Document intelligence, built by engineers
For documents off‑the‑shelf OCR gets wrong.
Complex, multilingual, rule‑heavy documents turned into validated, structured data your systems can trust. You talk directly to the engineer who builds it.
What we do
A focused set of document intelligence building blocks, wired directly into your existing workflow.
Classification & routing
Incoming documents are scanned or ingested, classified, and routed to the correct process or review queue.
AI document interpretation
OCR and document understanding recover structure and meaning from PDFs, scans, tables, forms, and inconsistent layouts.
Structured data extraction
The exact fields your systems need, extracted and validated against your domain rules — not a generic key-value dump.
Workflow integration
Processed data flows directly into the tools you already use — APIs, exports, queues, or existing systems.
Documents we typically handle
- Invoices and delivery notes
- Contracts
- Customs declarations
- Insurance policies
- Medical reports
- Forms and applications
- Legacy archives and historical records
- Technical and regulatory documents
Built with the right tools
Right model for the job
We combine scanning, OCR, vision models, language models, deterministic rules, and domain-specific logic instead of forcing every document through one model.
Deployment that fits the data
The architecture can be adapted to data sensitivity, infrastructure constraints, cost, volume, and deployment requirements.
Human-verifiable output
Important extracted data can be validated, audited, reviewed, and traced back to the source document.
Your documents stay yours
Where it runs
On your own infrastructure (on‑premise) with locally hosted models, or in the cloud in an EU region — chosen per project based on how sensitive the data is.
No training on your data
Your documents are never used to train models, ours or anyone else’s.
GDPR and NDA
We process data under a data processing agreement (DPA), in line with the GDPR and Serbia’s Personal Data Protection Law, and sign an NDA before you share anything.
Retention
We keep documents only as long as the project needs them and delete them when it ends, or earlier on request. In production, retention follows the terms agreed in the DPA.
Case study
- sections
- 16
- pages per document
- 20+
- languages
- 30+
- per document
- Hours → < 1 min
Problem
Safety Data Sheets are complex regulatory documents with overlapping standards, inconsistent formatting, multilingual content, and many domain-specific validation rules.
System
Chemplora ingests SDS documents — extracting text via OCR where needed — identifies relevant sections, extracts data into a strict domain model, applies domain validation and consistency checks, and supports human review.
Result
What used to take hours of expert work per document now runs in under a minute; people only review what the system flags. The resulting data is structured, validated, traceable, reusable, and suitable for downstream chemical and regulatory workflows.
Source PDF — Section 2
SAFETY DATA SHEET
2. Hazard identification
GHS Classification:
Flam. Liq. 2, Eye Irrit. 2
Signal word: Danger
H225, H319
Extracted & validated
{
"section": 2,
"hazardClass": [
"Flam. Liq. 2",
"Eye Irrit. 2"
],
"signalWord": "Danger",
"hStatements": ["H225", "H319"]
}
Capabilities
- Section-aware PDF processing
- OCR where required
- Multilingual extraction (30+ languages)
- Translation memory
- Cross-section consistency checks
- Domain validation
- Full audit trail
Engineering, not marketing
Grazer ONE is run by Vuk Pavlović, a software architect who modernizes enterprise systems and puts language models to real work. He writes openly about architecture, scanning workflows, OCR and the engineering decisions behind this work.
How it works
From paper and PDFs to structured, audited data.
- 01
Tell us about your documents
Describe the PDFs, scans, or paper documents you process today, and how you handle them now. A representative example helps; we sign an NDA first and agree on a secure way to share it.
- 02
We map the domain
We identify document structure, OCR requirements, required output, business rules, validation logic, and edge cases.
- 03
We build and verify the pipeline
We combine scanning or ingestion, OCR, extraction, models, deterministic rules, and validation into a system tested against real documents.
- 04
You get structured, audited data
The result is delivered through an API, export, or direct workflow integration, with traceability where required.
How we start
The first assessment is free: you get a written answer on what can be automated and how. Next comes a paid pilot on your real documents, 2–4 weeks, with scope and price agreed upfront. You only commit to production once the pilot proves it works.
Have documents your current tools cannot reliably process?
Tell us what you’re processing. We’ll tell you what can realistically be automated, how we would approach it, and where the real limits and the hard parts are.