Upload your technology inventory and get a fast view of end-of-life exposure and remediation priorities.
| Technology | Version | EOL Date | Status | Est. Priority |
|---|---|---|---|---|
| Windows Server | 2012 R2 | Oct 2023 | Expired | Critical |
| Oracle Database | 12c | Nov 2024 | Expired | Critical |
| Red Hat Enterprise Linux | 7 | Jun 2025 | 90 days | High |
| VMware vSphere | 6.5 | Oct 2025 | 6 months | Medium |
| SQL Server | 2022 | Jan 2033 | Supported | — |
Illustrative output generated by the prototype workflow
The Problem
Enterprise stacks run thousands of components — each with an expiry date. When support ends, patches stop, vulnerabilities compound, and auditors take notice.
Inventories live in spreadsheets and tribal knowledge — never in one place where EOL status can be assessed across all of them at once.
Teams find out about EOL when a vendor sends a final warning — or after a CVE drops on a component that's already out of support.
Extended support contracts and emergency migrations cost significantly more than planned ones. Without visibility, these never make it into budgets.
Governance frameworks require software within supported lifecycles. EOL components create audit findings and remediation backlogs that are difficult to prioritize without data.
Workflow
EOLTracker is designed to reduce manual research and accelerate lifecycle risk identification — without replacing existing asset management platforms.
Drop in a CSV from your CMDB, ServiceNow, SCCM, or a manual spreadsheet. The normalization engine handles messy vendor names, abbreviations, and version variations — "MSSQL 2019" and "Microsoft SQL Server 2019" resolve to the same canonical entry. Low-confidence matches are flagged for review rather than silently assumed.
A hybrid pipeline uses deterministic matching first — a curated alias table covering hundreds of common enterprise technologies — with an LLM fallback for edge cases and ambiguous entries. Ambiguous matches are escalated for manual validation rather than forced through. Results are cross-referenced against vendor lifecycle data.
See what's expired, what's expiring soon, and a prioritised action list structured for governance and remediation tracking. Outputs are designed to support audit preparation workflows — not as formal compliance deliverables.
Example Use Cases
EOLTracker is most useful in early-stage risk identification and prioritisation workflows.
Capabilities
Hundreds of vendor aliases built-in, LLM fallback for edge cases. Designed to support iterative improvement of alias mappings over time.
EOL findings scored by severity and proximity to end-of-support date — structured to support planning conversations.
Microsoft, Oracle, Red Hat, VMware, Apache, IBM, Cisco, SAP and more — matched against vendor lifecycle data.
Works with exports from any asset management tool. No template or pre-processing required.
Low-confidence matches are flagged for review. Ambiguous entries are escalated rather than silently resolved.
Outputs structured for governance and remediation tracking workflows, useful for compliance review preparation.
Why I Built This
Enterprise teams often lack clear visibility into software lifecycle exposure. EOL status is scattered across vendor documentation, CMDB records, and manual tracking — making it hard to get a consolidated view without significant research effort.
I built EOLTracker as a working prototype to explore how AI-assisted normalization and lifecycle signal interpretation can reduce that manual overhead and accelerate early risk identification. The concept draws on practical experience in enterprise architecture and technology modernisation planning.
The goal is not to replace asset management platforms, but to provide a faster first-pass view — the kind of signal that helps teams prioritise before committing to deeper investigation.
Current Status
Prototype currently processes test inventories and generates first-pass lifecycle risk signals. Focused on normalization accuracy and lifecycle signal interpretation.
Exploring patterns for scalable enterprise use — including feedback-driven normalization improvements and confidence scoring.
Expanding vendor coverage, refining the matching pipeline, and exploring integration patterns with existing CMDB workflows.
An independent working prototype exploring enterprise architecture and AI-assisted lifecycle analysis.
info@eoltracker.com · eoltracker.com