Illustrative scenarios — built from published industry benchmarks — showing the problems this research investigates and the capabilities the prototype is designed to address. These are models, not customer engagements.
Organizations of this scale typically run hundreds or thousands of automations across multiple platforms with no complete inventory. Compliance audits become time-consuming and risky.
The prototype is designed to discover and catalog automations across connected platforms and produce audit-ready reports on demand.
Automation and AI spend tends to grow without per-team attribution. Teams suspect duplicates and unused workflows but have no way to identify them at scale.
The prototype is designed to detect duplicates and near-duplicates across platforms and produce modeled consolidation savings.
Maintaining continuous compliance across GDPR, HIPAA, and SOX is complex. Point-in-time, manual monitoring is error-prone and lags real-world changes.
The prototype is designed to evaluate each automation against policy-as-code rules continuously, with workflows for human review of surfaced findings.
Automations can fail silently or degrade gradually, with issues discovered only after downstream impact. There is rarely proactive monitoring at the automation layer.
The prototype is designed to score automation health and surface early-warning signals on likely failures.
Organizations managing automations across multiple business units or tenants need tenant isolation, branding, and centralized oversight.
The prototype is designed with tenant isolation in its data model so that multi-unit governance can be researched without commingling.
Teams want to apply AI to analyze patterns, relationships, and lifecycles across their automation catalog, but lack the data and tooling to do so.
The prototype includes AI-driven analyses (relationship graphs, lifecycle profiling, anomaly detection) designed to operate over the catalog the prototype builds.
The scenarios above are drawn from public benchmarks in these regulated and large-scale environments.
SOX, Basel III, MiFID II — high audit cost and compliance complexity
HIPAA, patient-data governance, clinical workflow oversight
Multi-platform sprawl, AI workload spend, agentic systems
Process automation governance and multi-site oversight
Workflow proliferation across teams and platforms
Compliance, security, and multi-tenant oversight
Reach out to discuss whether any of these scenarios align with your own evaluation interests. Beta access is non-commercial.