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RFI Cycle Time Analytics

AI analysis of RFI response times and workflow optimization.

Definition

RFI Cycle Time Analytics AI analyzes Request for Information workflows to identify bottlenecks and optimize response times. It tracks RFI volumes, response durations, and patterns to improve the RFI process and reduce construction delays caused by pending information.

In Depth

RFI cycle time — the elapsed days from submission to response — is a key indicator of project communication efficiency. When RFI cycle times lengthen, it often signals document quality issues (many questions needed), communication bottlenecks (responses stuck in review), or scope uncertainty (questions that nobody can answer from the existing documents).

AI analyzes RFI cycle time data across the project to identify patterns — which disciplines have the longest response times, which types of questions take the longest to answer, and which phases of construction generate the most RFIs. These patterns inform process improvements — for example, if most RFIs about structural connections take three weeks to answer, providing a structural connection standard earlier would reduce RFI volume and cycle time.

Examples

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Analyzing RFI response times

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Identifying workflow bottlenecks

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Reducing information delays

Nomic Use Cases

See how Nomic applies this in production AEC workflows:

Frequently Asked Questions

RFI Cycle Time Analytics AI analyzes Request for Information workflows to identify bottlenecks and optimize response times. It tracks RFI volumes, response durations, and patterns to improve the RFI process and reduce construction delays caused by pending information.

Analyzing RFI response times. Identifying workflow bottlenecks. Reducing information delays.

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