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Work tells its own story. You still have to be able to hear it.

Cohortes equips workplace harassment investigations, psychosocial risk assessments and workplace wellbeing surveys.

We support occupational risk prevention specialists in their practice, and any organisation that wants to set up an open testimony platform.

See how an investigation unfolds

Investigations › Town hall · workplace climate › People › Michel B.

Michel B.Cluster

This record brings together the alleged acts in which this person is named.

6 witnesses name them as the accused in 34 alleged acts.

7 witnesses describe them as the target in 18 alleged acts.

19 alleged acts name them without their role being established.

All · 78Accused · 34Target · 18Witness present · 9Role not established · 19Observed · 0No verbatim · 2

78 alleged acts, from the oldest to the most recent

2024

Accused Michel B. raises their voice in the Monday team meetings.

From March to June, six witnesses describe department meetings where the head of department goes over the week’s instructions in a curt tone, cuts people off and publicly criticises the work handed in. Two of them say they stopped speaking up there.

“The Monday meetings have been different since they arrived. We just wait for it to be over.”

Testimony 41 · Witness 07 · 14 March

2024

Accused Role not established Michel B. reprimands a colleague in front of the whole team.

On 2 April, in a department meeting, an officer who was asking for extra time was told in front of everyone that their work was not up to the expected standard. Two witnesses report the scene in the same terms; none of the managers present intervened.

“They said it in front of everyone. Nobody reacted, and we moved on to the next item.”

Testimony 118 · Witness 07 · 2 April

The organisations that have trusted us

Anact, Agence nationale pour l’amélioration des conditions de travail

How an investigation unfolds

You have run a quantitative survey? Here is what happens, step by step.

Step 1: Import

CSV, Google Forms, Typeform, Sphinx… you drop in the export

Whatever tool did the collecting: they all export a table.

What you no longer do

Copying responses one by one into a working document.

investigations / town hall · workplace climate 2026 / import

climat-social-2026.csv · 312 testimonies

Column selected: “What would you like to say about your working conditions?”

Step 2: Processing

Every testimony goes through the same grid

What you no longer do

Rereading the testimonies over and over to work out which ones deserve your attention.

processing · 312 testimonies

Anonymisationconsistent pseudonyms across the whole investigation

Summarythe key points of each testimony

Categorisationharassment coding framework · 7 families, 44 categories

Fact extractionwhat happened, when, and in which testimony

312 testimonies processed · 148 dated facts extracted · ready to read.

Step 3: Reading

You move through the testimonies by theme, by person, by period, and so on.

Every testimony carries its labels. You combine what you are looking for and land on the testimonies concerned.

What you no longer do

Searching a spreadsheet to find out who said what.

testimonies · 312 · filtered: 27

Family: working conditionsPeriod: March — JuneCategoryPersonDated facts only

Witness 07Testimony 4114 March

Monday meetings tense since a change of team; contradictory instructions.

Work organisationInstructionsDated fact

Witness 12Testimony 6321 March

Workload redistributed without notice; two departures not replaced in the department.

WorkloadStaffing

Witness 07Testimony 1182 April

Remark repeated in a meeting in front of the team; no reaction from management.

Working relationshipsIn publicDated fact

Step 4: Categorisation

Building your own coding framework

Inductive categorisation: themes surface from the testimonies, you set aside, you name, everything is recoded.
By hand: you create your families and your categories yourself.
Ready-made grid: the coding frameworks shipped with the software.

What you no longer do

Categorising every testimony by hand.

testimony 118 · categories

Witness 07 · 2 April

Working relationshipsIn public Dated fact+ add a category

Harassment coding framework · 7 families, 44 categories · editable.

Inductive analysis · themes outside the framework

Reorganisations losing their meaning34

Instructions that change from one day to the next21

Isolation of the evening teams18

Step 5: Chronology

Every dated fact, tied back to its testimony

The facts are put back in order automatically, and each one keeps the verbatim it was drawn from. That is what makes the debrief defensible: nothing is asserted without a way back to the source.

What you no longer do

Rebuilding the chronology by hand, piecing the dates together one at a time.

people / person 03 · chronology

Person 039 facts · cited in 6 testimonies · March — June

14 March

Contradictory instructions given in a team meeting.

“… the Monday meetings are different now.”

Testimony 41 · Witness 07

2 April

Remark repeated in front of the team, with no reaction from management.

“… in front of everyone. Nobody reacted.”

Testimony 118 · Witness 07

19 May

A live case withdrawn, announced by group message.

“… I found out at the same time as everyone else.”

Testimony 205 · Witness 12

The sorting is done. The investigation is yours to lead.

The software prepares the ground: it masks, it files, it dates, it sources. Interpretation, the debrief and the conclusion are not its business.

Book a demo

AI spots, dates and labels. It does not conclude for you.

Generating text on your behalf holds no interest for us, and letting an algorithm decide what your conclusions should be even less. Our tools exist to give you as much grip as possible on your testimonies: anonymising them, filing them, dating them, sourcing them. What to make of them remains your profession.

AI hosted in France

Testimonies are analysed on servers located in France, at OVHcloud, a French provider. They never leave European territory and no foreign authority can demand access to them. The models used are open source: no dependency on an American vendor.

Nothing is kept

Testimonies are analysed, then deleted. They are not read, not stored, and never used to train a model. This is a contractual commitment from OVHcloud.

Anonymised from the first pass

Anonymisation is the first processing step applied. Everything that circulates afterwards (summaries, categories and so on) is based on pseudonymised text.

More about our products

Two pieces of software, two moments: one for when an investigation is open, the other for when a group speaks up throughout the year.

InvestigationAnalyse the testimonies gathered in an investigation Open testimony platformGather what a group has to say, all year round