AI CSV Analyzer
Ryna AI reads the CSV or Excel file you upload, identifies column types, computes summary stats, flags missing data, and suggests Excel or Sheets formulas for your questions.
Ryna AI Editorial Team · Updated 24.03.2026
Opening a CSV and staring at hundreds of rows is easy; understanding what those rows actually say is the hard part. A recurring finding in Anaconda's data science surveys: analysts spend nearly half their time (~45%) cleaning and preparing data, leaving little for the actual interpretation. The result is familiar: you have the sales table in front of you but can't give a one-sentence answer to 'why did revenue drop this month.'
Ryna AI steps in right here. Upload your CSV or Excel file; Ryna identifies column types (date, currency, category, text), computes summary statistics (mean, median, min-max, standard deviation), reports missing and duplicate rows, flags outliers, and — most importantly — explains the findings in plain language: 'Revenue fell 18% in March, and nearly all of that drop comes from a single category.' You ask questions in business language ('which segment is most profitable', 'is there seasonality', 'which channel has high cost'), and Ryna gives both the answer and the formula (SUMIFS, COUNTIFS, pivot, =FILTER) you'd use to verify it in your own file.
Treat the output as a draft, a first read — Ryna interprets the numbers, but the decision and the responsibility stay with you. On large files especially, calculations can be approximate; re-check every figure that goes into a report, an invoice, or a decision in your own tool (Excel, Sheets, SQL). File upload and analysis is a Plus feature (399.99 TRY/month; image analysis/OCR, PDF and file analysis, deep thinking, and web research come together). You can also get help without uploading: pasting your column structure and a few sample rows to request formulas, interpretation, and analysis logic is near-unlimited daily on the free plan.
Why use Ryna AI for this
Automatic column-type detection: date, currency, category, boolean — Ryna understands what's what and won't mistake a text column for a number.
Plain-language summary: 'mean is 1,240 TL but median is 890 TL — a few large orders pull the average up', a human sentence, not a bare table.
Missing, duplicate, and outlier detection: blank cells, double records, and extreme values reported in one prompt, with suggested cleanup steps.
Trend and seasonality interpretation: monthly/weekly direction, plausible hypotheses for rises and dips, and year-over-year change percentages.
Formula and pivot bridge: gives the SUMIFS, COUNTIFS, pivot, or =FILTER/=UNIQUE equivalent so you can verify every finding in your own file.
Chart recommendations: tells you which chart (line, bar, box, scatter) answers which question and, on request, produces matplotlib or Sheets code — you draw it, since Ryna doesn't generate images.
Example prompts
Copy any prompt below and paste into chat.rynaai.com. Each prompt is tuned for a different scenario — try them all to see how Ryna AI adapts.
How it works — step by step
- 1
Upload the file or paste a sample
Go to chat.rynaai.com and upload your CSV/Excel file (a Plus feature), or paste the column headers and 5-10 sample rows. Describe the columns in one sentence: 'date in ISO format, revenue in TRY, channel is text' — so Ryna won't confuse which column is a number and which is a category.
- 2
Give context and the question in business language
Say clearly what you're after: 'why did revenue drop in Q2', 'which customer segment is most profitable', 'which product has a high return rate'. One precise question beats a vague 'analyze this' by a wide margin.
- 3
Get the summary and findings, then verify the numbers
Ryna returns summary stats, trend interpretation, and plausible cause hypotheses. Re-check every critical figure in your own file (with a pivot or SUMIFS) — on large files the math can be approximate, and a decision number can't carry an error.
Example: input → output
You ask
Attached sales.csv — columns: date, product, category, qty, unit_price, amount. For 2025, interpret the monthly revenue trend, the top 3 categories, and the likely cause of the sudden drop in March.
