Sample set
100+ real documents, ground truth fields.
Scans and PDF → structured fields → accounting system. No manual requisites entry.
B2B OCR is about structured extraction, not plain text. Bober AI Systems builds: image → OCR → field parser (rules + LLM) → validation → export to accounting.
OCR text extraction case: batch processing for accounting — minutes per batch vs hours manual entry. Confidence score drives auto-post vs review queue.
Typical invoices and acts: template detection and regex. LLM fallback for non-standard layouts. Hybrid lowers cost and raises accuracy.
On-prem OCR (Tesseract, PaddleOCR, commercial SDK) for NDA. Yandex Vision cloud if compliance allows.
Integrates with document-processing workflow: OCR is first step before approval and payment.
MVP 3–4 weeks. Budget from €3,000.
100+ real documents, ground truth fields.
Preprocessing, language, table detection.
Rules engine + LLM structured output.
1C API, review UI, accuracy monitoring.
−85%
primary document entry time
99%+
accuracy on standard invoices after tuning
3–4 wks
MVP pipeline

Документы обрабатываются пакетно за минуты вместо часов ручного ввода, снижается доля ошибок при переносе данных и ускоряется закрытие периода.
View case study →
Пакет 50 счетов за 10 минут вместо 4 часов, −90% ошибок ввода.
View case study →Scans and PDF → structured fields → accounting system. No manual requisites entry.