Success Stories Architecture, Engineering & Construction

AI Document Intelligence for AEC Project Delivery

A 200-employee architecture and engineering firm running 30+ concurrent projects was losing thousands of hours annually to manual document handling. Project managers spent 15+ hours per week searching for specs, cross-referencing RFIs, and tracking submittals across disconnected systems. We deployed an AI document intelligence platform that transformed how the firm finds, processes, and acts on project information.

Client
Multi-Office AEC Design Firm
Industry
Architecture, Engineering & Construction
Duration
4 months
Year
2025
74%

Faster Retrieval

$480K

Annual Time Savings

92%

Extraction Accuracy

15 hrs/wk

Saved per PM

01

The Challenge

AEC firms generate enormous document volumes — every project produces hundreds of drawings, specifications, RFIs, submittals, change orders, and meeting minutes. This firm stored documents across Procore, SharePoint, email, and local drives with no unified search or cross-referencing. Finding the right revision of a specification could take 45 minutes. Missed RFI responses were causing rework that cost the firm an estimated $600K annually in unbilled hours and change orders.

02

The Solution

We built an AI-powered document intelligence system that ingests documents from all sources, automatically classifies them by type and project phase, extracts key data points (dates, spec sections, responsible parties, deadlines), and creates a searchable knowledge graph across the entire project portfolio. Project managers now find any document in seconds, get automatic alerts for expiring deadlines, and can trace the full history of any design decision across RFIs, submittals, and change orders.

What we set out to achieve
Reduce document search and retrieval time by 70%
Eliminate missed RFI response deadlines
Create a unified search across all document repositories
Deploy without disrupting active project workflows
Our approach

How We Did It

Step 01 Starting point

Document Landscape Audit

Mapped the firm's full document ecosystem — Procore, SharePoint, email, local drives — and cataloged 200K+ existing documents across 30 active projects to understand formats, naming conventions, and retrieval patterns.

Step 02

AI Classification & Extraction

Trained document intelligence models on the firm's actual documents — specs, RFIs, submittals, change orders, meeting minutes — to automatically classify, tag, and extract key fields like deadlines, responsible parties, and spec references.

Step 03

Knowledge Graph & Search

Built a cross-project knowledge graph that links related documents together — connecting an RFI to the spec section it references, the submittal it triggered, and the change order that resulted. Unified search returns results in under 2 seconds.

Step 04

Workflow Integration

Integrated with Procore and Outlook so the system fits into existing workflows. Automatic deadline alerts, weekly project document summaries, and an RFI response tracker that flags items approaching their due date.

Insights

What We Learned

AEC Documents Are Uniquely Messy

Scanned markups, hand-annotated drawings, email chains buried in forwards — AEC documents don't look like clean corporate paperwork. Training on real project documents instead of templates was essential for accuracy.

Search Changes Behavior

Once project managers could find anything in seconds, they started proactively checking precedents before making decisions. Document intelligence didn't just save time — it improved decision quality across the firm.

Technology

Built With

Cloud Platform
Microsoft AzureAzure Cognitive SearchAzure Blob Storage
AI & Document Processing
Azure AI Document IntelligenceAzure OpenAICustom Classification ModelsGraph RAG
Integration
Procore APIMicrosoft GraphAzure FunctionsAzure Service Bus

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