- Manual reporting can be completely replaced using Nvidia GB10 and structured AI workflows.
- Automation reduces reliance on additional staff while maintaining consistent reporting accuracy.
- Sequential workflows simplify testing and troubleshooting before scaling automation to the enterprise level.
Many organizations rely on employees to manually collect, organize, and report performance metrics from multiple digital platforms.
A recent Serve at home (STH) replaced part of this manual reporting process using local AI systems built around Nvidia GB10 hardware.
The work involved repetitive requests received through long, unstructured emails, often requesting metrics from multiple sources and specific date ranges.
Reduce the need for additional staff
Instead of hiring additional staff to manage this increasing volume, SOMETHING focused on designing an automated reporting pipeline that could handle these tasks reliably.
The automation followed a structured flow to collect and aggregate data from all relevant platforms.
Pre-built integrations within n8n reduced setup time by connecting directly to analytics systems without the need for custom code.
Planning each step ensured that time limits, filters, and query details were applied consistently.
Although the workflow was executed sequentially, this approach simplified testing and troubleshooting during initial deployment, allowing the reviewer to verify results before escalating.
To validate the system, the review used approximately 1,000 historical applications from 2015 to 2025 with known results.
Different AI models, including gpt-oss-20b FP8 and gpt-oss-120b FP8, were compared to evaluate step accuracy.
Initial tests showed that the smaller models performed well on simple requests, but errors arose as complexity increased.
Because workflows required multiple model calls per request, even small inaccuracies were compounded, reducing overall reliability.
Larger models improved per-step accuracy to greater than 99.9%, reducing workflow errors from weekly occurrences to rare annual events.
Two Dell Pro Max systems with GB10 drives ran AI locally, keeping all data on-premises.
The reviewer estimated that automation replaced the need for a dedicated reporting function, with hardware costs covered within twelve months.
The AI tools handled both internal and external reporting requests, including article views, video engagement, and newsletter metrics, without requiring human intervention.
The process allowed the system to redirect resources to other functions, such as hiring an editor-in-chief, while maintaining consistent reporting quality.
Automating reports with AI systems shows how manual tasks of metric consolidation and retrieval can be removed from human workflows.
This means that roles that primarily focus on collecting, cleaning, and summarizing performance data are especially vulnerable once reliable automation is in place.
Although the review shows clear efficiency improvements, its success depends on model accuracy, workflow design, and maintaining control over sensitive data.
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