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Entertainment & Media

From a product line to an AI-hosted broadcast - image × video × lip-sync in one pipeline.

AI landing builder, voice-synthesis AI, lip-sync AI, background-audio AI, and video-edit AI orchestrated on a DAG autonomous execution engine.

Overview

Content, ads, and broadcasting are stitched together by people - planners, designers, and video teams each use their own tools. A single image or video generator alone cannot connect brand consistency, multilingual output, multi-format delivery, and performance feedback.

AICLUDE wires 4 image providers, 3 video providers, voice-synthesis (TTS) AI, lip-sync AI, and the AI landing builder into a single DAG autonomous execution engine. It does not compete with existing video/image tools - it runs as the orchestration layer above them.

Key Capabilities

4 Image Providers

Multiple image AI models in parallel. Auto-conversion of prompts, negatives, and aspect ratios.

3 Video Providers

Multiple video AI models in parallel. Auto-generate from script → scene → video.

AI video host (TTS + lip-sync)

Voice-synthesis AI + lip-sync AI + background-audio AI + video-edit AI auto-generate AI shopping and broadcast hosts.

5-stage AI landing builder

5 stages — planning, design, code, images, editing — with the AI landing builder plus a drag-and-drop visual editor.

DAG autonomous orchestration

The DAG autonomous execution engine runs "plan → script → video → edit → deploy" in parallel or series, autonomously.

Brand-consistency RAG

Learn brand guidelines and past campaigns via Graph N-hop Agentic RAG → compliance is verified at generation time.

Case Stories

Self-contained Application Scenarios

Every case is shown in full: Pain, AICLUDE Apply, Scenario, Impact, and Tech: without collapse.

Case 01

AI video host & virtual humans

Customer Pain

  • Shopping, broadcast, and ads need a live host every time - production cost and scheduling pain.
  • Multilingual hosts add casting and dubbing costs.
  • Every product update means a re-shoot.

AICLUDE Apply

  1. 1Auto-generate natural-language scripts from product DB and script RAG.
  2. 23 video providers (multiple video AI models) auto-generate scenes.
  3. 3Voice-synthesis AI synthesises multilingual voice.
  4. 4Lip-sync AI merges video and voice.
  5. 5Background-audio AI and video-edit AI handle background audio and post-edit.
  6. 6Native KO/EN/JA produces multilingual hosts in parallel.

Scenario

Show Me
Input · Merchandiser
"10 new items - 30-second AI host videos, 9:16 short-form"
  1. 01Step
    Query product DB
  2. 02Generate
    script auto-generated
  3. 03Generate
    3 video providers called in parallel (best per scene)
  4. 04Step
    Voice-synthesis AI
  5. 05Step
    Lip-sync AI composition
  6. 06Step
    Background-audio AI
  7. 07Step
    Aspect ratio auto-converted

Impact

  • Faster video production.
  • Multilingual hosts produced in parallel.
  • Faster response to product updates.

Tech

  • 3 Video Providers
  • Voice-synthesis AI
  • Lip-sync AI
  • Background-audio AI
  • Video-edit AI
  • DAG Autonomous Execution Engine
Case 02

Short-form & ad mass production

Customer Pain

  • Short-form and ad demand are spiking - variants needed per segment, campaign, and channel.
  • Designers and video teams build them manually every time.
  • A/B variant cadence is low.

AICLUDE Apply

  1. 1Call 4 image providers + 3 video providers in parallel.
  2. 2DAG autonomous execution engine plans "plan → content → edit → deploy" with parallel execution.
  3. 3Multi-LLM router (5 providers) generates channel-specific copy tones separately.
  4. 4Publishing integration to email, web, and app channels.

Scenario

Show Me
Input · Marketer
"Fall promo - 3 banner variants × 2 short-form variants"
  1. 01Execute
    DAG autonomous execution engine plans the run
  2. 02Generate
    Image AI generates 3 variants in parallel
  3. 03Generate
    Video AI generates 2 short-form variants in parallel
  4. 04Generate
    Per-channel copy tones generated separately
  5. 05Step
    Scheduled publication to 3 social channels

Impact

  • Faster content production.
  • Higher A/B variant cadence.
  • Per-channel tone stays consistent.

Tech

  • 4 Image Providers
  • 3 Video Providers
  • DAG Autonomous Execution Engine
Case 03

Multilingual content localisation

Customer Pain

  • Global content needs scripts, voice, and subtitles per language all at once.
  • Translator-based pipelines lose context and nuance.
  • Brand tone drifts across languages.

AICLUDE Apply

  1. 1Native KO/EN/JA prompts (not a translator layer).
  2. 2Voice-synthesis AI synthesises voice per language.
  3. 3Lip-sync AI produces per-language lip-sync (mouth shapes match).
  4. 4Brand RAG keeps tone consistent across languages.
  5. 54 image providers swap in local imagery automatically.

Scenario

Show Me
Input · Content team
Korean ad
  1. 01Step
    localise to EN/JA
  2. 02Step
    Source script natively re-written (not via a translator)
  3. 03Step
    EN/JA voice synthesis with voice-synthesis AI
  4. 04Step
    Per-language lip-sync via lip-sync AI
  5. 05Validate
    Brand RAG verifies tone compliance
  6. 06Step
    3 language versions finished together

Impact

  • Faster multilingual content production.
  • Better contextual accuracy than translator pipelines.
  • Brand tone stays consistent.

Tech

  • Native KO/EN/JA
  • Voice-synthesis AI
  • Lip-sync AI
  • Brand RAG
  • 4 Image Providers
Case 04

Brand-consistency RAG

Customer Pain

  • At scale, brand tone and CI consistency are the hardest part.
  • External and internal teams interpret differently - output drifts.
  • Single-prompt control yields low consistency.

AICLUDE Apply

  1. 1Learn brand guidelines and past campaigns via Graph N-hop Agentic RAG.
  2. 2Referenced automatically at generation time - tone, colour, fonts, vocabulary stay consistent.
  3. 38-stage Fortress Pipeline scores brand-compliance rate.
  4. 4Low-compliance outputs auto-regenerate at the auto-correction stage.

Scenario

Show Me
Input · Team
upload Brand Guide v3.2
  1. 01Retrieve
    Graph N-hop Agentic RAG splits and learns at proposition level
  2. 02Generate
    Referenced automatically for every subsequent image, video, and copy generation
  3. 03Analyze
    Each output gets a brand-compliance score
  4. 04Step
    Below threshold
  5. 05Generate
    auto-correction stage regenerates it

Impact

  • Higher brand compliance.
  • Less external review effort.
  • New hires apply the tone instantly.

Tech

  • Graph N-hop Agentic RAG
  • Fortress Pipeline 8 stages
  • Auto-correction stage

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