Phone photos from your customers, scans, PDFs, emails, handwriting. A BrilliantFlux system checks the shot on the spot, reads the fields you need, tests them against your rules and files the result into your CRM, ERP or DMS. People only see the cases that need a person.
The samples above are examples. The same pipeline handles whatever arrives from your customers' phones, your inbox, scanner or shared drive.
The case that runs in production today. You send a link, the customer photographs each required document on the phone. Every shot is checked before it is accepted, fields are read and confirmed by the customer, and your staff see a queue of complete cases instead of an inbox of attachments. Missing or rejected pages go back to the customer with one click, and the loop closes without email ping-pong. Insurance claims and dependent eligibility, KYC and onboarding, visa and civil-status paperwork, tenant screening, loan applications.
Supplier, totals, VAT, IBAN, PO matching, approval limits. Duplicates and changed bank details flagged before payment.
Passports, IDs, residence permits, proof of address. MRZ reading with checksum validation, expiry checks, consistency across the documents of one case.
Parties, term, renewal, fees, liability and non-standard clauses. Reminders before auto-renewal. Search across the whole archive.
Intake forms, questionnaires, delivery notes, inspection sheets. Handwritten fields with confidence scores, low ones go to a person.
Bank statements, price lists, lab results, multi-page tables that OCR breaks. Table structure recognition is our published specialty.
Classify incoming email and attachments, extract what matters, route to the right person or system, answer the routine ones.
Your documents are not on this list? They rarely are. Describe the flow on a call and we tell you which parts automate and which do not.
Book a callFour stages, each one auditable. Every field knows which pixels it came from.
Guided photo capture, scan or PDF intake. Quality checks catch blur, poor light and cut-off pages before they become bad data.
Page type, layout, tables, stamps, signatures, handwriting. Own models trained on your document set, run on CPU or your GPU.
Key-value extraction, table cells, cross-document consistency. Your business rules run on every result. Low confidence goes to a human.
Results land in your CRM, ERP, DMS or spreadsheet with an audit trail. Corrections your team makes become training signal.
Each check runs on every photo in the production platform. The customer sees the verdict in a second and retakes on the spot. Nothing here is a promise, it is what runs today.
Every extracted field carries a confidence score. You set the threshold per field, per document type. Above it, straight through. Below it, a person confirms in a two-click review screen. Drag the threshold to see the trade-off on a typical month of invoices.
Illustrative curve. The real one depends on your documents.
One platform used every day since June 2026. Client named on the call, not on the website.
The client's customers photograph passports, IDs, certificates and forms on their phones in three languages. The system computes which documents each case needs, guides the photo, checks quality, reads the document, asks the customer to confirm, and hands a clean structured case to staff. Staff accept or reject page by page; rejected pages go back to the customer and the case returns when fixed. Built end to end by BrilliantFlux: models, backend, web app, CI/CD, GDPR-aware consent and retention.
Every first call asks this. A short honest list makes the rest of the page believable.
The system prepares, checks and flags. A person makes the accept or reject decision on anything below your confidence threshold. That is a feature, and your auditor agrees.
A blurred or cut-off photo is sent back for a retake at the moment it is taken. No "best effort" fields end up in your database.
Payment and e-signature stay in the tools you already have. We write the status into them, we do not replace them.
Questions in free text over thousands of documents are a different system with a local LLM. That is the Private AI direction, and the two combine well.
Want the numbers on your own documents instead of ours? Bring three real ones to the call - accuracy is measured on your documents in the pilot, before we commit to a figure.
Book a callPixel-precise table structure recognition with speculative decoding, co-authored by the founder. This is the table-reading know-how behind the "Tables & reports" use case.
Read the paper →First place in the 2025 international document-analysis competition, second in 2024 (multi-font OCR). The benchmarks the field measures itself by.
The same pipeline runs inside your network with a local LLM for questions over the archive. See the Private AI direction.
Private AI →That is the normal case, not the edge case. The production deployment above runs on phone photos of IDs and certificates in Russian, English and German. Accuracy is measured on your documents before we commit to numbers.
Generic OCR gives you text. We give you the fields you need, checked against your rules, in your system, with a confidence score and a review screen for the rest. And it can run on your own hardware, which cloud APIs cannot.
Not for extraction. The production models run on CPU at about 13 ms per page. A GPU is only needed if you add a local LLM for free-text questions over the archive.
A first working slice on your real documents in 3–6 weeks. Full rollout with integrations in 2–4 months.
Fixed-scope pilot first, priced from the saving we estimate together in hours per week. Most document projects pay back within 2–6 months.
Thirty minutes. We look at them together and tell you honestly what accuracy to expect and how many hours a week it frees.
Book a call with Artem