🧭 Overview

Schemas define the structure and meaning of the data you bring into cofi.ai.

When you connect a source—such as a CSV file, ERP system, or CRM—the schema acts as a blueprint that helps the platform recognize and align incoming fields.

A well-structured schema ensures your imported data can be easily mapped, processed, and connected to your databases for analysis.

🧩 What a Schema Does During Data Import

When a new source is added, cofi.ai reads its schema to determine:

• 🏷 Column names — the labels assigned to each field in your file or API.
• 🔢 Data types — whether values are numeric, text, or dates.
• 🧱 Structure — how data is formatted and organized for ingestion.

This metadata allows the platform to automatically match fields to existing database structures or flag unmapped data for review.

⚙️ How Schemas Are Used in Data Sources

• 📄 CSV or File Uploads: The schema derives from the header row of your file.
• 🔗 System Connections (e.g., CRMs or ERPs): The schema comes from the API’s defined field structure.
• 🛠 Manual Mapping: You can rename, format, or categorize fields during setup to align with your workspace standards.

Once configured, your schema acts as a contract between the source and the database—ensuring consistent field alignment during every sync or reprocess.

✅ Best Practices

💡 Use these tips to ensure clean imports: