High-Level Architecture Analysis
High-Level Architecture analysis helps Software Engineering Teams and Regulatory Affairs Teams evaluate system boundary definitions, interface and deployment dependencies, cybersecurity exposure, and regulatory scoping readiness before design-control and architecture review decisions.
What Engineering Teams Can Decide From the Analysis
Is the system boundary clear?
Identify whether the High-Level Architecture defines subsystem boundaries, interface ownership, deployment scope, control logic, and responsible teams, so teams can confirm review scope before design review.
Where could cybersecurity risk appear?
Spot missing trust boundaries, weak interface definitions, conflicting deployment assumptions, outdated dependency references, or traceability gaps before they create validation delays, approval risk, or audit exposure.
Can teams make a clearer scoping decision?
Evaluate whether the High-Level Architecture provides enough software evidence, integration detail, and regulatory references for Regulatory Affairs, Quality Assurance, and Engineering teams to escalate with confidence.
How Teams Use This Analysis
Regulatory Affairs and Quality Assurance teams use High-Level Architecture analysis to review architecture records more consistently, catch system-boundary risk earlier, and turn diagrams into decisions about regulatory scope and validation planning.
IEC 62304 Software Lifecycle Scope Assessment
Turns scattered subsystem descriptions into structured findings, helping software teams prioritize lifecycle scope, validation needs, and approval decisions.
Device Risk Profiling
Surfaces control-boundary gaps, interoperability dependencies, and resilience assumptions, giving risk reviewers earlier visibility into system risk before the High-Level Architecture reaches regulatory review.
QMS Planning
Connects software dependencies to QMS scope, giving teams a clearer basis for change-control planning and supplier oversight decisions.
AI/ML Device Regulatory Signal Analysis
Checks whether AI module descriptions and workflow claims hold together, reducing the chance that teams rely on unsupported automation claims or weak rationale.
Intended Use Analysis
Maps device purpose and clinical workflow role into a clearer decision view, helping regulatory teams understand intended use boundaries before pathway planning.
Information Gap Analysis
Organizes boundary ambiguities and reviewer notes into a practical follow-up path, so unresolved issues can be addressed before they become submission delays.
Key High-Level Architecture Insights to Look For
Automatan organizes High-Level Architecture evaluation into structured insights that help teams judge system coverage, regulatory alignment, scoping readiness, and the quality of the evidence behind review decisions.
Document Name
Architecture title is captured to maintain version traceability and avoid scope confusion across all architecture review cycles.
Device Name
The platform name provides a consistent anchor for insights, ensuring the analysis stays linked to the correct device or software system.
Device Description
A structured summary of system function, problem addressed, and workflow role gives teams immediate context without architecture misinterpretation.
Device Purpose
Extracted purpose signals distinguish explicit commitments from implied intent, helping teams focus regulatory and lifecycle planning where it matters most.
Diagnostic/Therapeutic Decisions
Classification of diagnostic versus workflow-support roles clarifies which regulatory lens applies and directs early evidence collection.
Life Support
Flags critical-care association to highlight elevated regulatory scrutiny and prioritize architecture review efforts.
Invasiveness
Determines invasive or non-invasive status to guide device assignment and risk control expectations.
Active Device
Active versus passive designation informs the correct device framework and classification rule.
Duration of Use
Transient, short-term, or long-term use signals shape risk classification and monitoring priorities.
Biological Effect
Interaction with tissue or physiological data is identified, ensuring device functions or software features are properly scoped.
Patient Population
Adult, pediatric, or mixed-use population signals provide clarity on intended-use scope and regulatory relevance.
Anatomical Location
Automated extraction of anatomical context, such as cardiac or neurological references, informs classification and evidence requirements.
Device Class
Directional FDA class signals offer early insight for submission planning.
Device Type
Monitoring systems and clinical-support roles anchor classification reasoning and support device-type mapping.
Intended User Type
Identifies clinician, patient, or administrator users to guide usability, labeling, and training considerations.
Intended User Skill Level
Required technical expertise signals inform validation planning and usability study design.
Intended Use Environment
Deployment context, including hospital, clinic, home, or cloud environments, ensures operational risk alignment with actual use conditions.
Claims & Clinical Assertions
Automatan compiles automation, performance, interoperability, and workflow claims to highlight potential regulatory focus areas.
Regulatory Impact of Claims
Mapping claim implications guides prioritization of validation documentation and ensures evidence requirements are addressed early.
Device Characteristics
Technical and operational traits, including connectivity, automation, and data exchange, affect software lifecycle planning and validation.
Who Uses This Analysis
High-Level Architecture review pulls in several stakeholders at once. Each group needs a different cut of the same document, focused on the technical, regulatory, quality, risk, and operational questions closest to its mandate.
Regulatory Affairs leadership
Reads High-Level Architecture for pathway signals, using the analysis to decide whether early regulatory scoping needs adjustment.
Quality Assurance leadership
Reviews QMS dependencies, helping the team identify documentation gaps before design-control planning.
Software Engineering teams
Checks software lifecycle evidence and interface references, making sure the document can support validation planning.
Systems / Hardware Engineering teams
Uses the analysis to compare subsystem boundaries against design intent, giving stakeholders a clearer basis for verification scope.
Cybersecurity teams
Targets trust-boundary gaps and follow-up needs, turning the document review into a prioritized security action list.
Product Management teams
Assesses roadmap feasibility to determine whether the architecture supports planned release and regulatory complexity.
How High-Level Architecture Analysis Connects to Your Design Control Workflow
Automatan works inside the tools engineering teams already use. High-Level Architecture Documents and supporting files can be imported from common document sources and turned into structured planning intelligence without rebuilding the development process.
Google Drive
Import documents directly from Google Drive so Automatan can extract architecture signals and risk indicators from files already stored by the team.
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Google Docs
Analyze High-Level Architecture Documents stored in Google Docs without moving files, enabling seamless extraction of planning intelligence within the existing workspace.
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OneDrive
Access documents from OneDrive so teams in regulated development environments can capture interoperability signals directly from their repository.
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Dropbox
Pull architecture records from Dropbox to turn embedded interface signals, deployment assumptions, and dependency indicators into structured review intelligence inside Automatan.
Add AI IntegrationAnalyze High-Level Architecture Documents With Clearer Architecture Evidence
Engineering Teams and Regulatory Affairs Teams need more than diagrams. Automatan helps teams analyze High-Level Architecture Documents for system boundaries, validation readiness, and follow-up actions, so every review leads to clearer compliance decisions.