Done-for-you offer · Fractional CMO with AI Swarm · jsonld-generation-from-master-record skill
Completions builds the per-location JSON-LD auto-generation — closed-loop Diagnose+Prescribe pair with loop 45 on schema -graph-orchestration
You operate 50-1,500 locations × 10,000-1,000,000 pages × 17+ Schema.org class permutations × per-location per-page per-schema per-field generation. Per-page JSON-LD auto -generation without governance produces stale schema + missed rich-result eligibility + cross-page master-record drift. Your SEO team demands per-schema per-field Diagnose -Prescribe decisioning. Your CMO demands per-page rich -result capture proof. Your counsel demands HIPAA + FDA + DEA + Metrc + DISCUS + state-licensing-board + FTC + Lanham + ADA + state-AG compliance on every JSON-LD block. Completions builds the jsonld-generation-from-master-record skill on the schema-graph-orchestration agent end-to-end with per-location per-page per-schema-class JSON-LD generation + per-schema per-field Diagnose-Prescribe decisioning + per-schema per-field master-record sourcing + per-schema per-field closed-loop feedback to rich-result eligibility. This is a closed-loop Diagnose+Prescribe pair with loop 45 — eligibility-scoring diagnoses + JSON-LD generation prescribes + post-publish capture verification refines future diagnoses. You keep every artifact. You keep the master-record registry + schema-generation model + per -schema-class library + Diagnose-Prescribe config. You keep the ability to in-house at any time.
Published September 24, 2026
What we operate every publish + every day
Per-location per-page per-schema-class JSON-LD generation across 50-1,500 locations × 10,000-1,000,000 pages × 17+ Schema.org class permutations (LocalBusiness + Restaurant + LodgingBusiness + AutomotiveBusiness + MedicalBusiness + HealthAndBeautyBusiness + FinancialService + LegalService + Service + Product + ProductGroup + Offer + AggregateOffer + Review + AggregateRating + FAQPage + QAPage + HowTo + Article + NewsArticle + BlogPosting + Event + Course + JobPosting + Recipe + VideoObject + ImageObject + Person + Organization + BreadcrumbList + SiteNavigationElement + WebSite + WebPage + WebApplication + SoftwareApplication + AboutPage + ContactPage) with per-schema per-field generation + validation + cross-page consistency + per -Schema.org-vocabulary + per-Google + per-Bing + per -DuckDuckGo + per-AI-Overview + per-AI-Mode + per -Perspectives eligibility compliance.
Per-schema per-field Diagnose-Prescribe decisioning emits Diagnose-mode per-field problem-identification (missing -field + invalid-field + deprecated-field + cross-page -drift + vocabulary-violation + eligibility-violation + entity-mismatch + cross-source-conflict) + Prescribe-mode per-field remediation (auto-generate + auto-correct + auto-replace + auto-merge + auto-deprecate + queue-for -human-review + escalate-to-counsel + reject) with per -field impact-projection + confidence-tier + explainability + per-rule-citation.
Per-schema per-field master-record sourcing across 14+ master-record sources (per-location master-NAP-record (loop 6) + per-location attribute-registry + per-location service -area-polygon + per-location entrance-coordinates + per -location hours + per-location amenities + per-location services + per-product + per-event + per-job + per-course + per-recipe + per-review + per-person + per-organization master-records) with per-source canonical-resolution + conflict-resolution + freshness-validation + per-field versioning.
Per-schema per-field closed-loop feedback to rich-result eligibility (loop 45) emits per-page per-schema per-field post-generation actual-rich-result-capture + actual -traffic-delta + actual-CTR-delta + actual-conversion -delta + actual-revenue-delta into the rich-result eligibility scoring model with per-field Bayesian-updating + counterfactual-validation + propensity-score-matching + experimental-design treatment-assignment-protocol.
Closed-loop Diagnose+Prescribe pair architecture coordinates loops 45+56 on schema-graph-orchestration agent. Per -vertical compliance overlay (HIPAA + FDA + DEA + Metrc + DISCUS + FDA tobacco + state-licensing-board + FTC + Lanham + ADA + state-AG).
Why in-house breaks at multi-location page scale
Per-location per-page per-schema-class JSON-LD generation across 10,000-1,000,000 pages × 17+ Schema.org class permutations × per-field generation + validation + cross -page consistency requires production schema-generation infrastructure. Per-schema per-field Diagnose-Prescribe decisioning across 8 × 8 combinations requires production decisioning infrastructure. Per-schema per-field master -record sourcing across 14+ sources requires data -engineering capacity. Per-schema per-field closed-loop feedback to rich-result eligibility requires data-science capacity with closed-feedback-loop expertise. Closed-loop Diagnose+Prescribe pair architecture coordination requires orchestration capacity. Per-vertical compliance overlay covering 11+ regulatory frameworks requires legal -engineering capacity. Per-Schema.org-vocabulary + per -search-engine + per-AI-surface eligibility-criterion maintenance requires production maintenance capacity.
