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1

Drop your file

CSV, Excel, JSON, or TSV. Columns and types are detected instantly — no setup, no schema mapping.

2

Ask in plain English

The engine writes and executes real Python on your data. Metrics, charts, and findings stream in as blocks — each with its computation attached.

3

Send the report

One click assembles the blocks into a themed document with a live share link and print-perfect PDF. That is the deliverable.

This is what your data becomes

A real report, generated from a real dataset by the same engine you get — every number computed, every chart from a validated spec.

Analysis reportJuly 1, 2026

Organic Search Performance — Q2 Review

Prepared for
Acme Outdoor Co.
Source
seo-traffic-last-90-days.csv · 90 rows

Organic clicks (30d)

13,344

+14.3%

Impressions (30d)

289k

+13.5%

CTR

4.61%

+0.03pp

Avg. position

8.4

-0.6

Organic clicks by week

Steady growth across the quarter, with the strongest week late in the period

Key insight

Organic clicks grew +14.3% over the last 30 days while average position improved to 8.4. Rising impressions with stable CTR means the growth is coming from new query visibility — not just better rankings on existing terms.

Recent weekly performance

WeekClicksImpressionsWoW change
Week of 05-212,81359.6k+9.1%
Week of 05-283,07166.4k+9.2%
Week of 06-043,11469.2k+1.4%
Week of 06-113,13266.3k+0.6%
Week of 06-183,12267.2k-0.3%
Week of 06-252,86562k-8.2%

All figures computed from source data · Updated July 1, 2026 · seo-traffic-last-90-days.csv

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Every number, sourced

Analyses run as real Python code on your data — click any figure to see the computation that produced it.

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The output is a branded, shareable report — not answers trapped in a chat thread.

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How to analyze data with AI

To analyze data with AI, you upload a dataset, ask questions in plain English, and let a model translate each question into computation it runs against the full file — then you verify the results before you share them. The verification step is what separates real AI data analysis from asking a chatbot to eyeball your spreadsheet: a language model predicting numbers will sometimes get them wrong, while executed code cannot arrive at an answer it didn't compute.

Here is the complete workflow as it runs in AnalyzeData:

  • Load your data. Drop a CSV, Excel, JSON, or TSV file into the workspace — parsing happens in your browser — or pull data straight from Google Search Console or GA4 with the built-in connectors.
  • Ask in plain English. "What drove the revenue change last quarter?" "Which pages gained the most clicks?" The engine writes real pandas code for each question and executes it on the full dataset while you watch it run.
  • Verify the answers. Every metric, chart, and finding carries its provenance — click any number to see the exact Python that produced it and its output. If a figure looks off, you can check the computation instead of trusting a black box.
  • Turn it into a report. One click assembles the verified blocks into a themed document with a written summary, a live share link, and a print-ready PDF — the deliverable, not a chat transcript.

What AI data analysis should give you — and what it can't replace

A good AI data analyzer removes the mechanical work: writing the groupby, building the chart, formatting the document, and drafting the "what changed and why it matters" narrative. On those jobs it turns hours into minutes, and because AnalyzeData executes real code with the computation attached, the output holds up when a client or manager asks where a number came from.

What it doesn't replace is judgment. Knowing which question matters, whether the data can answer it, and what to do about the result stays your job — the tool's role is to make every answer fast and verifiable. That's why the product is built around two things only: the analysis and the report. For chart-specific work, see AI data visualization; for the recurring version of the job, see automated reporting.

Free during launch — no signup, no credit card. Upload a file at analyzedata.io/dashboard and judge the output on your own data.

Frequently Asked Questions

Everything you need to know about using AnalyzeData.

AI data analysis uses artificial intelligence to examine datasets, identify patterns, and generate insights automatically. AnalyzeData goes one step further than most tools: every analysis runs as real Python code on your data, and every number links back to the computation that produced it — so results are verified, not guessed.

When you ask a question, the engine writes and executes Python (pandas) against your dataset in an isolated sandbox, then composes the computed results into metric rows, charts, and written insights. The code and its output are attached to every block — click "Verified" to see exactly how a number was calculated.

CSV, Excel (.xlsx), JSON, and TSV. Upload the file and the structure is detected automatically. Files up to 10MB and 50,000 rows are supported today; connected sources with higher limits are on the roadmap.

No. You ask questions in plain English and get back metrics, charts, and written findings. The Python that runs behind the scenes stays visible for anyone who wants to verify or reuse it — but you never have to write it.

Two ways. First, trust: AnalyzeData computes every number with executed code and shows you that code, while chat tools frequently estimate from samples. Second, the deliverable: your analysis becomes a themed, shareable report with a live link and PDF export — not a conversation you have to copy-paste from.

Files are parsed in your browser; for analysis, your dataset is processed transiently by our AI engine in an isolated code-execution sandbox and is never used to train models. Nothing is stored unless you create a report — reports live at an unguessable link, are hidden from search engines, and can be deleted at any time.

An AI data analyst is software that does what you would ask a human analyst to do: read a dataset, run the right computations, chart the results, and explain what matters. AnalyzeData is built to be exactly that — with the added discipline that every figure is computed by real code you can inspect, and the output is a report you can send.

Yes. Upload a CSV, Excel, JSON, or TSV file and ask questions like "what are the main trends?" or "which segment performs best?". The engine computes summary statistics, comparisons, and time trends, then presents them as charts and written findings you can turn into a report.

AnalyzeData handles common statistical tasks — summary statistics, distributions, correlations, outlier checks, and group comparisons — using pandas under the hood. Because the executed code is attached to every result, you can validate any statistic before relying on it.

Yes. Whether you call it analyze data or analyse data, the workflow is the same: upload a structured file, ask a question, and get a computed, explained answer you can share as a report.

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