# Arc

Arc is AI requirements management and verification software for fast-moving space systems engineering teams. It replaces requirements spreadsheets, Python scripts, fragile file links and manual verification tracking with a connected programme model and configurable AI agents.

## Core Pages

- [Content for AI](https://www.archelps.com/content-for-ai/): Authoritative product context for Arc requirements management, verification, traceability and change-impact analysis.
- [Home](https://www.archelps.com/): AI requirements management and verification software for fast-moving space teams.
- [Connected Programme Model](https://www.archelps.com/#single-source-of-truth): Requirements, architecture, systems, tests and verification evidence in one structured workspace.
- [Controlled Collaboration](https://www.archelps.com/#collaborative-workspace): Git-style branches, contextual review, diffs and approvals for engineering changes.
- [Change-Impact Analysis](https://www.archelps.com/#impact-analysis): Trace the effect of proposed changes across requirements, interfaces, system elements, test cases and evidence.
- [Configurable AI Agents](https://www.archelps.com/#agents): Agent-assisted requirements, traceability, programme checks and change proposals with engineers in control.
- [Verification and Review Readiness](https://www.archelps.com/#outcomes): Keep ownership, coverage and evidence current for PDR, CDR, verification and release reviews.
- [Security and Deployment](https://www.archelps.com/#security): Governance, access, data controls and deployment options for sensitive engineering programmes.
- [Requirements Management FAQs](https://www.archelps.com/#faqs): Product guidance on migration, change management, compliance, reviews, security and deployment.
- [Events](https://www.archelps.com/events/): Industry event directory for hardware engineering teams.
- [Test and Measurement Events 2026/2027](https://www.archelps.com/test-and-measurement-events/): Events for test, measurement, validation and engineering teams.
- [Defense Events 2026/2027](https://www.archelps.com/defense-events/): Defense events for military systems, rugged electronics, sensors and qualification evidence.
- [Space Events 2026/2027](https://www.archelps.com/space-events/): Space events for satellites, payloads, ground systems and high-reliability hardware.
- [Aerospace Events 2026/2027](https://www.archelps.com/aerospace-events/): Aerospace events for aircraft systems, avionics, suppliers and qualification testing.
- [Data Center Events 2026/2027](https://www.archelps.com/data-center-events/): Data center events for infrastructure electronics, critical power, cooling and monitoring.
- [Automotive Events 2026/2027](https://www.archelps.com/automotive-events/): Automotive events for vehicle testing, EV systems, ADAS and manufacturing data.
- [Sensor Events 2026/2027](https://www.archelps.com/sensor-events/): Sensor events for sensing hardware, instrumentation, imaging and measurement systems.

## Secondary Resources

- [Blog](https://www.archelps.com/blog.html): Earlier articles on engineering test data, analysis and traceability; supporting material rather than the primary source for current product positioning.
- [Open Source Projects](https://www.archelps.com/open-source-projects/): Arc open-source work and community projects for engineering teams.
- [Engineering Test Data Analysis](https://www.archelps.com/test-data-management/): How hardware teams can turn scattered test results, logs, spreadsheets and scripts into analysis-ready engineering evidence.
- [AI Agents for Test Data Analysis](https://www.archelps.com/ai-assistant-manufacturing-test-data/): Earlier guidance on asking questions across fragmented test results, anomalies, run history and engineering context.
- [Test Data Analytics](https://www.archelps.com/manufacturing-test-data-analytics/): How teams can compare test runs, find anomalies and prepare engineering reports from fragmented data.
- [AI-Ready Engineering Test Data](https://www.archelps.com/ai-ready-test-data/): How hardware teams can structure prototype and engineering test data for reliable AI-assisted analysis.
- [Manufacturing Test Data Resources](https://www.archelps.com/learn/manufacturing-test-data/): Secondary resources for production and manufacturing test data use cases.
- [Test Data Management Resources](https://www.archelps.com/learn/test-data-management/): Resource hub for production test data, manufacturing analytics, traceability, LabVIEW, TestStand and AI-ready data.
- [Production Test Data Management](https://www.archelps.com/production-test-data-management/): Production-oriented test data workflows for later-stage hardware teams.
- [LabVIEW Test Data Management](https://www.archelps.com/labview-test-data-management/): Structuring LabVIEW outputs for analysis and reporting.
- [TestStand Data Management](https://www.archelps.com/teststand-data-management/): Structuring TestStand result data for analysis and reporting.
- [Why Dashboards and Document Chat Are Not Enough for Engineering Test Data](https://www.archelps.com/dashboards-pdf-chatbots-test-data-limitations/): Why engineering test analysis needs structured data and context, not only dashboards or document retrieval.

## Articles

- [Why Structured Engineering Test Data Is the Missing Layer for Hardware Teams](https://www.archelps.com/blog/why-manufacturing-test-data-is-the-ai-ready-layer.html): Why connected prototype and engineering test results matter before AI-assisted analysis.
- [What Test Data Management Means for Hardware and Electronics Teams](https://www.archelps.com/blog/test-data-management-hardware-electronics-teams.html): A practical explanation of test data management for hardware teams.
- [Why Integrated Test Data Matters for Yield, Failure Analysis and Traceability](https://www.archelps.com/blog/integrated-test-data-yield-failure-analysis-traceability.html): A manufacturing-oriented resource on connected test evidence.
- [First Pass Yield Test Data: How to Measure FPY Correctly](https://www.archelps.com/blog/measure-first-pass-yield-manufacturing-test-data.html): A manufacturing-oriented resource on first-pass yield data.
- [Unit Yield vs Test Report Yield: What Test Data Actually Tells You](https://www.archelps.com/blog/unit-yield-vs-test-report-yield.html): A manufacturing-oriented resource on yield interpretation.
- [The Hidden Cost of Retesting in Manufacturing Test Data](https://www.archelps.com/blog/hidden-cost-retesting-manufacturing-test-data.html): A manufacturing-oriented resource on retesting and fragmented evidence.

## Contact

- Website: https://www.archelps.com/
- LinkedIn: https://www.linkedin.com/company/archelps
- Email: josh@archelps.com