Why Your AI Agent Is Only as Good as Your Data | MCP Workshop Part 1/5
Your AI system is only as good as your data and in AEC, most data is incomplete, inconsistent, and scattered across disconnected systems. A Revit model says one thing. A spreadsheet says another. A PDF plan set contradicts both. Before you write a single line of MCP code, you need to understand what data you're actually working with. That's what this session covers.
📚 What's Covered:
→ The 6 core data types in AEC: Numeric, Text, Audio, Visual/2D, Video, and Geometric/3D — how each behaves and what it takes to process it
→ The 4 data quality attributes every intelligent system depends on: Accuracy, Consistency, Completeness, Accessibility
→ Internal vs. external data quality — and why the rules are different
→ Structured vs. unstructured data — and how to convert unstructured AEC content into machine-readable formats
→ How to manage data quantity: why more isn't always better
→ Noise reduction: how to identify and minimise erroneous values using domain expertise
→ The 5-stage AEC Data Pipeline: Collection → Evaluation → Structuring → Cleaning → Enrichment
🧱 Series Structure:
This is Workshop #1 of 4. Each pillar is independent but together they form the complete foundation for building intelligent AI agents on Autodesk Platform Services.
→ #1 DATA — You are here
→ #2 AI — What types of AI exist and where they apply in AEC
→ #3 API — Connecting your data to intelligent workflows
→ #4 MCP — Building and deploying your MCP server end-to-end
📌 Full MCP Workshop Playlist: https://autode.sk/MCPWorkshopPlaylist
📌 Full Tutorial and Source Code: https://autode.sk/MCPWorkshopTutorial
▬▬▬▬▬▬ T I M E S T A M P S ⏰ ▬▬▬▬▬▬
0:00 – Introduction & Series Overview
0:34 – Workshop Structure: 4 Pillars (Data, AI, API, MCP)
0:47 – Why AI is Entirely Dependent on Data
1:31 – 6 Categories of Data in AEC
1:53 – Numeric Data
2:16 – Text Data
2:43 – Audio Data
3:09 – Visual / 2D Data
3:42 – Geometric / 3D Data
4:18 – How to Evaluate Data Quality
4:38 – Accuracy, Consistency, Completeness & Accessibility
6:11 – Structured vs. Unstructured Data
7:01 – Data Quantity
7:25 – Noise in Data
7:57 – The AEC Data Pipeline
8:51 – What's Next: Pillar 2 – AI
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