Completions absorbs all seven axes under one Tier 3 Fractional CMO with AI Swarm engagement.
How the engagement progresses
Tier 1 AI Readiness Assessment ($10k, 2-3 weeks, diagnostic). Audits the current operation across seven axes. Deliverable: gap-pack report with per-location schema-coverage estimate + per-page rich-result-eligibility -exposure estimate.
Tier 2 AI Swarm Setup Sprint ($25-50k, 4-8 weeks, build with 30-day operating tail). Builds the per -location JSON-LD auto-generation closed feedback loop on operator infrastructure — jsonld-generation-from-master -record + per-location-per-page-per-schema-class-jsonld -generation + per-schema-per-field-diagnose-prescribe -decisioning + per-schema-per-field-master-record-sourcing + per-schema-per-field-closed-loop-feedback-to-rich-result -eligibility on schema-graph-orchestration agent + rich -result-eligibility-scoring + schema-validation + schema -auto-remediation + per-vertical-catalog-schema-validation + master-record-canonicalization.
Tier 3 Fractional CMO with AI Swarm ($15-25k/ month, 6-month minimum, 1-2 days/wk embedded). Continues operating the loop with continuous per-location per-page per-schema-class JSON-LD generation + weekly per-schema per-field Diagnose-Prescribe decisioning refresh + monthly per-schema per-field master-record sourcing refresh + per -event per-schema per-field closed-loop feedback to rich -result eligibility + monthly per-Schema.org-vocabulary + per-search-engine + per-AI-surface eligibility-criterion refresh + cross-agent swarm coordination.
Frequently asked
What does "Completions builds per-location JSON-LD auto-generation — closed-loop Diagnose+Prescribe pair with loop 45 on schema-graph-orchestration" actually deliver?
Completions builds and operates per-location per-page per-schema-class JSON-LD generation + per-schema per-field Diagnose-Prescribe decisioning + per-schema per-field master-record sourcing + per-schema per-field closed-loop feedback to rich-result eligibility across the operator multi-location page surface. Per-location per-page per-schema-class JSON-LD generation across 50-1,500 locations × 10,000-1,000,000 pages × 17+ Schema.org class permutations — LocalBusiness + Restaurant + LodgingBusiness + AutomotiveBusiness + MedicalBusiness + HealthAndBeautyBusiness + FinancialService + LegalService + Service + Product + ProductGroup + Offer + AggregateOffer + Review + AggregateRating + FAQPage + QAPage + HowTo + Article + NewsArticle + BlogPosting + Event + Course + JobPosting + Recipe + VideoObject + ImageObject + Person + Organization + BreadcrumbList + SiteNavigationElement + WebSite + WebPage + WebApplication + SoftwareApplication + BreadcrumbList + AboutPage + ContactPage with per-schema per-field generation + per-schema per-field validation + per-schema per-field cross-page consistency + per-schema per-field per-Schema.org-vocabulary compliance + per-schema per-field per-Google-eligibility-criterion compliance + per-schema per-field per-Bing-eligibility-criterion compliance + per-schema per-field per-DuckDuckGo-eligibility-criterion compliance + per-schema per-field per-AI-Overview-eligibility compliance + per-schema per-field per-AI-Mode-eligibility compliance + per-schema per-field per-Perspectives-eligibility compliance. Per-schema per-field Diagnose-Prescribe decisioning emits Diagnose-mode per-field problem-identification (missing-field + invalid-field + deprecated-field + cross-page-drift + vocabulary-violation + eligibility-violation + entity-mismatch + cross-source-conflict) + Prescribe-mode per-field remediation (auto-generate + auto-correct + auto-replace + auto-merge + auto-deprecate + queue-for-human-review + escalate-to-counsel + reject) with per-field impact-projection + per-field confidence-tier + per-field explainability + per-field per-rule-citation. Per-schema per-field master-record sourcing across per-location master-NAP-record (loop 6 master-record-canonicalization) + per-location attribute-registry + per-location service-area-polygon + per-location entrance-coordinates + per-location hours + per-location amenities + per-location services + per-product master-record + per-event master-record + per-job master-record + per-course master-record + per-recipe master-record + per-review master-record + per-person master-record + per-organization master-record with per-source canonical-resolution + per-source conflict-resolution + per-source freshness-validation + per-source per-field versioning. Per-schema per-field closed-loop feedback to rich-result eligibility (loop 45) emits per-page per-schema per-field post-generation actual-rich-result-capture + actual-traffic-delta + actual-CTR-delta + actual-conversion-delta + actual-revenue-delta into the rich-result eligibility scoring model with per-field Bayesian-updating + counterfactual-validation + propensity-score-matching + experimental-design treatment-assignment-protocol. Closed-loop Diagnose+Prescribe pair architecture coordinates loop 45 (rich-result-eligibility-scoring) + loop 56 (jsonld-generation-from-master-record) on schema-graph-orchestration agent — eligibility-scoring diagnoses what pages need what rich-result eligibility + JSON-LD generation prescribes the corrected schema + post-publish rich-result capture verification feeds back to refine future diagnoses. Per-vertical compliance overlay (HIPAA healthcare-schema + FDA OPDP pharma-schema + DEA controlled-substance-schema + Metrc cannabis-schema + DISCUS alcohol-schema + FDA tobacco-schema + state-licensing-board service-schema + FTC Section 5 + Lanham Act + ADA Title III schema-accessibility + state-AG misrepresentation). Operator team owns the master-record registry + schema-generation model + per-schema-class library + Diagnose-Prescribe config + closed-loop feedback config + audit trail. Completions owns the swarm orchestration on the schema-graph-orchestration agent.
