Case study
Multilingual documents and searchable scans
Two internal products built around different document jobs: translating files across languages while keeping their layout usable, and turning scan-first PDFs into searchable document output.
Client
Internal products, Ascent Innovate Software
Status
Live internal products
Category
SaaS Development & MVP Upgrades
Timeline
Current
We built two internal document-processing products instead of treating translation and OCR as one generic file utility. One handles multilingual translation across common office and document formats while keeping the translated output usable in its original layout. The other focuses on scanned and image-based PDFs, where the first job is recovering text that users can search and continue working with.
The translation product, AI TranslateDocs, gives users a self-serve path from document upload and language selection to translated file download. The OCR product stays separate because its workflow begins earlier in the document lifecycle: recognizing text that is not yet available as machine-readable content.
Product context
The two products solve different points in the document journey. Translation has to keep the file usable after the language changes; OCR has to recover text from documents that begin as images.
Document processing is not one problem. Translation can produce correct words while leaving the file difficult to use if layout or document structure is lost. OCR has a different starting point: the text may exist only inside a scanned page or image. The products therefore needed separate processing paths and separate definitions of a successful output.
The common layer is file processing, but the user job changes after intake. Translation preserves a document while changing its language; OCR turns image-based content into text that software and people can search and reuse.
The translation workflow accepts common document and office-file formats, while the OCR workflow is centered on scanned and image-based PDFs. Each path begins by preserving the file context needed for the output that follows.
Users choose a target language, process the uploaded document, and receive translated output designed to remain usable without rebuilding the file manually after translation.
Scan-first PDFs can be converted into searchable, text-ready documents so information that was previously locked inside page images becomes easier to find and work with.
The translation workflow supports direct self-serve use, while the OCR product also exposes an API path for larger or system-connected document conversion needs.
Users can translate working documents across languages without treating the output as plain extracted text, while scan-heavy PDFs can move into a searchable form that is easier to inspect and reuse.
Keeping the products separate also keeps their operating promises clearer. Translation is judged by whether the document remains usable after the language changes. OCR is judged by whether image-based text becomes available for search and downstream processing, with an API path when the workflow needs to move beyond individual uploads.
The value came from what these decisions changed for the people using the product and the team responsible for running it.
Translation and OCR were treated as different product outcomes, so the interface and processing path could stay focused on what the user was actually trying to do.
The translation path keeps document layout in view, while the OCR path makes scan-based text searchable instead of stopping at an image-only file.
An API route gives the OCR product a path into bulk and system-connected processing without changing the self-serve document experience into an integration console.
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