Which release did this evidence support?
Show the exact model, data, eval run, approval, and monitoring plan behind a customer-facing claim.
Ormedian turns high-stakes AI releases into living evidence trails: release contracts, evaluation gates, monitoring signals, provenance, and Assurance Packs.
View the open-source scaffold on GitHub →A model release moves through evidence gates and monitoring into a living Assurance Pack.
Release evidence trail
Release contract
Evaluation gate
Risk control
Monitoring
claims-routing-v4.1
Intended use Complete
Evaluation Passed
Risk register Controlled
Monitoring Live
Metrics, risk notes, logs, approvals, and incident context usually exist. They just do not travel together as one release record. Ormedian makes the release itself the organizing unit.
Show the exact model, data, eval run, approval, and monitoring plan behind a customer-facing claim.
Trace the release diff, risk owner, and control that was in force when live behavior shifted.
Turn scattered notebooks, dashboards, documents, and chat threads into one defensible artifact.
Assurance Packs
An Assurance Pack is the destination of the release evidence trail: compact enough to inspect, complete enough to defend.
Intended use
Route enterprise support claims to the right specialist queue with human review on uncertain or policy-sensitive cases.
Release gates
Manual review required for claims above the financial-risk threshold.
Intended use, scope, assumptions, exclusions, and policy constraints
Evaluation results with run IDs, baselines, thresholds, and failure notes
Risk register entries with owners, controls, residual risk, and review cadence
Monitoring thresholds, alert rules, incidents, and escalation paths
Provenance linking data, model, policy, reviewer, and release decision
Approval history that stays coupled to the deployed version
Workflow
Ormedian turns review gates into concrete outputs, so the pack is assembled while work happens instead of after everyone has moved on.
Capture intended use, exclusions, success metrics, evaluation criteria, and policy constraints before review.
Attach run IDs, baselines, thresholds, failures, and deltas from the evaluation suite.
Connect production signals, drift thresholds, incident notes, policy violations, and alert ownership.
Publish the Assurance Pack as the current record for the deployed version and release decision.
Define intended use
Run evaluation suite
Review residual risk
Attach monitoring plan
Bundle release evidence
After deployment
Monitoring signals, provenance edges, incidents, and approvals remain coupled to the deployed version instead of drifting into separate operational tools.
Signal trail
Evidence updates
Financial advice classifier exceeded review threshold.
New market segment observed in production traffic.
Monitoring evidence linked to the current release record.
Provenance graph connecting Eval Run, Dataset, Model, Policy, Reviewer, and Decision with a fixed release evidence topology.
Review moments
Package intended use, evaluation coverage, residual risks, monitoring plans, and approvals before production.
Respond with a release-specific assurance record instead of scattered screenshots and ad hoc documents.
Trace what changed, which thresholds fired, who reviewed it, and which control applied.
Give engineering, product, risk, and compliance the same evidence record before sign-off.
Show how the AI system is evaluated, monitored, governed, and kept under control.
Keep controls, owners, assumptions, exclusions, and review cadence explicit.
Evidence tied to deployed versions
Reproducible evaluation references
Risk controls with named owners
Monitoring plans that stay current
Early access
Join the waitlist for a sample Assurance Pack, beta access, and practical templates for evaluation, monitoring, provenance, and release governance.