AI-Powered Media Intelligence and Visual Search
Built for intelligent discovery: turning complex comic universes into AI-ready, searchable knowledge
About the client
A leading global entertainment publisher with a deep catalog of illustrated storytelling, spanning decades of characters, timelines, and interconnected narrative arcs.
As new publishing initiatives launched and creative teams evolved, internal stakeholders needed faster ways to explore storylines, verify continuity, and understand previous runs without manually digging through PDFs, scripts, and internal archives.
The organization wanted to test whether modern Agentic AI and large language models could serve as a smart assistant for navigating a focused slice of their comic universe.
The Challenge
Traditional archives were built for storage, not discovery.
Teams across editorial, legal, and creative development faced familiar pressures:
- Fragmented story data: Scripts, lettering documents, PDFs, and metadata all existed in different locations and formats, with no single system that could answer basic continuity questions across runs.
- Slow internal research: Answering questions like “When does this character first appear in this event?” or “Who worked on issue X?” often meant manual searches through documents, wikis, and inboxes.
- Limited visibility across issues: With thousands of legacy pages, multiple art styles, and tight turnaround times, the team needed a system that could automate repetitive tasks without disrupting creative control.
To support faster decision making and unlock the value of their archives, the publisher needed a proof of concept that could show how AI might understand, navigate, and retrieve information from a modern comics event.
Why KoobrikLabs
KoobrikLabs works at the intersection of creative workflows and intelligent systems, with experience across comics, film, publishing, and story-driven media.
That background shapes how we build: practical, explainable AI systems that respect creative craft while delivering immediate operational value.
For this engagement, the publisher partnered with KoobrikLabs for our understanding of how story worlds are built across issues and lines, our experience with Agentic AI and cloud-scale LLM systems, and our ability to turn architecture into a working proof of concept in a matter of weeks.
Our Approach
KoobrikLabs delivered a focused AI exploration designed to show how two selected comic titles could be transformed into a searchable, structured knowledge base.
- Processed PDFs and lettering documents to extract dialogue, narration, and on-page text
- Identified characters, locations, creators, and issue metadata
- Normalised and organised content into a clean, machine-readable format
- Created a reliable foundation for AI-driven search and reasoning
- Used an AI-driven agent to interpret natural-language questions
- Combined graph lookups, text search, and contextual reasoning across the two titles
- Returned grounded, issue-level answers tied back to the underlying source materials
- Enabled flexible exploration of character appearances, plot points, and creative credits
- Delivered a simple chat interface for testing queries and reviewing results
- Captured feedback on answer quality, retrieval behavior, and expansion opportunities
- Demonstrated how the approach could scale to additional titles or larger catalogs
Searchable Storyworlds at Issue Level
Smarter, Faster Internal Research Workflows
A Scalable Foundation for Expanding the Archive
Outcomes
Structured Knowledge From Complex Materials
Previously fragmented scripts and PDFs are transformed into a unified, machine-readable dataset that supports deeper insight and discovery.
Quicker Answers to Creative and Editorial Questions
The AI-assisted interface reduces reliance on manual searches, helping teams verify continuity, credits, and story beats with ease.
Greater Trust in Content Accuracy
Responses are grounded directly in issue-level data, giving stakeholders confidence that answers reflect the actual source material.
Clear Path Toward Broader Catalog Intelligence
The POC establishes a proven method for extending AI-driven search and understanding across additional runs, eras, and content types.
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