Prepare Documents for a Secure RAG Assistant in Poland

Start here: the minimum to make documents RAG-ready and safe

To prepare company documents for a secure retrieval‑augmented generation (RAG) assistant in Poland, you need to do five things: define scope and access rules; identify and protect sensitive data (like PESEL, NIP, REGON); convert files into clean, searchable text; chunk content with useful metadata; and test for relevance and leakage before rollout. If you skip any of these, your assistant risks hallucinating, giving outdated answers, or exposing information to the wrong people.

In practice, start with one business domain (for example HR policies or IT helpdesk), apply your company’s identity provider for access control, run a redaction pass for personal data, normalize the text so Polish diacritics and tables are preserved, and index with consistent metadata (owner, date, confidentiality, version). Then run a closed pilot with real queries and measure what the assistant retrieves and reveals.

Scope and access: who can see what, and how to enforce it

Pick a narrow scope for the first iteration, such as HR policies, software procurement, or quality procedures. List the exact repositories you will include (e.g., SharePoint libraries, Confluence spaces, a network share). For each repository, write down the business owner, data steward, and the intended audience (e.g., All Employees, HR only, Finance only).

Enforce access using your identity provider (Azure AD/Microsoft Entra, Google Workspace, Okta) and role‑based access control (RBAC). The safest pattern is to filter results at retrieval time using the user’s group memberships, not just at index time. Avoid building a separate, ad‑hoc permission model inside the assistant; instead, mirror the source systems’ ACLs so a file that is restricted in SharePoint remains restricted through the assistant.

If you operate under GDPR/RODO, coordinate early with your IOD/DPO (Inspektor Ochrony Danych / Data Protection Officer). This article gives general guidance; verify your access model and data categories with your internal policies and applicable law.

Spot and protect Polish personal and company identifiers

RAG pipelines often ingest mixed content where personal and company identifiers are common. In Poland, watch for PESEL (national ID), NIP (tax ID), REGON (statistical ID), KRS (company register number), IBAN bank accounts, personal emails, phone numbers, home addresses, and signatures. These may appear in contracts, invoices, HR documents, and attachments scanned as PDFs.

Decide what the assistant may disclose. Typical options are: block disclosure entirely; partially mask (e.g., show last 4 digits); or allow only to specific roles. Implement at least two layers: a preprocessing step that redacts or tags sensitive items, and an answer‑time filter that prevents the model from quoting blocked patterns back to the user.

Use pattern recognizers tuned for Polish formats (for example, PESEL structure and length, NIP with optional separators) and supplement with dictionary-based recognizers for local entities (street names, voivodeships). For uncertain matches, store the item as sensitive metadata rather than deleting content, so authorized users can still retrieve with proper controls.

Make clean text: OCR, diacritics, tables, and dates

Your assistant is only as good as the text you feed it. Convert scans and complex PDFs with OCR that supports Polish. Tesseract with the pol traineddata or cloud OCR services with Polish language packs will handle diacritics (ą, ę, ł, ń, ó, ś, ż, ź) better than generic settings. After OCR, normalize encoding to UTF‑8 and de-hyphenate words split across line breaks so retrieval matches user queries.

Preserve structure. Keep headings, lists, and table boundaries because they improve chunking and relevance. For invoices (including KSeF exports), capture line items as structured text or key‑value pairs rather than a single blob. For spreadsheets, export relevant ranges to CSV/Parquet and store a link back to the source file and tab.

Normalize numbers and dates for consistent search. Record a machine‑readable form (YYYY‑MM‑DD, dotless thousands separators) alongside the original Polish formatting (e.g., 1 234,56 zł, 31.12.2026). Keep both in metadata to support precise filtering and natural‑language answers.

Structure for retrieval: chunking, metadata, and versions

Chunk content so each piece answers one sub‑question without losing context. A common baseline is 400–800 tokens with 10–20% overlap, but adjust by document type: longer chunks for narrative policies, shorter for FAQs and procedures. Avoid mixing access levels inside a single chunk; if a file has restricted and public sections, split them before indexing.

Attach consistent metadata to every chunk. Minimum set: title, source system, URL or file path, owner, document type, language (pl/en), confidentiality level, effective date, revision/version, and allowed audiences or groups. Include a hash of the source so you can detect duplicates and safely re‑index only changed content.

Keep a canonical path and a current/obsolete flag. Many Polish organizations store multiple copies of the same PDF across SharePoint, email archives, and network drives. Deduplicate, prefer the canonical location, and mark older copies as superseded so the assistant cites the latest version.

Integrations you might need in Poland-based stacks

Plan connectors for the systems you actually use rather than crawling everything. Common sources in Poland include Microsoft 365 (SharePoint, OneDrive, Teams), Atlassian Confluence/Jira, Google Drive, ERP suites (e.g., Comarch ERP Optima, enova365, SAP), service desks (Jira Service Management, ServiceNow), and document repositories for contracts and quality management.

For finance documents, consider integrating e-invoice exports and archives. If you process KSeF data, work through your accounting or ERP integration and follow your firm’s compliance guidance; verify any handling of ePUAP or PUE ZUS exports with your legal/compliance team before ingestion.

Use vendor APIs where available and respect existing permissions. Avoid flat-file dumps that lose ACLs and version history. If you must export, carry over group memberships as metadata and enforce them at retrieval time.

Evaluate relevance and leakage before rollout

Create a small, realistic test set: 50–100 typical questions users ask today, plus 10–20 red‑team prompts designed to elicit sensitive data (e.g., requests that try to extract PESEL, payroll figures, or supplier bank details). For each question, note the correct source document and an acceptable answer range.

Measure three things: retrieval precision (are the right documents in the top 3–5?), answer faithfulness (does the answer stick to retrieved content?), and leakage resistance (does the assistant refuse to disclose blocked data and cite policy). Adjust chunking, embeddings, and metadata until top‑k results are consistently relevant.

Add guardrails: a policy pre‑prompt that explains what the assistant can and cannot disclose, output filters that block sensitive patterns, and a fallback that cites a policy page instead of answering when content is missing. Log queries and citations with user IDs under your retention policy; review early usage for drift and access anomalies.

Example workflow: from raw folders to a safe internal assistant

Scenario: A mid‑size manufacturer in Poland wants an internal assistant for HR and IT policies in Polish. Scope is two SharePoint sites (HR Polityki, IT Procedury) and one Confluence space (Helpdesk FAQ). Audience is All Employees; a subfolder with payroll procedures is HR‑only.

Step 1: Connect via Microsoft Graph and Confluence APIs; mirror ACLs. Step 2: Run OCR on legacy PDFs with Polish models; normalize to UTF‑8, fix diacritics and hyphenation. Step 3: Detect PESEL/NIP/IBAN; redact in HR‑public content, preserve masked variants for HR‑only with field‑level tags. Step 4: Chunk at section level (avg. 600 tokens), metadata includes effective date and version. Step 5: Index embeddings; enable Polish language detection and synonyms for HR terms. Step 6: Build a 80‑question test set from existing helpdesk tickets; add 15 red‑team prompts targeting payroll fields. Step 7: Pilot with 30 users; monitor citations and denial rates; update three outdated policies found during testing; roll out to the company with a short usage guideline.

If you need help designing the pipeline, vendor selection, or guardrails, Auranik’s AI & Automation team in Poland can assist with a secure, audit‑ready setup that fits your stack.