RFI Agent

The RFI Agent accelerates the end-to-end RFI drafting process, transforming user input or database records into precise, unambiguous, submission-ready RFIs — ensuring technical validation and adherence to project documentation.

Overview

It turns a plain-language field issue into a validated, submission-ready RFI with mapped fields, suggested assignees, and due dates — presented as a draft to review before it syncs back to Procore.

Inputs

Field coordination issue (natural language)
Drawings, specs, and submittals

Outputs

Draft RFI with mapped fields
Suggested assignees and due dates
Submitted RFI synced to Procore

Compatible Integrations

Microsoft Teams

Connect Microsoft Teams with Datagrid to import channel conversations, members, and settings into AI agent workflows.

QuickBooks

Connect QuickBooks with Datagrid to automate invoice processing, bill creation, and financial data workflows using agentic AI.

Azure Blob Storage

Connect Azure Blob Storage with Datagrid to automate document extraction, trigger workflows on file uploads, and route unstructured data through processing pipelines.

Acumatica

Connect Acumatica to Datagrid to automate ERP data extraction and run agentic workflows.

Amazon Redshift

Connect Amazon Redshift with Datagrid to run agentic AI workflows on your warehouse data autonomously.

BigCommerce

Connect BigCommerce with Datagrid to import data for cross-platform analysis and automated workflows.

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Related Guides

What a procurement RFI is and how it fits the sourcing lifecycle

Discover when to issue an RFI, how it differs from construction RFIs, and how AI agents streamline drafting, follow-up, and response aggregation at scale.

RFIs vs Submittals: What's the Difference?

RFIs clarify contract ambiguity. Submittals confirm compliance. Learn routing rules, response windows, and how misrouting creates schedule and claims exposure.

RFI vs RFP in Construction: Definitions, Process, and Where AI Agents Fit In

Learn how RFIs and RFPs differ in construction, how the workflow breaks down at scale, and where AI agents cut through the backlog.

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