Intelligent document data extraction, entirely on-premise and without GPUs.
Quantum DocLens is an intelligent document processing and data extraction engine that turns scanned PDFs into structured data. It brings document identification, classification and accurate data extraction to servers inside your own data centre, with no GPU and no internet connection. Any value DocLens is unsure of goes to a reviewer who can review results using an extremely friendly user interface.
Customer documents cannot go to the cloud, poor data extraction quality on-premise so the work stays manual and error prone.
Traditional OCR engines are not accurate enough on their own, so most products add machine learning or vision language models (VLMs). Those need either internet access or a large GPU server before they are fast and accurate enough to use.
Data residency law, the regulator’s outsourcing rules or the institution’s own policy keep many organisations from sending customer documents to cloud AI extraction services.
The documents themselves make it harder. A fixed-format form is easy, because each field sits in the same place on every copy. Many documents are not like that. In a company credit report, the shareholders table may fill a few lines for one company and run onto the next page for another, making data extraction extremely cumbersome.
So the work stays manual and error-prone. An account application, a loan file or an insurance claim arrives as one scanned bundle: the signed form plus certificates and supporting documents, in whatever order the scanner took them, with some pages sideways or skewed. Somebody opens the PDF, finds the right pages and types the values into the onboarding, loan or claims system, one field at a time.
From a scanned PDF to reviewed, structured data in one pass.
A PDF arrives from a user in the web workspace, or an application through the REST API. DocLens identifies every page, even upside down, sideways or skewed, classifies the documents in the bundle and intelligently extracts the fields your process asks for.
It checks each value before accepting it and flags any it is unsure of for review. The calling system then receives structured JSON with the evidence behind each value.
No cloud and no GPU, but still very fast and accurate extraction
Most OCR products read text. DocLens knows what a document is, where its fields sit, and how sure it is of each value, and it does all of that without a GPU or an internet connection.
Your data stays on your servers
No internet, no GPU, no cloud calls. Documents and extracted data never leave your data centre, so DocLens fits where data residency law, outsourcing rules or internal policy keep cloud services out.
Intelligent & accurate extraction
Every value is parsed, corroborated and checked before it is accepted. A value DocLens is unsure of is marked uncertain and sent to a reviewer. It is never passed off as extracted.
Fast and accurate without GPUs
Written in native C++, DocLens is fast and very accurate on standard CPU servers. There is no GPU hardware to buy or wait for.
Fits your systems and controls
Your onboarding, loan or claims systems submit PDFs and collect JSON results through a REST API, each with its own key. Role-based access controls what each user can do in the workspace, and every sign-in and action is logged.
Made for real scans
Pages can arrive in any order, mixed across documents, upside down, sideways or skewed. DocLens reads fixed forms, certificates and multi-page reports, including tables, checkboxes and complex data formats.
New document types without code
A new document type, be it a form, certificate or report is added by configuration without needing a code change and server downtime.
Reviewers look at the doubtful fields, not the whole bundle.
Every value comes back with a status (extracted, blank or uncertain), the page and position it was read from, an image of that spot and a confidence score. Fields DocLens is unsure of are flagged, with the image beside the extracted value, so a reviewer confirms or corrects each one at a glance instead of hunting through the bundle.
Every correction is recorded with who made it, so any value can be traced back to the page it came from and the person who approved it.