Done-for-you offer · Fractional CMO with AI Swarm · two-sigma-outlier-flagging skill · P10 INVERSION 2nd
Completions builds two-sigma outlier flagging — 🎯 17th 4-skill bundle on benchmarking-agent + 🎯 NEW Cross -entity-scope statistical detection mirror + 🎯 P10 INVERSION 2nd case + 🎯 P15 cross-agent same-mechanic -shape sub-instance
You operate 50-1,500 locations × per-metric per-entity outlier dependency. Per-metric per-entity two-sigma outlier flagging without governance fragments per-metric per-entity cohort + sigma + flag + attestation. Completions builds the two-sigma-outlier-flagging 4 -skill bundle on the benchmarking-agent end-to-end. 🎯 17th 4-skill bundle on benchmarking-agent (benchmarking -agent tier-2 4-skill bundle host). 🎯 NEW Cross-entity -scope statistical detection mirror (FIRST in catalog — same sigma-detection mechanic, inverted scope: cross -entity-at-time-snapshot vs anomaly-detection intra -entity-across-time). 🎯 P10 INVERSION 2nd case (CONFIRMED-recurring catalog-mechanic at 2 instances). 🎯 P15 cross-agent same-mechanic-shape sub-instance (P15 now 2-axis pattern: same-name + same-shape). You keep every artifact.
Published September 24, 2026
Frequently asked
What does "Completions builds two-sigma outlier flagging — 17th 4-skill bundle on benchmarking-agent + NEW Cross-entity-scope statistical detection mirror + P10 INVERSION 2nd case + P15 cross-agent same-mechanic-shape sub-instance" actually deliver?
Completions builds and operates per-metric per-entity cohort-definition + sigma-computation + outlier-flagging + attestation across the benchmarking-agent. Per-metric per-entity cohort-definition (skill 1) defines per-metric per-entity peer-cohort across 50-1,500 locations × per-metric per-entity 12+ cohort-axes (per-metric per-entity per-vertical-peer + per-metric per-entity per-region-peer + per-metric per-entity per-revenue-band-peer + per-metric per-entity per-staff-count-peer + per-metric per-entity per-location-density-peer + per-metric per-entity per-marketing-spend-peer + per-metric per-entity per-locations-count-peer + per-metric per-entity per-customer-LTV-peer + per-metric per-entity per-vertical-sub-segment-peer + per-metric per-entity per-PE-portfolio-peer + per-metric per-entity per-franchise-system-peer + per-metric per-entity per-supplier-network-peer). Per-metric per-entity sigma-computation (skill 2 — CROSS-ENTITY-SCOPE STATISTICAL DETECTION MIRROR) computes per-metric per-entity sigma-from-cohort-mean — mirroring anomaly-detection sigma-computation from #557 in TIME dimension but applying it in CROSS-ENTITY scope instead of TIME scope (P10 INVERSION 2nd case: anomaly-detection is intra-entity-across-time; benchmarking-agent is cross-entity-at-time-snapshot — same statistical-detection mechanic, inverted scope) with per-metric per-entity 1-sigma + per-metric per-entity 2-sigma + per-metric per-entity 3-sigma thresholds + per-metric per-entity directional-flag (over-performer + under-performer) + per-metric per-entity confidence-interval. Per-metric per-entity outlier-flagging (skill 3) flags per-metric per-entity outliers with per-metric per-entity outlier-classification (per-metric per-entity true-outlier + per-metric per-entity transient-outlier + per-metric per-entity cohort-misclassification-outlier + per-metric per-entity data-quality-outlier) + per-metric per-entity remediation-recommendation + per-metric per-entity per-stakeholder-routing. Per-metric per-entity attestation (skill 4) emits per-metric per-entity attestation-record with attestor-identity + attestation-timestamp + WORM-storage-attestation + chain-of-custody-record + per-vertical compliance overlay. 🎯 17th 4-skill bundle on benchmarking-agent — extends prior 16 4-skill bundles on benchmarking-agent by adding the 17th 4-skill bundle (cohort-definition + sigma-computation + outlier-flagging + attestation) on the benchmarking-agent; cumulative 4-skill bundle count on benchmarking-agent reaches 17 with this skill; 17th 4-skill bundle on benchmarking-agent establishes benchmarking-agent as a tier-2 4-skill bundle host. 🎯 NEW Cross-entity-scope statistical detection mirror — extends prior statistical-detection mechanic instances (anomaly-detection on time-scope) by adding the NEW Cross-entity-scope statistical detection mirror where the SAME statistical-detection mechanic (sigma-computation + threshold + outlier-flagging) operates in CROSS-ENTITY scope (compared across peers at time-snapshot) rather than INTRA-ENTITY scope (compared across time for single entity); the FIRST Cross-entity-scope statistical detection mirror in the catalog; marks Cross-entity-scope statistical detection as a CANONICAL design-mirror to anomaly-detection in the catalog. 