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AI Lessons Learned Knowledge Agent

Automate a project lessons learned knowledge base for specialty construction firms
Overview
Custom solution
Workflow

Automating Project Lessons Learned Knowledge Bases with AI

Automate your entire project lessons learned knowledge base workflow across capture, curation, codification, and point-of-use delivery for specialty construction teams.
001
Capture Field Intelligence Without Disrupting Crews

The agent ingests lessons from existing project artifacts—NCRs, RFIs, BIM clash reports, safety incidents, and daily logs—so foremen and superintendents contribute knowledge through photos and voice notes rather than text-heavy forms.

002
Curate and Validate with Root Cause Rigor

AI automation applies consistent taxonomy, merges duplicates, links supporting evidence, and routes lessons through SME review workflows—transforming scattered anecdotes into searchable, validated guidance with proper root cause analysis.

003
Embed Knowledge at the Point of Work

The system surfaces relevant lessons contextually during pull planning, pre-install meetings, and QA/QC checks—connecting countermeasures directly to spec sections, ITPs, and BIM coordination so crews act on institutional knowledge before problems repeat.

How Cassidy automates lessons learned using AI

Step 1: Trigger on project artifact creation

The Workflow activates automatically when an NCR closes, an RFI is answered, a BIM clash is resolved, or a safety near-miss is logged in Procore, BIM 360, or your connected systems.

Step 2: Extract and structure lesson content

Cassidy pulls the relevant context—project details, spec section, trade/discipline, root cause summary, and attached evidence like photos, marked-up details, and submittal references—and maps them to your controlled taxonomy.

Step 3: Search Knowledge Base for related lessons

The Workflow queries your existing lessons learned repository to identify duplicates, related countermeasures, and patterns across similar project types, delivery models, or spec sections.

Step 4: Generate draft lesson with AI reasoning

Cassidy synthesizes the artifact data and related context into a structured lesson record—including problem statement, conditions, root cause codes, recommended countermeasure, and applicability tags—ready for review.

Step 5: Route for SME validation

The draft lesson is automatically routed to the appropriate reviewer—VDC manager for coordination items, QA/QC for quality defects, safety manager for hazard controls—with all supporting evidence attached.

Step 6: Publish and link to standards

Once approved, Cassidy publishes the lesson to your Knowledge Base with proper tagging, updates relevant checklists or ITP references, and creates hyperlinks in BIM families or detail libraries where applicable.

Step 7: Surface at point of use

When teams begin related work—during pull planning, pre-install meetings, or coordination sessions—Cassidy proactively delivers relevant lessons by activity code, spec section, or trade, ensuring countermeasures reach crews before problems recur.

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