Grounded in sector needs, not technology first
The architecture is derived from media sector needs and tasks, so every component earns its place by solving a real editorial or archival problem.
AIME product 02
AIME.Architecture
For media organisations building an internal AI roadmap without wanting to design infrastructure from a blank page, AIME.Architecture is a shared media AI reference architecture: task-specific patterns for retrieval, generation, verification, and agentic media workflows, rolled up into one overall, EU-sovereign reference architecture.
It is not an abstract diagram. Each task-specific reference architecture is made operational as a reference implementation, and every implementation is reported on by AIME.Testbench across cost, quality, latency, sovereignty, and trust — so the architecture stays accountable to evidence, not just intent.
Value proposition
One architecture, derived from real media sector needs.
AIME.Architecture starts from media sector needs and the concrete tasks media organisations actually run — archive retrieval, content transformation, disinformation defence, rights and value attribution. Each task defines its own task-specific reference architecture, and those task-specific architectures map into one overall media AI reference architecture that the whole program shares.
That overall architecture is EU sovereign by design: it assumes organisations keep control over their own data, models, and infrastructure choices, rather than inheriting an architecture dictated by whichever cloud AI vendor gets there first.
The architecture is made operational through task-specific reference implementations, and each implementation is in turn measured by AIME.Testbench. That closes the loop: architecture decisions are testable, not just documented.
The problem
Why most media AI architecture work stalls.
- Every organisation designing retrieval, generation, or agentic AI infrastructure alone tends to re-solve the same architectural questions, at real cost, with no shared reference to build from.
- Architecture proposals that are never made operational stay diagrams: they do not tell an organisation whether a given implementation is actually fast, affordable, accurate, or sovereign enough to deploy.
- Sovereignty is often treated as an afterthought bolted onto a cloud-API-first design, rather than a property the architecture is built around from the start.
Differentiators
Six design choices that define AIME.Architecture.
Task-specific, then unified
Each media task gets its own task-specific reference architecture, and these map into one overall media AI reference architecture the whole program shares.
EU sovereign by design
Data, model, and infrastructure control stay with the organisations that use the architecture, not with a third-party cloud AI platform.
Made operational, not just documented
Every task-specific reference architecture is built out into a working reference implementation, so architecture decisions can be run, not only reviewed.
Reported on by the testbench
AIME.Testbench measures each reference implementation on cost, quality, latency, sovereignty, and trust, so architecture choices are backed by evidence.
Shared and pre-competitive
The architecture is co-developed and co-owned across the AIME program, so no single organisation carries the full cost of getting it right alone.
How it fits together
From sector needs to a measured implementation.
Proof points
Credibility grounded in the AIME research agenda.
Drawn from five active research streams
Agentic retrieval, liquid media generation, disinformation defence, data value attribution, and the media AI testbench each contribute a task-specific reference architecture to the overall design.
Already productised in AIME.Retrieve
The archive retrieval task-specific reference architecture has already been made operational as AIME.Retrieve, showing the path from architecture to deployable product.
Target customer
Who this is for.
Fit
CTOs, architects, and innovation leads at media companies and public broadcasters who need a credible, sovereign starting point for their AI infrastructure roadmap, and want architecture decisions backed by a testbench rather than by vendor claims alone.
Next step
Map your AI roadmap onto a shared, sovereign architecture.
We can walk through which task-specific reference architectures apply to your organisation, and how a reference implementation would be measured by AIME.Testbench. Scope and commercial packaging are confirmed per implementation context.