RadioTEDUTechnologies

RTAI / Applied media intelligence

Media adapts.
So do we.

RTAI is our applied media intelligence practice. We turn emerging AI capabilities into useful, measurable tools for radio, podcasts and participation.

rtai

RadioTEDU
Applied Media Intelligence

Human-ledMachine-assistedEditorially accountable

01 / The practice

Useful intelligence
starts with real context.

Media discovery is moving across podcasts, video, voice interfaces and participatory environments. Radio contributes something durable: a live voice, a shared moment and an editorial point of view.

We explore where machine assistance can support that work. Generated output is a material to evaluate. People remain responsible for its use.

02 / Audio production

A brief becomes sound.
People decide what airs.

  1. 01

    Brief

    Editorial intent, tone and pronunciation.

  2. 02

    Synthesize

    Local QwenTTS narration and iteration.

  3. 03

    Produce

    FFmpeg processing and music ducking.

  4. 04

    Review

    Human approval before broadcast use.

RTAI Jingle prepares audio. Deterministic playout remains a separate broadcast responsibility.

03 / Agentic interfaces

Connection guide

A station.
With an interface.

Compatible assistants can discover public media context through MCP. Public reads need no account; a studio booking follows a permissioned, confirmed workflow.

Streamable HTTPhttps://radiotedu.com/mcp

Public discovery

Station status & catalogue

Now playing & programme schedule

Podcasts & content search

Focus presets & studio availability

Studio booking

Authenticated member · appropriate access · explicit confirmation · idempotency key

04 / Research directions

More capable.
Still accountable.

Editorial copilots

Context gathering and production assistance that keep judgement with an editor.

Multilingual media

Investigating how language and voice interfaces can widen access.

Metadata intelligence

Making programme context more useful for discovery and compatible assistants.

Audience signals

Studying participation with proportionate data use and measurable questions.

These are areas of investigation, not a list of released products.

A method,
not a shortcut.

01

Observe

Start with a real need in the studio, the audience or the campus.

02

Prototype

Build a focused experiment with a clear boundary.

03

Measure

Evaluate sound, latency, accessibility and operational behaviour.

04

Operate

Connect the result to a workflow that people can own.

05

Document

Share evidence, decisions and code where practical.

RTAI / Media signals

Read the shift.
Keep the context.

From the RTAI research archive. These studies cover different dates and populations; each finding retains its source and scope.

McKinsey & Company / TMT estimate / 2024

Generative AI in media

$80–130B

Estimated potential annual economic impact for the media segment. A value-pool estimate, not realized revenue.

Read the source

Reuters Institute / 48 markets / 2025

News moves toward video

52% → 65%

Social video news use, 2020–2025. Any video news use increased from 67% to 75% across the same period.

Read the source

Edison Research / US, age 12+ / 2025

Podcasts become visual

51% / 37% / 26%

Video podcast consumption: ever / monthly / weekly. Each figure refers to the US population aged 12 and older.

Read the source

Deloitte / US consumers / 2025

A shared pool of attention

About 6 hours

Average daily media and entertainment consumption among surveyed US consumers. Video, games, music and podcasts compete within that time.

Read the source

RadioTEDU Technologies

Human judgement.
Greater possibility.

Explore the systems. Read the research.
Or simply press play.

Listen to RadioTEDU