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Guide 2 of 5

Uploading & Processing Files

POD accepts almost any file format your team already uses. Upload once and the AI handles the rest.

Supported File Formats

POD's "Accept Anything" philosophy means you do not need to change your existing workflows. Upload whatever format your team uses today.

Excel (.xlsx, .xls)

The most common format. POD reads all sheets, headers, and data ranges.

CSV (.csv)

Comma-separated files. POD auto-detects delimiters and encoding.

PDF (.pdf)

Structured PDFs and scanned documents. AI extracts tables and text.

Images (.jpg, .png)

Photos of handwritten forms, whiteboards, or printed reports.

Voice recordings

Record a voice report and POD transcribes and maps it to KPIs.

P6 / MPP

Primavera P6 and Microsoft Project schedule files.

Tip: For images and scanned PDFs, POD uses vision AI to recognize handwriting and extract data — no separate OCR software needed.

How to Upload Files

There are two ways to get files into POD:

Drag and Drop

Drag files directly onto the upload area on your dashboard. You can drop multiple files at once. A progress indicator shows the upload and processing status for each file.

File Picker

Click the upload button and select files from your computer. This works the same as drag and drop but through your operating system's file browser.

Note: You can also connect cloud storage (Google Drive, Box, OneDrive, SharePoint, Dropbox) for automatic ingestion. When you save a new file to your connected folder, POD detects it and analyzes it automatically.

What Happens After Upload

Every file goes through a quick AI analysis. The entire process typically completes in under 30 seconds.

1

Analyze

POD reads the file and extracts all structured data — columns, rows, tables, text blocks, or transcribed speech.

2

Map

The AI mapping engine recognizes your terminology and automatically maps your column headers and data fields to the right POD KPIs.

3

Render

Matched data populates your dashboard KPIs. Cards with sufficient data appear automatically; cards without enough data are hidden until more data arrives.

Important: The rendering rule is simple: if the required data is present, the KPI renders. If data is missing, the KPI is hidden — you never see empty or broken cards.

AI Field Mapping

POD's AI mapping engine is what makes the platform so flexible. It learns your column naming conventions and improves over time.

Example: How mapping works

Your column:"Total Manpower"
AI reads:"Total Manpower" recognized as a crew count field
AI maps:Matched to "headcount"
Result:Mapped to HeadcountTracker KPI

The AI recognizes standard column names instantly. For common variations and abbreviations, it understands the context. Even truly unusual column names get mapped correctly because the AI understands construction terminology.

Tip: The AI mapping engine learns per project. The more files you upload, the more accurate it becomes at recognizing your team's naming conventions.

Coverage Score

Your coverage score is a percentage that shows how many of POD's available data fields have been populated by your uploads. Think of it as a data completeness indicator.

0 - 30%
Getting Started
Basic data — a few KPIs will render.
30 - 70%
Good Coverage
Most dashboards are useful. Keep uploading.
70 - 100%
Excellent
Comprehensive data. Full dashboard experience.

To improve your coverage score, upload more detailed daily reports. The more data fields you include (weather, labor counts, equipment hours, safety observations), the higher your score climbs and the more KPIs become available on your dashboard.

Troubleshooting

If something is not working as expected, check these common issues:

My file uploaded but no KPIs appeared

The file may not contain data that maps to any KPIs. Check that your spreadsheet has recognizable column headers (e.g., "Headcount", "Weather", "Safety Incidents"). Try adding more columns.

My coverage score is low

Your daily report may only cover a few data categories. Add more fields to your report template — labor by trade, equipment hours, weather conditions, safety observations, and schedule milestones all contribute to coverage.

The AI mapped a column incorrectly

You can manually override any AI mapping from the field mapping review screen. Your correction teaches the AI to handle similar column names better in the future.

My image or PDF was not analyzed correctly

Ensure the image is clear and well-lit. For PDFs, check that the text is selectable (not a low-resolution scan). Higher resolution images produce better results.