By Gwylym Owen — 40–60 min read
AethergenPlatform provides a modular pipeline for high‑fidelity synthetic data, schema design, and model training—Databricks‑ready and enterprise‑grade. Every release includes evidence bundles with signed metrics, stability bands, latency SLOs, and privacy probes. SLAs reference evidence as of September 2025.
Here’s the powerhouse setup:
schema → seeds → generation → overlays → validation → privacy → training → packaging → evidence
↘ ablations ↗
Here’s the proof you can trust:
We’ve got your back with these commitments:
Scenario: A simulated healthcare claims detector setup.
OP utility hit 0.758 [0.749,0.767]; region stability stayed ≤0.03; procurement signed off in a simulated two-week cycle. Evidence and SBOM filed with the contract—smooth sailing!
Scenario: A simulated AML graph detector trial.
Motif features boosted OP utility by +3.8% (CI +3.0,+4.6); buyers re-ran metrics in a trial workspace; listing converted after a week-long simulated pilot—proof paid off!
# 1) Register assets in Unity Catalog # 2) Load sample table; run UDF at OP # 3) Verify OP utility and stability summaries # 4) File SBOM and manifest; sign acceptance
We ship proof, not promises. With AethergenPlatform, adoption accelerates because every release is a verifiable evidence unit.
Here’s the toolkit:
sources → schema → seeds → generation → overlays → validation → privacy → training
↘ ablations ↗
packaging → catalog/marketplace → evidence → procurement
entities:
Patient: {id, age, region}
Provider: {id, specialty, region}
Claim: {id, patient_id, provider_id, date, pos, amount}
LineItem: {id, claim_id, cpt, icd10, units}
relations:
Patient 1..* Claim; Claim 1..* LineItem; Claim.provider_id → Provider.id
constraints:
amount ≥ 0; units ≥ 1; CPT in CPT_v12
claims_v3:
generator: copula+sequence
params:
amount.ln_mu: 4.1
amount.ln_sigma: 0.7
interarrival.mixexp: {lambda: [0.3,0.8], weight: [0.4,0.6]}
overlays:
upcoding: {prevalence: 0.03, factor: 1.2}
duplicate_billing: {delay_days: 7}
Spice it up with these:
Check it and measure it:
capacity: analysts_per_day: 20 cases_per_analyst: 100 budget: alerts_per_day: 2000 op: target_fpr: 0.01 threshold_sweep: [0.70, 0.76]
Controls:
Train it up:
Wrap it and ship it:
index.json ├─ metrics/utility@op.json ├─ metrics/stability_by_segment.json ├─ metrics/latency.json ├─ privacy/probes.json ├─ plots/op_tradeoffs.html ├─ plots/stability_bars.html ├─ configs/evaluation.yaml ├─ configs/thresholds.yaml ├─ sbom.json ├─ manifest.json └─ seeds/seeds.txt
{
"version": "2025.01",
"artifacts": ["metrics/utility@op.json", "plots/op_tradeoffs.html", "sbom.json"],
"hashes": {"metrics/utility@op.json": "sha256:..."},
"env": {"python": "3.11", "numpy": "1.26.4"}
}
evaluate → evidence → gates → package → publish fail-closed on any gate breach
Capability Assisted Full-Service Response 1 business day 4 hours Refresh Monthly Negotiated Dashboard fixes 24 hours 24 hours
COMMENT ON TABLE prod.ai.claims IS 'Purpose: fraud triage; OP: fpr=1%; Evidence: manifest 2025.01.'; GRANT SELECT ON TABLE prod.ai.claims TO `buyer-group`;
bundle_id: 8e7... op_utility: PASS stability: PASS latency: PASS privacy: PASS decision: APPROVE | REJECT signoff: ____________ date: ________
Keep the pulse alive:
Lock it tight:
# 1) Load sample # 2) Run UDF at OP # 3) Compute OP utility # 4) Review stability summary
Your action plan:
INC-2025-0012: stability breach in APAC → rollback to 8e7...; patch overlay; re-evaluate; promote 2025.02
Yes—air‑gapped bundles with offline dashboards and QR‑verifiable manifests.
Yes—Unity Catalog private schemas and Marketplace private listings.
Use bundled dashboards, manifests, and signatures; optionally re‑compute OP metrics with notebooks.
Tie it to the deal:
Shipping is an evidence release. AethergenPlatform makes AI delivery a governed, evidence‑first process—audit‑ready, reproducible, and rollback‑safe.