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Use Case

Automate Out-of-Scope RFI Detection

ProductAgentsUse CasesAutomate Out-of-Scope RFI Detection

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Job to Be DoneThe Operational ProblemHow It WorksInputs & OutputsWorkflow ContextWorks WithFAQGet Started

Screen RFIs against drawings, specs, and scope records before formal submission, so project teams catch out-of-scope or unnecessary questions earlier.

👷

Try the RFI Validator Agent

Validate RFIs before submission by identifying trivial requests and flagging cost, schedule, or quality implications.

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The Operational Problem

RFI text sits in one system. Contract drawings and specifications live in others. Field teams submit questions, but scope review often happens after the RFI reaches the design team's queue.

That delay carries real cost. Each RFI can consume significant administrative and technical effort, and response cycles can stretch across more than a week. Some RFIs are unnecessary because the answer already exists in the contract documents. Out-of-scope RFIs that slip through can surface in claims proceedings and blur design liability lines.

How RFI Validator Agent Automates This

Datagrid's RFI Validator Agent is an AI agent for construction project file workflows. People make the submission decision. The AI agent cross-references scope, delivers a structured assessment, and does not author RFI responses or modify contract terms.

1

Ingest project inputs

The AI agent connects to existing project tools and ingests incoming RFI drafts with their attachments. It also ingests the contract drawings, specifications, and executed scope of work for the relevant project.

2

Compare against scope

The AI agent compares the RFI's subject matter against contract drawings and specs. Questions that reference work, materials, or methods outside the executed scope get flagged before anyone routes them to the architect or engineer of record.

3

Classify request impact

Each RFI receives a structured classification. The AI agent flags RFIs with potential cost, schedule, or quality implications and separates them from unnecessary requests where the answer already exists in the contract documents. Historical RFIs also feed the classification, so repeated questions or previously resolved issues are detected automatically.

4

Deliver screening report

The AI agent generates a structured report with the scope determination, impact classification, and action items. This output documents the screening decision for the project record and gives project teams context for response drafting if the RFI proceeds to formal submission.

Inputs & Outputs

Inputs

  • RFI text and attachments, including field sketches and markup references

  • Contract drawings and specifications for the relevant project

  • Historical RFIs and their resolutions from the same project or portfolio as precedent for recurring question types

  • Supporting project files, including field data that informs scope boundaries

Outputs

  • Structured report with scope determination, impact classification (cost, schedule, quality), and flagged out-of-scope indicators per RFI

  • Action items specifying whether each RFI should proceed to formal submission, requires revision for clarity, or can be resolved from existing contract documents

Workflow Context

The RFI Validator Agent operates inside a broader project file and project controls workflow. It connects directly to RFI generation and response drafting by screening requests before they consume design team bandwidth. It also feeds procurement and change order workflows when flagged RFIs carry cost or schedule implications. Datagrid's construction document automation resources cover how screening agents connect to submittal and change order tracking. For portfolios spanning multiple projects, the structured outputs create a consistent screening record across active jobs. Unfiltered RFIs contribute to delayed responses, disputed change orders, and weaker defensibility during claims.

Works With

The RFI Validator Agent connects to your existing construction management stack without custom integration work, so teams can screen RFIs against the same project records they already use to manage drawings, files, and field workflows.

Procore

Procore

Connect Procore to Datagrid to run AI agents across RFIs and project records, so incoming questions can be screened against drawings, specifications, and scope documents before formal submission.

Oracle Aconex

Oracle Aconex

Integrate Oracle Aconex with Datagrid to automate project file processing and RFI triage using AI and route flagged RFIs with a structured screening record.

BIM360 Docs

BIM360 Docs

Connect BIM360 Docs with Datagrid to automate project file processing, classification, and cross-platform data flows, so teams get direct access to the drawings, specifications, and supporting files used in RFI screening.

BIM 360 Build

BIM 360 Build

Connect BIM 360 Build with Datagrid to automate workflows with AI agents using field data like issues, RFIs, and forms. Keep scope validation and flagged item review tied to active project records.

A

Autodesk Construction Cloud (ACC)

Connect Autodesk Construction Cloud with Datagrid to extract project data and run AI workflows that execute RFI screening, scope validation, and routing across the project file set.

With these systems in place, teams can keep RFI screening, scope validation, and project file processing aligned across the broader construction workflow.

Frequently Asked Questions

The AI agent cross-references each incoming RFI against the contract drawings, specifications, and executed scope of work for the relevant project. When the subject matter falls outside the design team's contractual authority, the AI agent flags it in the structured report with a specific scope determination. This happens before the RFI enters the formal submission cycle.

No. The AI agent delivers a structured report with scope classifications and impact flags. The project manager reviews that output and decides whether to submit, revise, or reject each RFI. The AI agent handles project file cross-referencing and classification. The PM evaluates the scope determination against field conditions, project priorities, and contractual context, then makes the call.

The AI agent compares the RFI text against the full set of contract drawings, specifications, and historical RFI resolutions. When an incoming question matches information already documented in the project files, the AI agent classifies it as trivial or unnecessary and flags the relevant source material. Some RFIs are not justifiable because the answer already exists in the contract documents.

The AI agent ingests historical RFIs and their resolutions as a standard input alongside contract drawings and specifications. When a new submission covers ground already addressed in the project record, the AI agent compares the incoming RFI text against previously resolved questions and flags the match. This detection feeds directly into the trivial or unnecessary classification.

Each RFI receives a classification that separates routine information requests from those carrying potential cost, schedule, or quality impact. The structured report tags high-impact RFIs with specific flags so project managers can prioritize review and route them to the appropriate decision-makers.

Improve RFI Screening Consistency with Datagrid

Datagrid's AI agents screen RFIs against contract scope before unnecessary questions reach design review.

  • Scope validation: Cross-reference incoming RFIs against drawings, specifications, and executed scope of work.
  • Impact classification: Flag questions with cost, schedule, or quality implications for prioritized review.
  • Trivial request detection: Identify RFIs where the answer already exists in contract documents or prior project records.
  • Structured reporting: Produce a documented screening record with action items for submission, revision, or resolution.
Try the RFI Validator Agent

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RFI Validator Agent

Validate RFIs before submission by identifying trivial requests and flagging cost, schedule, or quality implications.

Works with

ProcoreProcoreSharePointSharePointPlanGridPlanGridSlackSlackTrimble ConnectTrimble Connect
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Related Guides

RFI Meaning in Construction and Procurement: Definition, Types & Examples
Guide 011 agent

RFI Meaning in Construction and Procurement: Definition, Types & Examples

RFI means different things in construction vs. procurement. Learn the key differences, workflow steps, and how AI agents validate RFIs before submission.

Read

You've got more important things to do. Let Datagrid handle the rest.

Watch our quick demo to see how Datagrid transforms workflows. Discover the seamless integration of our AI assistants in real-time tasks.

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