Overview

ANDRE is an AI-powered survey data analyst for customer experience work. You upload customer evaluation or feedback survey data, and ANDRE cleans it, analyses it and produces an insight report. The company says this takes under 15 minutes and requires no data skills, and positions the tool as an alternative to manual analysis or generic chatbots.

Workflow

The process has two visible steps: upload your survey data, then get an analysis report. ANDRE says it bundles five products in one: data conversion, data cleaning, narrative suggestions, evidence-based story generation and action-oriented synthesis. An Expedite feature can automate the whole process, from cleaning through narrative extraction to report creation.

Data cleaning

ANDRE cleans and processes data automatically using data science and AI. Missing data is handled through structure mining, parsing and imputation, which matters because survey exports are often messy, with blank answers and inconsistent formats.

Narratives, not numbers

A distinctive idea is analysis at the narrative level. Instead of starting from charts, ANDRE suggests stories found in the data and uses the numbers as supporting evidence. Outputs aim to be action-oriented conclusions you can use for decisions, and one testimonial highlights automatically generated synthesis slides.

Data sources

ANDRE works with data collected through Google Forms, SPSS, Typeform, CSV files and other sources, so you do not need extra software to prepare your files.

Who it is for

Listed users include startup founders, customer experience specialists, marketing professionals, product managers, HR managers and academic researchers. The company claims up to 90% analysis time saved and five times faster iteration; these are its own figures.

Pricing

You can start free with a free trial and cancel any time. A Plans page lists the paid options.

Things to consider

Survey data often contains personal information, so remove names and contact details before uploading and check ANDRE's data handling terms. Imputation fills gaps with estimates, so check how much of your data was imputed before relying on a conclusion. For academic research, confirm that automated analysis meets your methodology and ethics requirements, and keep a record of the steps taken. Treat generated narratives as hypotheses to check against the raw responses.