AI Spreadsheet & CSV Analysis

Summarize a Spreadsheet, Flag the Outliers, Ask It Questions

Send a CSV or Excel file and get a clear first read: what the dataset contains, which rows or trends look unusual, and answers to plain-English questions about the numbers. Built for the moment when you need a decision, not a BI project.

Summary · row-grounded anomaly flags · ask-your-dataAnswers grounded in the rows you provide

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Summarize CSV and Excel filesFlag outliers with row contextAsk questions in plain English

Answer first

Turn a spreadsheet into a decision-ready first read

Start with the file itself. VUST identifies its shape and columns, summarizes the main pattern, points to unusual rows with surrounding context, and keeps follow-up answers grounded in the same data. The result is a compact analysis you can verify against the source before acting.

Useful for sales, expenses, campaigns, operations, and research datasets.

Specimens

See the difference

See the user problem, an illustrative analysis, and the line between an anomaly flag and a business decision.

The everyday data problem

What you have

A CSV export of orders, expenses, survey responses, or ad spend with hundreds of rows and a dozen columns. Somewhere inside is the month that spiked, the region that stalled, or the row that should not be there.

What you need

A short account of what the file contains, which patterns deserve attention, and direct answers to questions such as 'which product drove the March drop?' without building a dashboard first.

Illustrative analysis

Six monthly sales rows

Jan 42,100 · Feb 44,800 · Mar 8,900 · Apr 46,200 · May 47,500 · Jun 49,100 units for one region.

Summary → anomaly → follow-up

Summary: five months sit between 42,100 and 49,100 while March falls far outside the pattern. Anomaly: March is about 80% below the surrounding months, so check for a partial export, stock-out, or data-entry error. Follow-up: excluding March, Jan-to-Jun growth is about 16.6%.

From flag to decision

A suspicious value

An anomaly score alone says only that a row is unusual. It does not know whether the cause is a promotion, outage, late upload, accounting rule, or genuine change in the business.

A grounded next question

VUST points to the relevant row and neighboring pattern, then helps you test explanations against the file. You keep the business judgment and verify decisions against the source data.

Practical use cases

Who spreadsheet analysis in chat helps

Small-business owners: A sales or expenses CSV export lands and opening a full BI tool is overkill; A plain-English read of what's in the file, the months or categories that look off, and answers to 'which product drove the drop?' — without pivot tables.
Marketers & ops leads: A weekly ad-spend or campaign dump needs a fast sanity check for anomalies; An automatic outlier pass that surfaces the spike, the stalled region or the row that shouldn't exist — a manual scan compressed into a focused first read.
Students & researchers: A first look at survey results or a dataset before the real analysis; A summary of the shape and columns plus a natural-language Q&A loop to poke at the numbers — orientation, not a statistics course.

How it works01–03

How spreadsheet analysis works

01
Summary
How many rows and columns, what each column looks like, the overall shape and any obvious trend — in a few plain sentences.
02
Outlier & anomaly flags
Values far outside the surrounding pattern, sudden drops or spikes, duplicate or empty rows — each flagged with a plausible reason to check.
03
Ask your data
Plain-English questions answered against the real rows: 'ignore March, what's the growth rate?', 'which category is over budget?' — not guessed.

Same tool · in Telegram@vustbot

Analyze a spreadsheet in Telegram

Send a CSV or Excel file for a concise dataset summary, row-grounded anomaly flags and plain-English follow-up questions.

Open in Telegram

Quality & trust

A useful first read, grounded in the file

Rows before rhetoric

The analysis starts from the spreadsheet's actual structure and values. Summaries name the columns and pattern; anomaly flags point back to the unusual row and its surrounding context.

A first pass, not a BI replacement

Use it to orient yourself, surface questions and investigate anomalies. Pivot tables, statistical models, dashboards and a person who understands the business context still matter.

Questions stay inspectable

Follow-up answers stay tied to the file, so you can compare the explanation with the original rows before making a financial, operational or research decision.

FAQ

Frequently asked questions

What files can I analyze?

Use CSV or Excel spreadsheets. VUST reads the table structure, identifies columns and row patterns, and keeps the analysis tied to the data in the file rather than treating it as an unstructured prompt.

What does the spreadsheet analysis return?

It returns three layers: a concise description of the dataset and its columns, outlier and anomaly flags with the relevant row context, and a question-and-answer loop for follow-ups such as which month drove a decline or which category exceeded budget.

Can I ask questions in ordinary language?

Yes. Ask questions such as 'ignore March, what is the growth rate?', 'which region contributed most to the increase?', or 'show me duplicate invoice rows'. Answers stay grounded in the rows and columns provided.

Why use chat for spreadsheet analysis?

Chat works well for the first decision-oriented read: you can move from a broad summary to a precise follow-up without rebuilding filters or formulas for each question. The output remains easy to inspect against the original sheet.

Does it replace Excel, a BI tool, or a data analyst?

No. It is a fast first pass for orientation, anomaly discovery, and follow-up questions. Pivot tables, statistical models, dashboards, and a person who understands the business context still matter. Treat every flag as a reason to investigate, not as a verdict.

Who benefits most from spreadsheet analysis in chat?

Small-business owners reviewing sales or expenses, marketers checking campaign exports, operations teams scanning weekly metrics, and students or researchers taking a first look at survey data all benefit from a clear summary before deeper analysis.

Where next

Ready when you are