.1-0.25 minor + over 0.25 significant in financial-services adoption. Kullback-Leibler (KL) divergence measures relative entropy between distributions + asymmetric. Wasserstein distance (earth mover’s) measures geometric distance between distributions + symmetric. Kolmogorov-Smirnov test compares cumulative distributions. Jensen-Shannon divergence is symmetric variant of KL. Per-token logprob shift detects LLM behavior change. Per-classifier-output-frequency shift detects category-distribution change. Sequence-level edit-distance detects response-text-shape change. Model Cards for Model Reporting (Mitchell et al, FAT* 2019) document model intended use + performance + caveats + bias evaluation. Datasheets for Datasets (Gebru et al, 2018/2021) document dataset motivation + composition + collection + processing + uses + distribution. NIST AI 100-2 (Adversarial Machine Learning) provides taxonomy of attacks + defenses. ISO/IEC TR 24028 (information technology — artificial intelligence — trustworthiness) provides framework. ISO/IEC 23053 (framework for AI systems using ML) provides framework. Compare runs operator-chosen drift methodology + operator-chosen baselines + operator-chosen thresholds at operator-counsel-and-AI-governance-team-approved cadence. Threshold setting is itself an operator-counsel-approved policy decision under EU AI Act Article 15 accuracy + robustness requirements. Per-monitor drift methodology + per-monitor threshold + per-monitor model-card + per-monitor datasheet + per-monitor NIST AI 100-2 attestation writes to WORM audit trail with rule-citation evidence + counsel-policy-version + AI-governance-policy-version.
How does Skill 3 Decide handle EU AI Act Article 14 human oversight + Colorado AI Act + NYC LL144 + EEOC?
Human-oversight + algorithmic-discrimination posture is operator-counsel-and-AI-governance-team-approved. EU AI Act (Regulation 2024/1689) Article 14 human oversight modalities for high-risk AI systems — continuous monitoring of AI outputs + on-call human override + scheduled human review checkpoints + automated kill-switches with human-approved triggers + accountability for monitoring outcomes. Article 26 deployer obligations including technical and organisational measures + monitoring + log-keeping + supplier cooperation. Article 72 post-market monitoring system requirements. Colorado AI Act SB 24-205 (effective February 1, 2026) — for high-risk AI making consequential decisions in employment + housing + insurance + financial services + legal services + healthcare + government + education — imposes developer obligations (risk-management + impact assessment + documentation) + deployer obligations (risk-management program + algorithmic-discrimination prevention + impact assessments + consumer notice + right-to-explanation + AG-cooperation). NYC Local Law 144 (effective July 5, 2023) requires automated employment decision tools to have annual bias audit + audit summary publication + candidate notice. EEOC algorithmic discrimination guidance + EEOC v Workday + Mobley v Workday class certification 2024 + EEOC 4/5ths rule (selection rate for any race + sex + ethnic group at less than 4/5 of rate for group with highest rate triggers adverse-impact analysis). Per-state employment-discrimination + per-state UDAP. Decide enforces per-detection-class action under operator-counsel-and-AI-governance-team-approved matrix — alert at operator-counsel-approved threshold + canary rollback when drift detected + shadow-mode re-evaluation + champion-challenger re-evaluation + paused-mode with operator-counsel routing for high-risk classifications + automatic kill-switch escalation to operator AI-governance committee when Article 14 thresholds breached + bias-audit re-run when EEOC + NYC LL144 + Colorado AI Act covered + human-review-checkpoint scheduling for high-risk classifications. Per-detection EU AI Act Article 14 + Colorado AI Act + NYC LL144 + EEOC posture attestation writes to WORM audit trail with rule-citation evidence + counsel-policy-version + AI-governance-policy-version.
What compliance does the orchestration enforce, and how does it map to NIST AI RMF + ISO 42001 + EU AI Act + SOX 404 + SEC Item 1.05 + per-vertical + privacy?
