Check whether the data behind one construction AI workflow is available, authoritative, fresh and properly restricted before you automate anything.
Keep the complete branded version for internal review and implementation.
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Do not treat every project file as equally trustworthy. Define authority, freshness, access, retention and project/customer restrictions before retrieval or automation.
| CATHA PRACTICAL AI FOR CONSTRUCTION LIBRARY Is Your Construction Data Ready for AI? A Practical Data Readiness Guide for Contractors Guide 4 |
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| Best use Assess the specific data needed for a target workflow. Do not attempt to clean the entire company before proving a use case. |
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| Test | Question | Score 0-5 |
|---|---|---|
| Availability | Does the required information exist? | |
| Authority | Which source is official when sources conflict? | |
| Quality | Is it accurate enough for the decision? | |
| Completeness | Are required fields/documents consistently present? | |
| Freshness | How quickly does information become stale? | |
| Access | Can the system retrieve only what it is allowed to use? | |
| Structure | Can information be found and interpreted reliably? | |
| Traceability | Can an output be tied back to its source? |
| Method | Use when | Do not use as a substitute for |
|---|---|---|
| Search / direct retrieval | The correct source can be found deterministically. | Basic information architecture. |
| RAG | The model must reason over changing internal knowledge. | Permissions, source quality or evaluation. |
| Fine-tuning | You need repeatable behavior/style or specialized pattern learning. | Keeping changing facts current. |
| Structured database/API | Exact values, transactions and authoritative records matter. | Natural-language knowledge work. |
| Check | Item | Notes |
|---|---|---|
| ☐ | Named authoritative sources | ____________________________ |
| ☐ | Sample of normal cases | ____________________________ |
| ☐ | Sample of difficult/exception cases | ____________________________ |
| ☐ | Known bad/obsolete examples | ____________________________ |
| ☐ | Access rules | ____________________________ |
| ☐ | Update frequency | ____________________________ |
| ☐ | Data owner | ____________________________ |
| ☐ | Retention requirement | ____________________________ |
| ☐ | Ground-truth test answers | ____________________________ |
| Catha principle Do not give an AI system broader access than the workflow requires. Data readiness includes the ability to say “no” to unnecessary access. |
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Practical questions construction owners ask.
Do we need to clean all of our data before using AI?
No. Assess the specific data needed for the target workflow. Do not attempt to clean the entire company before proving a use case.
How do you test whether construction data is ready?
Score it on availability, authority, quality, completeness, freshness, access, structure and traceability.
Should we use RAG or fine-tuning?
Use retrieval (RAG) when the model must reason over changing internal knowledge. Fine-tuning suits repeatable behavior or style, not keeping changing facts current.
Keep reading
Use AI where it creates value. Govern it everywhere it acts.
Catha helps construction companies identify, build and manage AI workflows while keeping business authority in human hands.