Evidano home

Features

Interviewer: What made you decide to leave the program? Participant: Honestly, the commute. Two buses each way, and once my shift changed I could not make the evening sessions anymore, even though I wanted to finish.

.pdf

Interviewer: What made you decide to leave the program? Participant: Honestly, the commute. Two buses each way, and once my shift changed I could not make the evening sessions anymore, even though I wanted to finish.

.docx

Interviewer: What made you decide to leave the program? Participant: Honestly, the commute. Two buses each way, and once my shift changed I could not make the evening sessions anymore, even though I wanted to finish.

.xlsx

Interviewer: What made you decide to leave the program? Participant: Honestly, the commute. Two buses each way, and once my shift changed I could not make the evening sessions anymore, even though I wanted to finish.

.csv

All Lengths & Languages

Analyze any document, any language, any format

PDFDOCXXLSXCSV50+ files per analysisUnlimited analysis100+ languages~150 pages per fileNo special formatting required
Documents
Interview 1
Survey Results
User Feedback
Themes
Usability0
Performance0
Features0

Thematic Analysis

Get codebook, themes, quotes, and more

Unlimited analysisNarrativeGrounded TheoryPhenomenologicalDiscourseHierarchicaland more
Citation
Key Insight
Data Point
Quote
Reference
Finding

Content Analysis

Get answers to questions

Unlimited analysisHigh AccuracyLow BiasVerified CitationsClickable QuotesTrusted by Peer-reviewed Journals
Theme
0%
Concept
0%
Insight
0%
Code
0%
Pattern
0%
Viewpoint
0%

Frequency Analysis

Track key concepts, viewpoints, themes, and codes

Unlimited analysisInteractive charts with quotesCustom codes/viewpoints/themesCompare across segments
GenderRegionAge
Male
68%
Female
75%
North
62%
South
72%
West
80%
18-24
85%
25-34
72%
35-44
68%
45+
58%

Cross-segment Analysis

Compare insights across gender, region, etc.

Unlimited analysisInteractive charts with quotesCustom segmentsSegment-specific analysisSimilarities and Differences

Custom Reports

Generate interactive reports with key findings

Unlimited analysisClickable quotesInteractive chartsCustom report templatesOne-click sharing
What's this document about?
This is a financial report for Q3
Summarize key findings
Revenue increased by 12%...

Chat with your documents

Ask questions to your documents

Unlimited chat sessionsAnalyzes the full documentRefine existing AI analysesGenerate charts

AI Survey / Interview

Conduct intelligent interviews automatically

30+ languagesVideo avatar, voice or text chatCustom avatarsNatural audio and visualsContextual follow-upsTrained on your interview guide

AI Transcription

Convert video/ speech to text with high accuracy

100+ languagesMultiple speakersCustom dictionaryRedact PIIAI suggested edits
EN
ZH

Document Translation

Translate entire documents

100+ languagesFormat preservationBatch translationHigh accuracyCustom translation phrases
Data Extraction Pipeline
Input
Output

Get Social Media/ Web Data

Extract data from websites

Upload URLsHigh accuracyFormat preservationBatch data extraction
BAR
Bar Chart
Frequency Analysis
NET
Network Chart
Code Co-occurrences
TREE
Hierarchical Chart
Relationships
WORD
DATAAITECHCODEMLAPIWEBDEV
Word Cloud
Text Analysis

Data Visualizations

Insightful graphical data representations

Bar charts for frequency analysisNetwork charts for code co-occurrencesHierarchical charts for relationshipsWord Cloud

Chat with Analysis

Analyze this data trend
Green bar shows 40% growth
What caused this spike?
Market expansion drove results

Share and Collaborate

Let others edit, view, and chat with your project

Chat with analysisView/ edit insightsClick on quotesInteract with charts
Video clip reel storyboard with participant faces

Create audio/ video clip reels

Generate multimedia clips organized by key perspectives to tell a more engaging story

Audio/ Video ClipsBulk DownloadEdit Options

Overall agreement

0.00

slight

Cohen's kappa · 95% CI: 0.00 to 0.00

Right when it applied a code

0%

Precision · 95% CI: 0% to 0%

Right when it left a code out

0%

True negative accuracy · 95% CI: 0% to 0%

Accuracy by code

CodeCheckedRightWrongAccuracy
JargonCommunication17 of 171432 over-coded · 1 missed82%
Travel burdenAppointments21 of 211922 over-coded90%
Being listened toCommunication28 of 282621 over-coded · 1 missed93%
Family supportSupport24 of 242311 over-coded96%
Waiting timesAppointments34 of 343311 missed97%

AI Accuracy & Inter-coder Agreement

Check the AI's coding against your own review, and report the statistics reviewers ask for

Cohen’s kappa95% confidence intervalsTrue positive/ negative accuracySample any number of decisionsRepeat validation runsRevise codebook from errors

Codebook

Trust in clinicians · Being listened to

“She wrote everything down before she looked at me. I was the last thing in the room.”

RK

rahul.k@university.edu

Coding this as relational trust rather than institutional trust — participants keep separating the two.

AM

amara.m@university.edu

@rahul.k@university.edu I widened the definition after interview 3. Noting it here so the change stays on the record.

SL

sofia.l@university.edu

My own clinical background may be shaping how I read this quote — revisit after the next round of coding.

Someone is writing a memo…

Research Memos & Reflexivity Journal

Keep analytic decisions and reflections attached to the theme, code, or quote they belong to

Threaded comments@mentionsMemo any theme, code, or quoteFavouritesTimestamped and attributedShared with collaborators
1 theme changed
2 codes added
1 code removed
3 codes changed
12 quotes added
4 quotes removed
7 quotes changed

Codes(# quotes)


Definition: Feeling heard and taken seriously by a clinician.clinician during an appointment.
Best Quote

Interview 07.docx

She wrote everything down before she looked at me. I was the last thing in the room.

Versioned Codebook & Audit Trail

Every codebook edit is saved as a version you can compare, attribute, and defend

Full version historySide-by-side diffAdded/ removed/ modifiedWho changed what, and whenOriginal vs currentQuote-level traceability
Dear Margaret,The harvest came in latethis year, but the barleyheld up well despite rain.Yours, Thomas — 1874

Image to Text

Extract, analyze, and transcribe text directly from images, screenshots, or scanned documents

Handwritten & printed textScreenshots & photosMulti-page PDF scansCustom transcription prompts~100 output languagesEdit & verify each page