AI agents don't wait for the next prompt: they are triggered by events or schedules, operate the existing functions of your application, and deliver their results to where the work happens – as an email to the responsible person, as a draft inside the system, or as a finished publication.
Our most important principle: an agent delivers drafts, not faits accomplis. Every AI intervention stays visible, and nothing becomes binding before a human approves it. Responsibility stays where it belongs – and the AI takes over the legwork.
Technically, our agents operate existing import, mapping, and analysis pipelines end to end instead of duplicating logic. The deterministic system calculates and validates; the model takes over the judgment and writing work that couldn't be automated before: deciding what is relevant, merging duplicates, writing a coherent briefing.
From automatic processing of incoming documents to reports that explain themselves to research agents that turn public sources into structured databases – we build agentic systems that arrive in your day-to-day work.
Autonomy needs guardrails
Agents deliver drafts with visible interventions – a draft revision, a briefing for review, a record with its source reference. A result only becomes binding through human approval.
Where AI results are critical, a second, independent model reviews the first one's work in context – the same review shape that has already caught real mis-mappings in a production system.
Results arrive where the work happens: as an email to the responsible person, as a draft in the system, or as a finished publication – not as yet another dashboard nobody opens.
Five patterns from our project work – from automatic document processing to a multi-agent panel.
For a major studio's production-finance team we are designing an agent in two acts: an intake agent recognises which production and document type an incoming attachment belongs to and operates the existing import and account-mapping pipeline end to end. A review agent then cross-reads the mapped revision in context, treating the automatic mapping as a draft, not a result – sign-off stays with a human controller.
The moment a budget revision or cost report is confirmed, a findings agent compares the document against the production's own history and against comparable productions – and emails the responsible controller a short findings brief. Threshold rules alone fail here: many large movements in film budgets are structural and expected. Separating noise from meaning is editorial judgment – exactly what the agent takes over.
For the member portal of a German film-industry association, an agent gathers market news from public sources on a fixed cadence and synthesises it into a market update. Any feed aggregator can collect links – deciding relevance, merging duplicate coverage, and writing a coherent briefing used to be unaffordable every single week. A human editor reviews every draft.
German film-funding decisions are public but practically unusable: national and state funding bodies publish in their own rhythm and format. Collection agents monitor the published reports, and an AI extraction step reads each heterogeneous document and emits structured funding records against a single target schema – every record keeps its source reference for auditability.
An industry association's readership ranges from small regional companies to distributors at corporate scale – one generic AI digest serves none of them. Three persona agents analyse each report independently from their own perspective, then debate it with each other like an editorial meeting. The debate is distilled into summaries and key findings per target group.
No parallel worlds
To us, an agent is not another tool next to your application but an operator of your application: it uses the same import, mapping, and analysis pipelines, the same validations, and the same permissions as your users. The deterministic system calculates and validates – the model takes over the judgment and writing work that couldn't be automated before. The result is automation that stays auditable and grows with your system instead of beside it.
