Culture and film
Tax-incentive simulator
and return per title
Turns reputation into a case
for investors.
A film is financed before it exists. Whoever puts up the money decides on the project, on the people behind it, and on the reputation of both.
The platform reads that reputation title by title and sets it beside the figures: which incentives a project can apply for and what return can be estimated for it. The production company goes to an investor with both.
What the platform reads
The platform reads what is published openly about each title and about the company that produces it: trade press, general media, review sites, social networks, and the answers of AI assistants. It also follows the conversation around genres and markets, where the audience for a title takes shape before its release.
Sources the platform reads
Media, social, search engines, AI assistants, and proprietary data.
60 min
shortest data refresh cycle
What a production company gets
Perception by title
Each title is treated as a brand of its own. Its profile follows how it is perceived, how the press covers it, and how interest moves between announcement and release.
Professional profiles
A production company’s standing and that of the people who work with it move together. The platform reads what is published openly about the directors, performers, and crew linked to each title.
Markets by territory
A title is not received the same way in every country. For the markets that matter to the production company, the platform reads how film is watched there and which media carry weight.
Tax-incentive simulator
It combines the financing sources a project can draw on, public incentives, funds, and private investment, and shows what each combination covers. The rules it applies are the published ones for the incentives configured with the production company.
Return per title
For each title it estimates the return for each financing source and the order in which the investment is recovered. It is an estimate, and it is presented as one.
Investor materials
The reading of each title and the simulator’s figures go into the materials the company presents to investors and co-producers. The company reviews them and sends them itself.
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.
Which project to present,
and to whom.
With that reading, the production company decides which project to present, to whom, and with what arguments, and where to concentrate the promotion of each title.
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 does the simulator calculate?
It combines the financing sources a project can draw on, public incentives among them, and estimates the return for each title. It applies the published rules of the incentives configured with the production company and does not replace tax advice.
Does it work for a single title or for a whole slate?
For both. Each title has its own profile and its own figures, and titles can be compared with one another.
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 do we know where each conclusion comes from?
Every fact has a source, and the recommended decision is traceable to the source data. 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.