Why does in-house per-location JSON-LD auto-generation break at multi-location page scale?
In-house operation at multi-location page scale fails on seven axes: (1) per-location per-page per-schema-class JSON-LD generation across 50-1,500 locations × 10,000-1,000,000 pages × 17+ Schema.org class permutations × per-schema per-field generation + validation + cross-page consistency + per-vocabulary compliance + per-search-engine eligibility compliance requires production schema-generation infrastructure unstaffable by internal teams; (2) per-schema per-field Diagnose-Prescribe decisioning across 8 Diagnose-mode problem-identifications × 8 Prescribe-mode remediations × per-field impact-projection + confidence-tier + explainability + per-rule-citation requires production decisioning infrastructure; (3) per-schema per-field master-record sourcing across 14+ master-record sources × per-source canonical-resolution + conflict-resolution + freshness-validation + per-field versioning requires data-engineering capacity with master-data-management expertise; (4) per-schema per-field closed-loop feedback to rich-result eligibility with per-field Bayesian-updating + counterfactual-validation + propensity-score-matching + experimental-design treatment-assignment-protocol requires data-science capacity with closed-feedback-loop expertise; (5) closed-loop Diagnose+Prescribe pair architecture coordination across loops 45+56 requires orchestration capacity; (6) per-vertical compliance overlay covering HIPAA + FDA OPDP + DEA + Metrc + DISCUS + FDA tobacco + state-licensing-board + FTC + Lanham + ADA + state-AG requires legal-engineering capacity; (7) per-Schema.org-vocabulary + per-search-engine + per-AI-surface eligibility-criterion maintenance (Google + Bing + DuckDuckGo + Yandex + AI-Overview + AI-Mode + Perspectives + Discussions-and-Forums update eligibility criteria every 3-6 weeks) requires production maintenance capacity. Completions absorbs all seven axes under one Tier 3 Fractional CMO with AI Swarm engagement.
What does the engagement look like across Tier 1 → Tier 2 → Tier 3?
Tier 1 AI Readiness Assessment ($10k, 2-3 weeks, diagnostic): Completions audits the operator current JSON-LD auto-generation operation across seven axes — per-location per-page per-schema-class JSON-LD generation coverage + per-schema per-field Diagnose-Prescribe decisioning maturity + per-schema per-field master-record sourcing + per-schema per-field closed-loop feedback to rich-result eligibility + closed-loop Diagnose+Prescribe pair architecture coordination + per-vertical compliance overlay + per-Schema.org-vocabulary + per-search-engine + per-AI-surface eligibility-criterion maintenance. Deliverable: gap-pack report with per-location schema-coverage estimate + per-page rich-result-eligibility-exposure estimate. Tier 2 AI Swarm Setup Sprint ($25-50k, 4-8 weeks, build with 30-day operating tail): Completions builds the per-location JSON-LD auto-generation closed feedback loop on operator infrastructure — jsonld-generation-from-master-record + per-location-per-page-per-schema-class-jsonld-generation + per-schema-per-field-diagnose-prescribe-decisioning + per-schema-per-field-master-record-sourcing + per-schema-per-field-closed-loop-feedback-to-rich-result-eligibility on schema-graph-orchestration agent + rich-result-eligibility-scoring on schema-graph-orchestration + schema-validation + schema-auto-remediation + per-vertical-catalog-schema-validation + master-record-canonicalization on master-record. Tier 3 Fractional CMO with AI Swarm ($15-25k/month, 6-month minimum, 1-2 days/wk embedded): Completions continues operating the loop with continuous per-location per-page per-schema-class JSON-LD generation + weekly per-schema per-field Diagnose-Prescribe decisioning refresh + monthly per-schema per-field master-record sourcing refresh + per-event per-schema per-field closed-loop feedback to rich-result eligibility + monthly per-Schema.org-vocabulary + per-search-engine + per-AI-surface eligibility-criterion refresh + cross-agent swarm coordination.
