| A Top‑Tier Insurance Company struggled to manage its $69M technology fleet. Manual plant surveys took 7–9 weeks and delivered inconsistent results (≈75% accuracy), delaying support and weakening compliance and license governance. |
| Replace manual surveys with an automated, ML‑based “spidering” inventory engine that continuously discovers, validates, and reports on assets with high confidence at enterprise scale. |
- Designed a distributed discovery engine to automatically crawl infrastructure and endpoints.
- Applied machine learning to normalize asset signatures, remove duplicates, and flag anomalies.
- Integrated license and configuration collection into the same pipeline for unified compliance and support data.
- Delivered dashboards & reports for IT, Finance, and Compliance to act on in near real‑time.
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- Cut survey cycle time from ~8 weeks to 2 hours.
- Improved asset accuracy from ~75% to 99.92%.
- Enabled proactive license tracking, compliance validation, and smoother support handoffs.
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- Brought the $69M fleet under precise, auditable management.
- Freed thousands of staff‑hours per survey cycle; reduced audit and licensing risk.
- Improved financial control and optimized vendor spend through accurate entitlement data.
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| Transformed asset management from a reactive, manual burden into a real‑time strategic capability—strengthening compliance, reducing operational risk, and demonstrating the tangible value of automation + ML in IT operations. |