The system accepts PDFs, scans, spreadsheets, and emails, extracts the required fields, validates them against rules, and sends the result to Excel, an ERP, CRM, accounting system, or API.
Uncertain values do not pass silently. They enter a human review queue, and pilot quality is measured on a separate held-out test set.
The output is not OCR text, but verified fields and clear exceptions.
A pilot makes sense when documents arrive regularly, employees repeat the same actions, and the result must reach a specific system.
Document types, required fields, and the main difficult cases repeat rather than changing completely from file to file.
Employees copy fields, check completeness, and reconcile company details, amounts, or related documents.
Incorrect or missing data delays an order, payment, approval, or client service.
You know which fields and checks are needed and where validated data must go: Excel, an ERP, CRM, or API.
For the first pilot, we choose one repeatable workflow, one or two related document types, and one destination for the resulting data.
Emails, PDFs, DOCX files, scans, or photos from clients and partners.
Completeness, required fields, formats, matching company details, and duplicates.
An application record, spreadsheet row, CRM entry, or exception queue for an employee.
Invoices, completion certificates, delivery notes, specifications, and price lists from different suppliers.
Number, date, counterparty, line items, amounts, tax, required details, and discrepancies.
Validated rows for Excel, an ERP, XML, or API without repeated manual entry.
An email, free-form text, and several attachments from a client or manager.
Request classification, extraction of terms, and checks for attachments and required data.
A structured order, an assigned task, and links to the source fragments.
Intelligent document processing combines recognition, field extraction, validation, and human review of exceptions. The model does not make important decisions silently.
The system accepts a file from email, a folder, a form, or an API. Digital documents are parsed directly, while scans and photos go through OCR.
the document type is identified and its pages and tables are available for extraction.
Data is mapped to an agreed schema: company details, dates, amounts, table rows, statuses, and other required attributes.
every required field has a value, a source, and a confidence score.
Formats, reference data, and cross-field conditions are checked in code. Uncertain values enter a human review queue.
an error cannot pass unnoticed and an operator can see why review is required.
Validated data is exported to Excel, JSON, or XML, or sent to a CRM, ERP, accounting system, or internal API.
the agreed workflow receives data without another round of manual entry.
The setup depends on the client's jurisdiction, data category, and security requirements. We agree on it before any production documents are transferred.
We first review formats, fields, and validation rules using the process description and anonymized or synthetic examples.
We choose an approved region and processors that meet the client's requirements. The preferred setup is the client's own cloud account.
OCR, extraction, validation, exception review, and storage are deployed inside the client's environment.
Before processing real personal data, we define the purpose, operations, access, confidentiality, retention, deletion, and incident response. Identity documents, medical or biometric data, banking secrecy, and critical infrastructure require a separate assessment.
My strength is backend systems in which data must be received, validated, retained, processed transparently, and delivered to the next system.
I design the field schema and validation rules, build the processing workflow and exception queue, and configure integrations, logs, access, and deployment. I design critical stages as a controlled workflow, with validation checks, a decision log, a held-out test set, and human review of uncertain values.
I first check whether the task is suitable using a process description and anonymized examples. I then build a paid pilot in 10 business days and measure its result.
One or two related document types, up to 15 standard fields or one agreed table, validation rules, and an output format.
Up to 100 examples in total. A portion is reserved in advance as a held-out test set that the system does not see during setup.
File intake, OCR or direct parsing, extraction, validation, an exception queue, and the agreed export.
Accuracy for every field, the share of documents processed without human intervention, exception volume, and processing time.
We record what already works, which limitations remain, and what a production implementation will cost.
Short answers about accuracy, models, personal data, and the pilot.
Not always. A cloud environment can be placed in a region that satisfies the client's jurisdiction and data requirements, preferably in the client's own account.
A local model and deployment inside the client's infrastructure are needed when required by security policy, data category, or a prohibition on external processing.
Not without separate approval. We first determine the data category, approved processors, and region. The solution may use an approved regional cloud or the client's own infrastructure.
No. That is why the system combines extraction, deterministic checks, and human review of exceptions. Accuracy is measured separately for every important field on a held-out test set.
One repeatable workflow, an outcome owner, the list of required fields, up to 100 examples in total with typical and difficult cases, and a clear destination for the data.
A process description and a few examples without personal data are enough for the first discussion.
No. I first review the process description and anonymized examples and tell you whether the task is suitable for a pilot.
The pilot is a paid working phase. It produces a bounded workflow and measures quality on a held-out test set. It is no longer a presentation prototype, but it is not yet a production implementation with full SLA coverage, every integration, and all production security requirements.
Yes. The pilot normally uses one simple output format so we can validate data quality first. A production integration with an ERP, CRM, accounting system, or internal API belongs to the next phase and is estimated for that specific system.
Identity documents, medical and biometric data, banking secrecy, critical infrastructure, government systems, difficult handwriting, and streams with dozens of unrelated document types require a separate security and technical assessment.
Describe the document types, volume, required fields, and data destination. I will tell you whether the task fits a pilot and what is needed to start. Production documents containing personal data are not required for this assessment.
I will review the task and usually reply within one business day. If it requires a closer review, I will get back with a short assessment within 1-2 business days.