AI that creates, moderates, and recommends content at scale.
We build AI systems for streaming platforms, publishers, and media companies - from recommendation engines that reduce churn to content moderation pipelines that scale with your library.
What we build
Content Generation & Summarisation
LLM-powered pipelines that generate episode summaries, article abstracts, push notification copy, and social media content - reducing editorial overhead by 60–80%.
Recommendation Engines
Personalised content recommendation systems using collaborative filtering, content embeddings, and real-time session context to increase watch time and reduce churn.
Content Moderation
Multi-modal AI moderation pipelines for video, image, and text - flagging policy violations, hate speech, and explicit content at scale with human-in-the-loop review workflows.
Metadata & SEO Enrichment
Automated metadata generation, tagging, captioning, and keyword extraction from video and audio content - improving discoverability and reducing manual editorial time.
Search & Discovery
Semantic search across content libraries - enabling natural language queries, contextual recommendations, and cross-format discovery for readers and viewers.
Ad Intelligence
AI-powered contextual ad placement, audience segmentation, and campaign performance prediction - improving ad relevance without relying on third-party cookies.
The challenges that matter at scale
Content volume
Media companies publish thousands of pieces of content per day. AI that doesn't scale to production volume isn't useful.
Multimodal pipelines
Video, audio, image, and text require different model architectures - orchestrated into a unified content intelligence layer.
Real-time personalisation
Recommendation systems must respond in under 100ms while processing real-time session signals across millions of concurrent users.
Rights and licensing
AI-generated content must navigate complex rights and licensing constraints - we build governance into the generation pipeline.
Our media AI stack
Media AI - impact at scale
80%
Reduction in editorial overhead from AI-assisted content generation
< 100ms
Recommendation engine response time under concurrent peak load
3.2×
Watch-time increase from contextual personalisation on streaming platforms
99.1%
Moderation precision on explicit content at 1M+ items/day throughput
Architecture
Content intelligence pipeline
How raw media assets become personalised, moderated, searchable content across your platform.
Step 01
Ingest & Transcribe
Video and audio assets ingested via S3 or CDN. Whisper-based transcription generates time-coded transcripts in 60+ languages with speaker diarisation.
Step 02
Metadata Enrichment
CLIP-based visual scene classification, named entity recognition, topic tagging, and content rating prediction run in parallel on each asset.
Step 03
Moderation Gate
Multi-modal moderation checks for policy violations, hate speech, and explicit content. Items exceeding threshold automatically queued for human review.
Step 04
Embedding & Index
Dense embeddings generated from transcript + visual + metadata combined. Indexed in Pinecone or Weaviate for sub-100ms semantic retrieval.
Step 05
Personalisation Layer
Real-time session context combined with collaborative filtering signals to generate ranked recommendation lists per user per session.
Step 06
Ad Placement Signal
Contextual brand-safety score and topic vector output to ad decisioning layer - enabling contextual targeting without third-party cookies.
Editorial transformation
What changes for editorial teams
Client result
“Our editorial team was spending 60% of their time on metadata and summaries. Now they spend that time on actual journalism. Watch time is up 3.2× and content discoverability is transformational.”
Chief Technology Officer
Regional streaming platform, 14M subscribers