Five anchors. Anchor 1 — NIST AI RMF + ISO 42001 + EU AI Act Article 9-17 + Article 72. NIST AI RMF (NIST AI 100-1) Map + Measure + Manage. ISO/IEC 42001 Clause 8 Operation + Clause 9 Performance evaluation. EU AI Act (Regulation 2024/1689) Article 9 risk management + Article 10 data governance + Article 11 technical documentation + Article 12 record-keeping + Article 13 transparency + Article 14 human oversight modalities + Article 15 accuracy + robustness + cybersecurity + Article 17 quality management system + Article 26 deployer obligations + Article 50 generative-content marking when AI-generated outputs + Article 72 post-market monitoring system. Anchor 2 — Model versioning + canary + shadow + champion-challenger + drift detection methodology + Model Cards + Datasheets + NIST AI 100-2. Canary + shadow + champion-challenger deployment patterns + per-model drift detection (PSI + KL divergence + Wasserstein + Kolmogorov-Smirnov + Jensen-Shannon + per-token logprob shift + per-classifier-output-frequency shift + sequence-level edit-distance) + Model Cards for Model Reporting (Mitchell et al, FAT* 2019) + Datasheets for Datasets (Gebru et al 2018/2021) + NIST AI 100-2 adversarial ML taxonomy + ISO/IEC TR 24028 trustworthiness + ISO/IEC 23053 ML framework. Anchor 3 — SOX 404 + SEC Item 1.05 + Item 106 + DTSA + state UTSA. SOX Section 404 internal controls over financial reporting + Section 302 CEO/CFO certifications + ASC 606 + SEC Reg S-K Item 303 MD&A + SEC Reg S-K Item 1.05 Material Cybersecurity Incidents (effective December 18, 2023) four-business-day Form 8-K when material + Item 106 annual cybersecurity disclosure + DTSA 18 USC 1836 + state Uniform Trade Secrets Act when model weights + training data + fine-tuning configurations constitute trade-secret. Anchor 4 — Per-vertical regulator. HIPAA 45 CFR 164.312 technical safeguards when AI processes PHI + FCRA 15 USC 1681 + GLBA Safeguards Rule + FDA 21 CFR Part 11 electronic records and signatures when regulated computerized systems + FDA Software as a Medical Device (SaMD) guidance + FDA Clinical Decision Support (CDS) guidance + FDA Good Machine Learning Practice (GMLP) + per-vertical FDA OPDP + DEA + DISCUS + per--regulator + FDA Center for Tobacco Products + state insurance commissioner + state medical-board. Anchor 5 — Privacy + per-vendor LLM zero-retention + Colorado AI Act + NYC LL144 + EEOC + DSA. CCPA Section 1798.140(ae) cross-context + state-comprehensive-privacy + GDPR Articles 5 + 6 + 9 + 22 automated individual decision-making + 25 + 26 + 28 + 30 + 32 + 35 DPIA + ePrivacy + UK GDPR + UK PECR + EU DSA Article 16 + Article 28 + COPPA + AADC + per-vendor LLM data-retention attestation (OpenAI Enterprise + ChatGPT Enterprise zero-retention; Anthropic API + Claude for Work zero-retention; Google Vertex AI zero-retention; Microsoft Azure OpenAI Service zero-retention; AWS Bedrock zero-retention) + Colorado AI Act SB 24-205 (effective February 1, 2026) + NYC Local Law 144 (effective July 5, 2023) + EEOC algorithmic discrimination guidance + EEOC v Workday + Mobley v Workday class certification 2024 + EEOC 4/5ths rule. Broader gate enforced via policy-as-code. WORM audit trail with per-statute retention (SOX 7yr + SEC Item 1.05 5yr + Item 106 5yr + DTSA 3yr + state UTSA variable + HIPAA 6yr + FCRA 5yr + GLBA 6yr + FDA Part 11 variable + Colorado AI Act variable + NYC LL144 variable + EEOC 1yr + GDPR 6yr + CCPA 3yr + EU AI Act 10yr) 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): audits the operator current response-shape drift detection posture; gap-pack identifies which per-agent + per-skill model variants lack baseline registration in operator MLOps platform, which lack drift detection methodology + threshold setting, which lack canary + shadow + champion-challenger deployment patterns, which lack SOX 404 internal-controls evidence + SEC Item 1.05 materiality assessment routing + Item 106 annual cybersecurity disclosure posture, which lack DTSA + state UTSA trade-secret-protection register, which lack per-vertical regulator (HIPAA + FCRA + GLBA + FDA Part 11 + SaMD + CDS + GMLP + per-vertical) wiring, which lack Colorado AI Act + NYC LL144 + EEOC posture, which lack EU AI Act Article 9-17 high-risk wiring + Article 72 post-market monitoring + Article 14 human oversight modalities, which lack per-vendor LLM zero-retention attestation chain, which lack CCPA + GDPR + DSA posture. Tier 2 AI Swarm Setup Sprint (4-8 weeks): builds the 4-skill bundle on the mlops agent, wires model monitoring + LLM observability + MLOps platform + model registry + feature store + drift detection + experimentation + policy-as-code + WORM-storage (operator-chosen subset), configures the operator-counsel-and-AI-governance-team-approved per-monitor drift methodology + threshold + Model Cards + Datasheets + NIST AI 100-2 + ISO/IEC TR 24028 + ISO/IEC 23053 + SOX 404 internal-controls + SEC Item 1.05 + Item 106 + DTSA register + per-vertical regulator wiring + Colorado AI Act + NYC LL144 + EEOC + EU AI Act Article 9-17 + 26 + 50 + 72 + Article 14 human oversight modalities + per-vendor LLM zero-retention attestation chain + CCPA + GDPR + DSA, runs 30-day shadow + canary with Decide in audit-only before flipping to enforce-mode. Tier 3 Fractional CMO with AI Swarm (6-month minimum): continues with continuous Sample + Compare + Decide + Attest. Tier 3 reporting is a 6-workstream pre-engagement-baseline reporting cycle (per-monitor drift methodology + threshold freshness + Model Card + Datasheet + NIST AI 100-2 freshness + SOX 404 + SEC Item 1.05 routing freshness + per-vertical regulator wiring freshness + EU AI Act Article 9-17 + 72 evidence + Colorado AI Act + NYC LL144 + EEOC posture freshness + per-vendor LLM zero-retention attestation freshness + WORM audit-trail completeness) measured against the operator’s pre-engagement baseline. Reporting carries explicit caveats: vendor SLA + NIST AI RMF version updates + ISO 42001 + ISO 27001 + ISO/IEC TR 24028 + ISO/IEC 23053 amendments + EU AI Act implementing acts + EU AI Office guidance + SOX 404 evolving guidance + SEC Item 1.05 interpretive guidance + Item 106 progeny + DTSA + state UTSA case-law + per-vertical regulator amendments + FDA SaMD + CDS + GMLP guidance evolution + Colorado AI Act progeny + NYC LL144 amendments + EEOC algorithmic discrimination guidance evolution + Mobley v Workday progeny + DSA implementing guidance + CCPA + state-comprehensive-privacy implementing rules + LLM provider model-version updates + LLM provider data-retention policy updates sit outside Completions control. Attorney-client privilege preservation across operator-counsel-approved rulesets.
Who owns the model monitoring stack, the model registry, the drift methodology, and the audit trail?
Operator owns every artifact. Model monitoring subscription (Arize AI + Fiddler AI + WhyLabs + Evidently AI + Aporia + Censius + Datadog ML + New Relic AI + Galileo + Truera + Mona Labs — operator chooses) runs under operator billing on operator-controlled accounts. LLM observability (LangSmith + LangFuse + Helicone + Arize Phoenix + Weights & Biases Weave + Datadog LLM Observability + Confident AI + DeepEval — operator chooses) runs under operator account. MLOps platform (MLflow + Weights & Biases + Comet + Neptune.ai + DVC + Determined AI + ClearML + Polyaxon — operator chooses) runs under operator account. Model registry (MLflow + W&B + Comet + AWS SageMaker Model Registry + Vertex AI Model Registry + Azure ML Model Registry — operator chooses) runs under operator account. Feature store (Feast + Tecton + Hopsworks + Featureform + AWS SageMaker Feature Store — operator chooses) runs under operator account. Drift detection (Evidently + Alibi Detect + River + NannyML — operator chooses) runs under operator account. Experimentation (Optimizely + GrowthBook + LaunchDarkly + Split.io + Statsig + Eppo + AB Tasty — operator chooses) runs under operator account. The operator-counsel-and-AI-governance-team-approved per-monitor drift methodology + threshold + Model Cards + Datasheets + NIST AI 100-2 + ISO/IEC TR 24028 + ISO/IEC 23053 + SOX 404 internal-controls documentation + SEC Item 1.05 materiality assessment library + Item 106 annual cybersecurity disclosure library + DTSA register + per-vertical regulator wiring library + Colorado AI Act + NYC LL144 + EEOC posture + EU AI Act Article 9-17 + 26 + 50 + 72 + Article 14 human oversight modalities documentation + per-vendor LLM zero-retention attestation chain + CCPA + GDPR + DSA records all live in operator counsel + AI-governance + CISO + CFO + controllers + audit-committee repo. The Sample + Compare + Decide + Attest skill code lives in operator code repo. The policy-as-code policies live in operator code repo, counsel-aligned. The WORM audit trail lives on operator-controlled cloud storage. Completions owns the orchestration knowledge and transfers it under the Tier 3 transition path (30-60 days at engagement end). Completions credentials revoke on engagement-end.
Engage Completions
Start with the AI Readiness Assessment (Tier 1, 2-3 weeks). Hand off to Tier 2 AI Swarm Setup Sprint (4-8 weeks). Continue under Tier 3 Fractional CMO with AI Swarm ( 6-month minimum, 1-2 days/wk embedded).