01
Files arrive through many channels
Documents live in inboxes, forms, folders and systems, so the team must first find them and assign them to the right case.
AI / OCR, validation and workflow
AI document automation from extraction to a controlled workflow
We automate document OCR, classification, extraction, validation and routing with confidence thresholds, human review, operation logs and exception handling.
01a representative sample and acceptance criteria before implementation
02validation rules, confidence thresholds and human review for exceptions
03an operation log and export to the agreed business system
AI development servicesBusiness context
The goal is not an error-free autopilot. It is a controlled reduction of one document workflow.
Documents differ in format, scan quality, layout and field meaning. We therefore begin with a sample and acceptance criteria, measuring classification, each important field and cases the system should reject separately.
We combine AI with OCR, business rules, validation, an exception queue and human approval. The safe automation level depends on the consequence of an error: marketing material, invoices, contracts and medical documents require different controls.
Where value leaks
Where manual document work most often slows the process
The first useful scope comes from the real bottleneck, not from a prebuilt package.
02
The same fields are re-entered manually
Numbers, dates, counterparties, line items and statuses move into spreadsheets, CRM or ERP at the cost of time and error risk.
03
An unusual document looks valid
A system without thresholds and validation may pass incomplete data forward instead of stopping the case for review.
04
There is no correction and approval trail
Nobody can see what the model extracted, what a person corrected and which version was ultimately sent to the downstream system.
Implementation scope
A controlled document pipeline with an explicit exception path
Not every document needs the same route. Recognition, rules and review are designed around the material type and the consequence of an error.
Document sample and taxonomy
We collect representative variants, file quality, categories, target fields and cases that require separate handling.
File intake
We design secure input from an agreed form, inbox, folder or API within the company requirements.
OCR, classification and extraction
We read text, identify the document type and extract the specified fields or sections.
Validation and confidence thresholds
We combine the result with rules, dictionaries, format checks and a threshold below which a person reviews the case.
Review and approval interface
A user can inspect the source, correct fields, approve the result or reject the document with a reason.
Export, audit trail and exceptions
We send approved data to the agreed system, retain history and maintain a queue for cases requiring intervention.
Delivery
Quality is measured on the documents the company actually receives
Each stage has a decision, an output and a clear reason to move forward.
01
Sample and criteria
We define document types, fields, error risk, a representative set and the pilot acceptance condition.
02
Pilot
We build extraction, classification and validation for a limited process without extending it to every file automatically.
03
Measurement and exceptions
We score individual fields, analyse rejected cases and adjust rules using observed errors.
04
Staged rollout
We launch a monitored workflow and retain human review wherever the risk requires it.
Expected outcomes
What document automation should improve
less time spent on initial sorting and data re-entry
a consistent output format across document variants
visible confidence thresholds and a review queue
faster transfer of approved data to the correct system
a history of extraction, correction and approval
measurable quality for each critical field and document type
Document automation pricing / net prices
We estimate a pilot from a representative sample, not a promise to handle every file.
Price depends on document types, scan quality, fields, languages, validation, review interface, integrations and the consequence of an extraction error.
| Option | Price | Scope | Included |
|---|---|---|---|
| AI opportunity audit | from PLN 900 net / one-off | The typical range is PLN 900–2,500. We select the documents, fields, sample and criteria needed to assess whether a pilot makes sense. |
|
| OCR, extraction and classification | from PLN 6,000 net / pilot | The typical range is PLN 6,000–18,000 for a controlled pilot covering extraction, validation, exceptions and an agreed output format. |
|
| ERP or CRM integration | from PLN 1,500 net / integration | The single API integration threshold. Final cost depends on documentation, data mapping, authentication and rejected-record ownership. |
|
| Advanced document workflow | Custom quote agreed individually | Multiple types and languages, high volume, local or hybrid architecture, complex approvals, migration or a regulated business-critical process. |
|
Net prices in PLN. “From” means the smallest sensible first-stage scope. Advanced delivery — including multiple integrations, migrations, custom roles and permissions, local or hybrid environments, high volumes, extended SLAs or business-critical workflows — is quoted individually after discovery or an audit.
Evidence
An AI feature is not automatically a complete document workflow
We show product capabilities that are confirmed and keep them separate from OCR, business validation and approval steps that the case does not document.
Adjacent evidence / not an OCR system
Med2Be — AI working with notes and PDFs
The platform creates learning materials from a student's notes and PDFs. We do not present it as invoice processing, business classification or a document approval system.
View the documented moduleCase study placeholder
Reserved for a verified document pilot
The future case will document document types, test sample, fields, thresholds, validation, human review share and results measured using an agreed method.
View the current portfolioDecision support
Related layers of document work
Company RAG knowledge assistant
A different goal: answering questions from governed sources instead of extracting fields and routing a document.
Open resourceBusiness process automation
Broader orchestration of statuses, roles and systems before and after the document step.
Open resourceAI and automation pricing
Indicative ranges for audits, document workflows, RAG assistants, agents and ongoing care.
Open resourceFAQ
Questions before the first scope
Clear answers before technology, timing and budget are committed.
Which document types can be handled?
The answer requires a sample and information about quality, variants, languages, fields and error consequences. We do not promise support for every format without testing.
Can human review be removed after launch?
That should not be assumed. Approval depends on risk and measured quality. Many workflows work best with automated preparation and human review of exceptions.
How do you measure extraction quality?
On an agreed representative sample. We score classification, each important field, rejected cases and documents the system incorrectly accepted.
Must the data be sent to the cloud?
Not always. We choose cloud, hybrid or local architecture after reviewing data type, company policies, infrastructure, model quality and operating cost.
Can you integrate the output with ERP or CRM?
Yes, after reviewing APIs, field mapping, permissions and failure ownership. Data should reach the system only after the required validation and approval.
How much does document automation cost?
An AI opportunity audit starts from PLN 900 net. An OCR, extraction and classification pilot starts from PLN 6,000, with a typical range of PLN 6,000–18,000. ERP or CRM output integration starts from PLN 1,500. Complex or regulated workflows are quoted individually.
Next step
Describe the document types, volume and current approval path
Tell us where files arrive, which fields are re-entered, where the data goes and who approves it. Do not send sensitive documents at the first enquiry stage.
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