Health and pharma
Employee voice
with explainable AI
Measures adoption of a cultural change
with live indicators.
A cultural change is announced in a day and adopted, or not, over many months. In between, management tends to work from impressions.
The platform reads what employees answer through the organization’s own channels and turns it into indicators that are kept up to date. Separately, it reads what is published openly about the organization, because a health or pharma organization is judged from inside and from outside.
Two readings, inward and outward
The platform takes two readings of the same organization, each from different material.
Inward
It starts from what employees answer in the surveys and open comments that the organization itself sends out, and it is built for each organization around the people analytics tools it already has. It shows how a change is being received: what is understood and what meets resistance.
Outward
It starts from what is published openly about the organization in the media, on social networks, in search engines, and in AI assistant answers. It shows how the organization is described by those who do not work in it.
Sources the platform reads
Media, social, search engines, AI assistants, and proprietary data.
60 min
shortest data refresh cycle
What a health or pharma organization gets
Employee voice
Surveys and open comments are answered through the organization’s own channels. The platform reads their tone and the topics that come up, including those no question had asked about.
Explainable analysis
Each conclusion comes with the words that support it and a confidence level. Whoever reads it can see why the platform says what it says, and disagree.
Live indicators
Adoption of the change is followed with indicators that are kept up to date, each against thresholds set at the start. An indicator that drifts can be opened down to what explains it.
Reputation outside
What is published openly about the organization is brought into one view. Media coverage is measured by reach, tone, and framing.
Alerts
A signal that crosses a threshold arrives as an internal alert, with context, history, and a recommended response.
Verification
It checks claims against traceable evidence. That gives the organization a fact base for responding quickly when a fact is called into question.
How the platform works
The platform works in a cycle of seven steps: listen, normalize, understand, anticipate, decide, act, and learn. In the inward reading, listening means reading what employees have chosen to answer; in the outward one, it is the continuous capture of media and social sources.
Six specialized AI agents analyze the same signal. One classifies the emotional spectrum, irony included, which matters when the text is an employee’s comment and not a headline. Another links each signal to the historical record, which is what allows a change to be followed over months.
The six reports are cross-checked before they reach a person.
Where the change takes hold,
and where it does not.
With that reading, management decides where to reinforce the change and what to explain again, and checks in the following readings whether it made a difference.
The same holds for the outward reading. Not every signal calls for action, and not every action should be public: each signal ends in one of three outcomes, based on thresholds.
Silence
Noise is tagged and archived. This is the default.
Internal alert
With context, history, and a recommended response.
Public action
Always previewed before it is published.
How an AI assistant
describes you.
First impressions no longer come only from a search engine. They also come from an assistant. Anyone asking about a health or pharma organization already gets an answer written by an AI, and the organization needs to know what it says and which sources it draws on.
The platform audits every search result and every assistant answer about the organization. Where information is missing, the gap is filled with original sources, which the organization publishes through its own channels and after reviewing them.
Security, traceability,
and human decision
What employees answer is data about people, so who can see it matters as much as what it says. Answers are analyzed in aggregate. Access is by invitation only, with MFA and enterprise SSO, and there are six roles with granular permissions by section. Every action and every AI query is logged, and data is encrypted in transit and at rest.
GDPR and the EU AI Act ask where each conclusion comes from and who makes the call. Here, that is built in from the start.
Every fact has a source.
The platform proposes.
A person decides.
Nothing ships unreviewed.
AI amplifies human judgment. It does not replace it.
In production in six months.
Typical schedule. Two-week sprints, a demo every two weeks, and incremental deliveries you can verify.
Months 1–2
Foundations
Discovery, architecture, corporate access, and the first modules in use.
Months 3–4
Measurement
Analysis modules, the first version of the AI, and the executive dashboard.
Months 5–6
Optimization
Early warnings, user testing, training, and launch.
Afterward
Evolution
New modules, API integrations, and handover to full autonomy.
Frequently asked questions
What does employee voice mean here?
What employees answer in the surveys and open comments that the organization itself sends out, through its own channels. The platform reads their tone and the topics that come up.
What makes the AI explainable?
Each conclusion comes with the words that support it and a confidence level. Every AI query is also logged, so the way a conclusion was reached can be reviewed afterward.
Does it also cover reputation outside the organization?
Yes, as a second reading. The platform reads what is published openly about the organization in the media, on social networks, in search engines, and in AI assistant answers.
Who decides what is done with a result?
A person does. The platform proposes and a person decides; nothing ships unreviewed. Whatever is communicated, inside or outside, the organization communicates, through its own channels and after reviewing it.
Who can see what employees answer?
Only those the organization invites, each with the role assigned to them, and always in aggregate. There are six roles with granular permissions by section, and every action is logged.
How long does it take to be up and running?
The typical schedule is six months, with the first modules in use in months 1 and 2. The analysis modules and the first version of the AI arrive in months 3 and 4.
We do not name our clients.
Confidentiality is part of the service: here we speak of industries.