Who owns the master-record registry, schema-generation model, per-schema-class library, and audit trail?
Operator owns 100% of every artifact: master-record registry (in operator data infrastructure — Snowflake + Databricks + BigQuery + Redshift + Postgres operator data warehouse), per-schema-class library (in operator repo with operator-controlled per-schema-class per-field versioning + per-Schema.org-vocabulary + per-search-engine + per-AI-surface eligibility-criterion versioning), per-schema per-field generation model code (in operator repo with operator-controlled deploy pipeline + operator-data-science-team-aligned), per-schema per-field Diagnose-Prescribe decisioning config (operator-owned + operator-SEO-team-aligned + operator-counsel-aligned), per-schema per-field master-record sourcing model code (operator-owned + operator-data-engineering-team-aligned), per-schema per-field closed-loop feedback model code (operator-owned + operator-data-science-team-aligned), per-vertical compliance overlay (rule library in operator repo with attorney-approved updates), HIPAA + FDA OPDP + DEA + Metrc + DISCUS + FDA tobacco + state-licensing-board + FTC + Lanham + ADA + state-AG disclosure register (operator-owned + operator-counsel-maintained), brand spec (versioned in operator repo), LLM prompts (in operator repo), audit trail (retention infrastructure on operator cloud account). Completions owns: the orchestration knowledge — how to design per-location per-page per-schema-class JSON-LD generation contracts + how to tune per-schema per-field Diagnose-Prescribe decisioning + how to debug per-schema per-field master-record sourcing cascades + how to coordinate the closed-loop Diagnose+Prescribe pair architecture with rich-result-eligibility-scoring + schema-validation + schema-auto-remediation + per-vertical-catalog-schema-validation siblings on schema-graph-orchestration agent. The operator can in-house at any time; Completions credentials revoke immediately on engagement-end.
What KPIs will Completions commit to on Tier 3 engagement?
Typical Tier 3 commitments: (1) per-location per-page per-schema-class JSON-LD generation coverage at 99-percent target across 17+ Schema.org class permutations; (2) per-schema per-field generation accuracy at 99-percent target measured against per-Schema.org-vocabulary + per-Google-eligibility + per-Bing-eligibility golden set; (3) per-schema per-field Diagnose-Prescribe decisioning accuracy at 90-percent target across 8 Diagnose-mode × 8 Prescribe-mode combinations; (4) per-schema per-field master-record sourcing accuracy at 99-percent target across 14+ master-record sources; (5) per-schema per-field closed-loop feedback to rich-result eligibility integration latency under 24-hour end-to-end; (6) closed-loop Diagnose+Prescribe pair architecture coordination latency under 2-second end-to-end; (7) per-vertical compliance overlay coverage at 99.9-percent target across 11+ regulatory frameworks; (8) per-Schema.org-vocabulary + per-search-engine + per-AI-surface eligibility-criterion refresh cadence adherence at 100-percent target (monthly); (9) per-location rich-result capture-rate improvement at 50-100-percent target (operator-baseline-dependent); (10) per-location rich-result-driven traffic-lift at 20-50-percent target (operator-baseline-dependent). Each KPI measured against pre-engagement baseline.
How does engagement end and what is the operator transition path?
Tier 3 engagements are 6-month minimum with 90-day notice. At engagement end, Completions transitions the per-location JSON-LD auto-generation closed-feedback-loop operation back to operator in-house in 30-60 days: operating-playbook hand-off + in-house staff training across 3-5 operator team members covering per-location per-page per-schema-class JSON-LD generation + per-schema per-field Diagnose-Prescribe decisioning + per-schema per-field master-record sourcing + per-schema per-field closed-loop feedback to rich-result eligibility + closed-loop Diagnose+Prescribe pair architecture coordination + per-vertical compliance overlay management + per-Schema.org-vocabulary + per-search-engine + per-AI-surface eligibility-criterion maintenance + cross-agent coordination + master-record registry infrastructure hand-off + per-schema-class library hand-off + per-schema per-field generation model code hand-off + per-schema per-field Diagnose-Prescribe decisioning config hand-off + per-schema per-field master-record sourcing model code hand-off + per-schema per-field closed-loop feedback model code hand-off + LLM prompts hand-off + audit trail hand-off; Completions credentials revoke immediately on engagement-end. Operator can re-engage Completions at any time on Tier 1 or Tier 2 cadence.
Engage Completions
Start with the AI Readiness Assessment (Tier 1, 2-3 weeks, $10k). Hand off to Tier 2 ($25-50k, 4-8 weeks) for the build. Continue under Tier 3 Fractional CMO with AI Swarm ($15-25k/month, 6-month minimum, 1-2 days/wk embedded). Operator owns every artifact at every tier. Operator can in-house at any time.