Infrastructure
Reputation cells for
executives and operators
Protects the individuals whose damage
would drag the institution down.
An infrastructure is judged by how it operates and by the people and companies that represent it. An accusation against one of its executives, or against a company that operates in it, ends up attached to the infrastructure as a whole.
The platform gives each of those identities a reputation cell of its own, so the operator learns of a problem while it is still small. A port is a clear example: the authority that manages it and the companies that work in it share one reputation.
What a reputation cell is
A reputation cell is the reading of a single identity: a person or a company whose name is tied to the infrastructure. It gathers what is published openly about that identity in the media, on social networks, in search results, and in AI assistant answers, along with the verified facts that describe it. Access to each cell is governed by the platform’s roles and permissions.
Sources the platform reads
Media, social, search engines, AI assistants, and proprietary data.
60 min
shortest data refresh cycle
What an infrastructure operator gets
Reputation cells
One cell for each executive and each operating company whose reputation is tied to the infrastructure’s. Each shows what is being published, how it is changing, and which facts support a response.
Threat classification
Each threat is classified by its origin and its nature. Its reach and its speed are measured, because each type calls for a different response.
Response plan
For each threat the platform proposes a response: with what message, by which route, and who should take part. A person reviews it and decides.
Crisis room
An incident in progress is followed in one place, with its severity, its reach, and how it evolves. The protocols for each kind of incident are prepared in advance.
Artificial activity
It identifies bots and coordinated account networks. Volume manufactured by a handful of accounts is not public concern, and the operator needs to tell them apart before responding.
Chain of custody
The evidence of each incident is preserved with a SHA-256 chain of custody. It can be presented in an audit exactly as it was collected.
How the platform works
The platform works in a cycle of seven steps: listen, normalize, understand, anticipate, decide, act, and learn. Capture of social, media, and proprietary sources is continuous, and every source is mapped to one common schema before it is analyzed. Every action feeds the next round of listening.
Six specialized AI agents analyze the same signal. One detects weak signals and volume anomalies, another classifies the emotional spectrum, and another extracts frames, arguments, and key actors; the rest cover artificial activity, verification, and the link to the historical record.
The six reports are cross-checked before they reach a person.
What to answer,
and what to leave alone.
With that reading, the operator decides which threats call for a response, who should give it, and which are better left alone.
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 an organization already gets an answer written by an AI. You need to know what it says, and which sources it draws on.
The platform audits every search result and every assistant answer, fills information gaps with original sources, and measures regularly, model by model, what the assistants answer.
Security, traceability,
and human decision
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. Data is encrypted in transit and at rest, and GDPR and Spain’s LOPDGDD are applied by design.
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
How is this different from a listening tool or a periodic report?
A listening tool delivers a mentions dashboard, which counts but does not interpret, and a periodic report arrives after the fact. The platform is software purpose-built for the organization: it captures its sources, analyzes them with specialized AI agents, and delivers a recommended decision traceable to the source data.
What is a reputation cell?
It is the reading of a single identity, a person or a company whose name is tied to the infrastructure. It gathers what is published openly about that identity and the verified facts that describe it.
Whose reputation does it cover?
The executives and the operating companies whose reputation is tied to the infrastructure’s. In a port, for example, the authority that manages it and the companies that work in it.
Does the platform publish or respond on its own?
No. The platform proposes and a person decides; nothing ships unreviewed. Silence is the default. Whatever is published, the organization publishes, through its own channels and after reviewing it.
How is the evidence of an incident preserved?
With a SHA-256 chain of custody, so it can be presented in an audit exactly as it was collected. Every action and every AI query is also 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. Work runs in two-week sprints, with a demo every two weeks and incremental deliveries you can verify.
We do not name our clients.
Confidentiality is part of the service: here we speak of industries.