Ryna AI returns
Here's a first read of sales.csv. The figures are approximate — verify them one-to-one with a pivot before the final report. Summary • 12 months, 8,412 rows, no blank cells; 3 duplicate rows exist (same date+product+amount) — I'd suggest deleting them. • 2025 total revenue: ~4.7M TL, monthly average ~392K TL. Monthly trend • Jan-Feb flat (~360K), Apr-Jun climbing (peak in June ~510K), a mild decline toward year-end. • March is clearly low (~250K) — about 30% below the neighboring months. Top 3 categories (by revenue) 1. Electronics — ~41% 2. Home & Living — ~23% 3. Accessories — ~15% March drop — likely cause Breaking down the rows, almost all of the drop comes from Electronics; qty fell but unit_price stayed flat. So it looks demand- or stock-driven, not a price issue. Check: was there a stockout or a campaign gap in Electronics in March? Verification formula (Excel): =SUMIFS(amount, date, ">=2025-03-01", date, "<=2025-03-31") For the category breakdown, a pivot on date+category is the cleanest. If you'd like, let's filter March to Electronics only and break it down weekly to pin down which week the drop began.
Pro tips
- State the number format and unit upfront: unless you say '1.234,56 in Turkish format, comma is the decimal separator', Ryna may misread the thousands separator and skew revenue by 1,000x.
- Instead of uploading a huge file as-is, first filter to the relevant columns and date range — a focused 5,000-row CSV beats a raw 200,000-row file on both speed and accuracy.
- Ask Ryna to 'show how you calculated it': have it state which rows it counted and which filter it applied. That way you audit the logic instead of accepting the number blindly.
- When hunting outliers, say 'flag them with the IQR or z-score method' rather than 'find the weird values' — you get a more consistent, reproducible, defensible result.
- When asking for correlation, hold the 'relationship, not causation' frame yourself: Ryna will say 'ad spend and sales rise together' but won't present it as cause and effect; check other variables before deciding.
- Upload sensitive data masked: if fields like national ID, IBAN, or phone aren't needed for the analysis, drop or anonymize them — the analysis still works and the needless risk drops.
Common mistakes to avoid
- ✕Putting Ryna's total into a report without verifying it — on large files there can be a counting or arithmetic error; recompute every decision figure in your own tool.
- ✕Not stating the column unit and number format — if Turkish format (comma decimal) gets mixed with English format, revenue can be off by a hundred or a thousand times.
- ✕Saying 'analyze this' and stopping — Ryna can't know what to look for; one precise question ('which channel has the lowest CPA') yields a far more useful answer than a broad request.
- ✕Uploading a massive raw file as-is — filter to the relevant columns and range; otherwise it slows down and the model may sample part of the data and return an incomplete result.
- ✕Mistaking correlation for causation — 'Y rose as X rose' is a relationship, not proof; check other possible causes before making a decision.
Who this is for
Marketing analysts, small business owners, accounting teams, and students learning data analysis.
FAQ
Can I trust the numbers it gives — are they exactly right?
Summaries and interpretations are usually accurate, but on large files Ryna may sample rows or slip in the arithmetic. Turn it into a rule: verify every figure headed for a report, an invoice, or a decision in your own tool (Excel, Sheets, SQL). Use Ryna as a fast first-read and interpretation layer, not as a precise accounting tool.
How large a CSV can it handle?
On very large files both speed and accuracy drop. The practical fix is to filter to the relevant columns or date range and upload a smaller CSV, or to summarize the data first (say, down to monthly totals) and upload that summary.
Does it draw charts or graphs?
No, Ryna doesn't generate images. But it tells you which chart best shows your issue and, on request, gives you the code/steps for matplotlib, Excel, or Google Sheets; you build the chart in your own tool in a few seconds.
Do I need Plus to upload a file?
File upload and analysis is a Plus feature (399.99 TRY/month). On the free plan you can still get formula, interpretation, and analysis-logic help without uploading — just paste your column structure and a few sample rows — and that's near-unlimited daily.
What happens to the data I upload — is it secure?
Uploaded files aren't used to train the model and aren't sold to third parties; it's KVKK-compliant. Even so, for sensitive financial data it's best practice to mask personal identifiers (national ID, IBAN) before the analysis.
Related use cases
Free — near-unlimited daily messages
No credit card. Plus at $12/mo (399.99 TRY) unlocks image analysis, file analysis (PDF/Word/Excel), deep thinking, web research, and assistants.