When people think of AI document analysis, they usually picture PDFs — research papers, contracts, reports. But the reality of modern knowledge work is far messier. Your most important data might be in a 50-tab Excel workbook. Your key insights might be buried in a 120-slide PowerPoint deck. Your sales data might live in CSV exports from your CRM. PDF is just one format among many, and AI analysis should handle all of them.
laminai supports the full range of document formats that real-world knowledge workers actually use — and applies the same AI summarization, extraction, and Q&A capabilities to each.
Supported Document Formats
- Text extraction from all PDF types
- Multi-column layout support
- Table detection and parsing
- Scanned PDFs (OCR coming soon)
- Full text with headings and structure
- Table content extraction
- Comments and tracked changes
- Headers, footers, footnotes
- All sheets extracted and analyzed
- Row/column data converted to context
- Formula values (not formulas) extracted
- Charts described from data
- All slide text extracted in order
- Speaker notes included
- Slide titles used for structure
- Alt text from images captured
- Column headers identified and explained
- Statistical summary of numeric columns
- Categorical data analyzed
- Patterns and trends highlighted
- Plain text fully supported
- Markdown formatting preserved
- RTF with basic formatting
- Fastest processing of all formats
How AI Handles Spreadsheet Data
Excel analysis is fundamentally different from PDF analysis because spreadsheets contain structured, tabular data rather than flowing prose. Here's how laminai's AI processes spreadsheet content:
Sheet Enumeration
All sheets in the workbook are identified and extracted. Sheet names provide important context — "Q3 Revenue" tells the AI what the data is about before it reads a single row.
Header Detection
Column headers are identified and used to label each column's data in the context provided to the AI. This is crucial — "147" means nothing; "$147K — Q3 Software Sales" means everything.
Data Conversion to Natural Language
Tabular data is converted into a structured text representation that the language model can reason about. For large spreadsheets, representative samples and aggregations are used to stay within context limits.
AI Summary and Q&A
The AI generates a natural language summary of what the spreadsheet contains, highlights key findings, and is available to answer questions: "Which region had the highest growth?" "What's the average deal size?" "Flag any anomalies in the data."
For Excel files with thousands of rows, the AI processes a representative sample plus computed statistics (min, max, mean, count, unique values) for each column. This gives the AI sufficient context to answer most questions accurately without needing to see every row.
Analyzing Presentations with AI
PowerPoint presentations are a unique format — they're designed for visual communication, with sparse text on slides that's meant to accompany spoken narration. This makes them both information-rich (the structure, ordering, and slide titles convey a lot) and information-sparse (there's rarely enough text on slides alone to understand the full content).
If your PowerPoint includes speaker notes, laminai extracts them along with the slide text. Speaker notes often contain the full narrative that the slides only hint at — enabling much richer AI analysis. Always add speaker notes to your presentations when sharing for analysis.
The AI handles presentations by treating each slide as a section, using slide titles to create document structure, and combining slide text with speaker notes to reconstruct the complete intended message.
Questions You Can Ask About Presentations
- "What is the main argument of this presentation?"
- "Summarize slides 10–20 on the competitive analysis"
- "What data sources are cited to support the claims?"
- "What recommendations does this deck make?"
- "What questions does the presenter think the audience will have?"
- "Create a one-paragraph executive summary of this deck"
"Your spreadsheets and presentations contain some of your most valuable institutional knowledge. AI makes it searchable and conversational for the first time."
CSV and Data File Analysis
CSV files are the universal data exchange format — exported from databases, CRMs, analytics platforms, and financial systems. They contain pure structured data, which the AI can analyze to surface patterns that would take an analyst hours to find manually:
- Descriptive statistics: "What's the average contract value and how does it vary by region?"
- Trend identification: "Is revenue growing quarter-over-quarter?"
- Anomaly detection: "Are there any rows with unusually high or low values?"
- Category analysis: "Which product category appears most often?"
- Correlation spotting: "Do customers with higher plan tiers have lower churn rates?"
AI analysis of CSV data provides descriptive insights and pattern identification — it's not a replacement for a proper data analytics tool like Tableau or Python pandas for complex statistical analysis. Think of it as a rapid first-pass that tells you where to look, not a full quantitative study.
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