By Gwylym Owen — 18–24 min read
Enterprises can adopt AI faster when claims are backed by signed, reproducible metrics. AethergenPlatform transforms evaluations into evidence bundles—featuring operating-point utility with confidence intervals, segment stability, latency service level objectives (SLOs), and privacy probes. This approach enables procurement to sign contracts in days, not months, as of September 2025.
In enterprise settings, trust is the bottleneck. Traditional AI pitches rely on high-level metrics like AUC, but decision-makers need more. AethergenPlatform addresses this with evidence that speaks to specific needs:
Every evidence bundle from AethergenPlatform includes cryptographically signed components, ensuring integrity and verifiability:
Operating points (OPs) are the thresholds where your teams operate—e.g., 2,000 alerts/day or a 1% false-positive rate. AethergenPlatform can collaborate with your teams to define these, publishing effect sizes (e.g., +5% detection lift) and confidence intervals around each OP. Thresholds are stored in config tables (e.g., `thresholds.yaml`) rather than hard-coded, allowing flexible updates without redeployment.
Stability across diverse conditions is critical for enterprise trust. We compute deltas between segment-specific KPIs (e.g., by region, product, or lifecycle) and the global KPI, reporting these with confidence intervals. Promotion to production fails if stability gates (e.g., max delta < 3%) are breached, safeguarding performance consistency as of September 2025.
Latency: We provide p50, p95, and p99 latency distributions to ensure models meet operational SLOs, critical for real-time applications. For example, a p95 latency of 120ms ensures 95% of inferences stay within that bound.
Privacy: Privacy probes test for leakage via membership-inference and attribute-disclosure attacks, reporting results with confidence intervals. Optional DP budgets (e.g., ε=2.0, δ=1e-6) can be included, with expected utility impacts (e.g., -1% ± 0.5%) disclosed for transparency.
AethergenPlatform automates evidence generation via a CI/CD pipeline (e.g., GitHub Actions), ensuring consistency and auditability:
{
"version": "2025.01",
"artifacts": {
"metrics": ["metrics/utility@op.json", "metrics/stability_by_segment.json", "metrics/latency.json"],
"plots": ["plots/op_tradeoffs.html", "plots/stability_bars.html"],
"configs": ["configs/evaluation.yaml", "configs/thresholds.yaml"],
"sbom": "sbom.json",
"privacy": ["privacy/probes.json"]
},
"hashes": {
"metrics/utility@op.json": "sha256:abc123...",
"metrics/stability_by_segment.json": "sha256:def456..."
},
"seeds": "seeds/seeds.txt",
"signature": "sig:xyz789..."
}
Scenario: A financial institution needed a fraud detector for transaction monitoring.
Scenario: A healthcare provider evaluated a diagnostic model.
Evidence bundles are tied to a robust governance framework:
Signing leverages `KeyManagementService` in CI:
No—AUC is a summary metric, but teams operate at fixed budgets (e.g., alerts/day). We prove utility at your OP with CIs.
Use the `manifest.json` and `signature.json` to check hashes; HTML/PDF dashboards allow offline review and re-computation with provided seeds.
Yes—work with us to define thresholds, and we’ll generate tailored evidence bundles.
Signed metrics de-risk AI adoption by providing proof that procurement and risk teams can trust. AethergenPlatform delivers evidence bundles with every release, enabling faster “yes” decisions and smoother enterprise integration as of September 2025.