We turn printed and scanned documents into output your service can use directly. Multimodal recognition, Human-in-the-Loop review, and automated delivery — delivered as one operational workflow.
Multimodal AIHuman-in-the-LoopLayout-aware
Beyond Recognition Accuracy
It's not how well it's recognized — it's how well it's used.
Model accuracy numbers like 90% or 95% don't create business value on their own. What matters is the quality of what your users actually receive — whether for search, RAG, or publishing.
That's why we take ownership above the model, not just the model itself. We draw a clear line between what can be automated and what should be reviewed by humans, and connect the two cleanly.
We deliver clean text, organized illustration assets, and structured metadata — accurately and fast. Search, RAG, publishing — extracted data is ready to be served in whatever form you need.
“Recognition accuracy alone doesn't make a business work. The workflow above the model, and the output it produces — that's where we take ownership.”
— CEO & Chief Architect, RightStack
What sets the output apart
Structured layout recognition
Recognizes document-specific structure as a unit — footnotes, Hanja ruby annotations, chapter titles, page numbers. Body content and ancillary elements come out separated and placed correctly.
Illustration detection and extraction
Detects illustration regions in body content and extracts them as separate assets — clean separation of text and image.
Human-in-the-Loop review
We provide a review workflow alongside the automated recognition, so humans can validate and correct the output. Automation efficiency without sacrificing output quality.
Region-level re-recognition
Re-process only the regions flagged in review, with reviewer feedback as guidance. More accurate results without restarting the whole job — and lower cost.
Push extracted text and assets straight into S3-compatible object storage. No intermediate file movement — downstream consumers read the output as soon as it lands.
Case Studies
Iseum NEW Nonsul Classics 100 — automated digital publishing
Problem
Iseum's NEW Nonsul Classics 100 series (Heidi, Daddy-Long-Legs, and other classics) needed to be converted from print to digital editions. Forty volumes — around 100 pages each — had to be onboarded on a tight schedule, preserving titles, subtitles, embedded illustrations, and other formatting variants exactly as in the print edition.
Our Solution
We built a multimodal AI OCR pipeline that automatically recognizes and extracts footnotes, ruby annotations, titles, page numbers, and illustrations, with a Human-in-the-Loop review workflow on top. The pipeline was wired so that review corrections flow directly into the final output. The digital onboarding throughput improved by 30–40%.