🎯 P10 INVERSION 2nd case — extends prior P10 INVERSION 1st case by adding the 2ND P10 INVERSION case where a canonical mechanic INVERTS scope (intra-entity-across-time anomaly-detection → cross-entity-at-time-snapshot benchmarking-outlier-flagging); cumulative P10 INVERSION case count reaches 2 with this skill; P10 INVERSION 2nd case marks P10 INVERSION as a CONFIRMED-recurring catalog-mechanic at 2 instances. 🎯 P15 cross-agent same-mechanic-shape sub-instance — extends prior P15 cross-agent same-name instances (per #545 + #548 + #555 + #559 5-instance density) by adding the P15 cross-agent same-mechanic-shape sub-instance where the SAME mechanic-shape (sigma-computation + threshold + outlier-flagging) appears on multiple agents (anomaly-detection per #557 + benchmarking-agent per this skill) — distinct from P15 cross-agent same-name (different name, same shape); cumulative P15 cross-agent same-mechanic-shape sub-instance count reaches 1 with this skill; P15 cross-agent same-mechanic-shape sub-instance marks P15 mechanic as having an emergent 2-axis pattern in the catalog (same-name + same-shape). Per-metric per-entity compliance overlay (per-vertical statistical-methodology-disclosure + per-vertical PE/LP-investor-reporting + per-vertical SOX + per-vertical SEC-Reg-FD + per-vertical FTC-substantiation + per-vertical per-vertical-policy). Operator team owns the per-metric per-entity cohort-definition + sigma-computation + outlier-flagging + attestation registries + audit trail. Completions owns the swarm orchestration on the benchmarking-agent.
Why does in-house two-sigma outlier flagging break at multi-metric multi-entity scale?
In-house operation fails on four axes: (1) per-metric per-entity cohort-definition across 12+ cohort-axes requires cohort-engineering capacity unstaffable by internal teams; (2) per-metric per-entity sigma-computation via cross-entity-scope statistical detection mirror with 1/2/3-sigma thresholds + directional-flag + confidence-interval requires statistical-engineering capacity; (3) per-metric per-entity outlier-flagging across 4 outlier-classifications with remediation-recommendation + per-stakeholder-routing requires outlier-engineering capacity; (4) per-metric per-entity attestation with WORM-storage + chain-of-custody + 6-vertical compliance overlay requires audit-engineering capacity. 17th 4-skill bundle + NEW Cross-entity-scope statistical detection mirror + P10 INVERSION 2nd case + P15 cross-agent same-mechanic-shape sub-instance architecture coordination requires orchestration capacity at the cross-entity tier. Completions absorbs all four axes under one Tier 3 Fractional CMO with AI Swarm engagement.
What does the engagement look like across Tier 1 to Tier 2 to Tier 3?
Tier 1 AI Readiness Assessment ($10k, 2-3 weeks, diagnostic): audits four axes. Tier 2 AI Swarm Setup Sprint ($25-50k, 4-8 weeks): builds 4-skill bundle on benchmarking-agent — completing the 17th 4-skill bundle + NEW Cross-entity-scope statistical detection mirror + P10 INVERSION 2nd case + P15 cross-agent same-mechanic-shape sub-instance architecture. Tier 3 Fractional CMO with AI Swarm ($15-25k/month, 6-month minimum, 1-2 days/wk embedded): continues operating end-to-end + cross-agent swarm coordination.
Who owns the registries?
Operator owns 100% of every artifact: 4 registries (in operator data infrastructure), 4-skill bundle model code (operator-owned + operator-analytics-engineering-team-aligned), per-vertical compliance overlay (rule library in operator repo with attorney-approved updates), cohort library, statistical methodology library, LLM prompts, audit trail. Completions owns the orchestration knowledge.
What KPIs will Completions commit to on Tier 3 engagement?
Typical Tier 3 commitments: (1) per-metric per-entity cohort-definition coverage at 99-percent target across 12+ cohort-axes; (2) per-metric per-entity sigma-computation accuracy at 99-percent target with 95-percent confidence-interval; (3) per-metric per-entity outlier-flagging precision at 95-percent target across 4 outlier-classifications; (4) per-metric per-entity false-positive-flag-rate under 5-percent target; (5) per-metric per-entity attestation persistence at 100-percent target with WORM-storage.
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 back to operator in-house in 30-60 days: operating-playbook hand-off + in-house staff training + 4 registries hand-off + 4-skill bundle model code hand-off + per-vertical compliance overlay rule library hand-off + cohort library hand-off + statistical methodology library hand-off + LLM prompts hand-off + audit trail hand-off; Completions credentials revoke immediately.
Engage Completions
Start with the AI Readiness Assessment (Tier 1, 2-3 weeks, $10k). Hand off to Tier 2 ($25-50k, 4-8 weeks). Continue under Tier 3 Fractional CMO with AI Swarm ($15-25k/month, 6-month minimum, 1-2 days/wk embedded).