RAG: answers straight from your data
we build an advantage in the AI era
We create solutions that combine document search with answer generation. Search less — work faster.
We deploy RAG for businesses: a system that searches your files, wikis, and databases before answering, then composes a clear response with citations. This helps people find specifics faster and gives you confidence in the source of the content. We support Polish and English, run on-prem or in a GDPR-compliant cloud, inherit permissions (SSO/LDAP/Okta), and integrate with Slack/Teams, Confluence, and SharePoint.
What is RAG (Retrieval-Augmented Generation)?
RAG is a way of building solutions on top of language models in which knowledge is first retrieved from your documents (e.g., using a vector database), and only then does the model generate the answer.
Thanks to this, the answers are:
- more accurate – supported by real data,
- up-to-date – using the latest versions of documents,
- verifiable – always with links/citations
Key benefits of implementing a RAG solution
Less searching, more substantive work
Answers in seconds, without clicking through folders.
Up-to-dateness
RAG solutions always use the latest versions of documents.
Confidence and source traceability
Each answer indicates the documents, sections, and pages.
Secure data processing
We process data locally or in a chosen GDPR-compliant cloud.
For whom and for what purpose
- Internal knowledge base – procedures, policies, regulations, instructions.
- Customer support – quick responses to inquiries, quotes from contracts and terms.
- Sales and presales – specifications, comparisons, offer terms, RFI/RFP.
- Compliance and legal – searching for clauses in contracts and regulations with citations.
- Operations and IT – runbooks, system documentation, logs, changes.
- HR and finance – answers about benefits, settlements, requests.
Example features that we can implement
- Cited sources in the answer (link, document name, page/section number).
- Permissions and visibility – according to your SSO/LDAP/Okta.
- „Document-only” mode – no additions from the model’s „memory.”
- Confidence thresholds and alerts – when material is missing or ambiguous.
- Quality dashboard – user-entered phrases, hit rate, answer evaluation.
- Languages – Polish/English (others on request).
- Integrations – Slack/Teams, browser widget, email, API, Confluence/SharePoint, knowledge bases.
- Supported formats – PDF (text, OCR-scanned), DOCX, XLSX/CSV, PPTX, web pages, and others.
Why RAG outperforms a „pure” language model?
Classic LLMs answer based on what they memorized during training. Their knowledge becomes outdated quickly and does not include new materials. RAG combines response generation with real-time searching in your sources (files, wiki, databases). This way, you get answers based on current, verified documents with cited sources. It also reduces „hallucinations” — the model does not invent from memory but uses actual content at the moment of generating the answer.
![Comparative diagram: Regular LLM (head and neural network, downward arrow, cylindrical database, speech bubble with answer) versus RAG (head and neural network, downward arrow, document and database, speech bubble with answer including citations [1], [2], [3]). It illustrates that RAG provides answers with cited sources.](https://sysmo.app/wp-content/uploads/2025/12/RAG_schema_2.png)
RAG implementation strategies in a company
1. Selecting use cases
First, identify areas with the highest return:
- Decision support (comparisons, terms, procedures),
- Customer support and helpdesk,
- Creating reports and analyses from documents,
- Offer and product recommendations.
2. Data preparation
The quality of sources determines the quality of the answers:
- Content audit and organization (duplicates, outdated versions),
- Simple standards: structure, tags, naming,
- Regular updates and accuracy checks according to a schedule.
3. Iterative development
A step-by-step approach is recommended:
- Start in a limited area (pilot),
- Systematic collection of user feedback,
- Tuning search, confidence thresholds, and „document-only” mode.
4. Compliance and ethics
Security and transparency should be built in:
- Privacy protection, encryption, access control (e.g., SSO/LDAP),
- Clear rules for AI usage within the organization,
- Regular audits: errors, biases, logs, and data retention.
FAQ – most frequently asked questions about RAG
What is RAG?
An approach where the system first searches for passages from your documents and then generates an answer based on them. This way, you get factual, up-to-date answers with references to sources.
When does RAG make sense?
When you have a lot of materials (procedures, offers, wiki, regulations) and want people to quickly find specifics. It works well in customer support, sales, internal knowledge bases, compliance, and more.
How is RAG different from a „chatbot”?
It doesn’t rely on rigid rules or general knowledge. It searches content in your files and builds an answer with cited sources while respecting access permissions.
RAG or fine-tuning?
Often both. RAG provides up-to-date information and source traceability. Fine-tuning helps with tone, terminology, and short company-specific prompts.
What data formats do you support?
PDF (text; OCR-scanned), DOCX, XLSX/CSV, PPTX, web pages, Confluence/SharePoint, knowledge bases, and others.
What about document permissions?
The system only sees what the user has access to (SSO/LDAP/Okta). Rules are inherited from the source systems.
Do the answers include sources?
Yes. The answer includes the document name, section/page, and link.
How do you limit „hallucinations”?
„Document-only” mode, content filters, confidence thresholds, and a clear message when material is missing.
How often is the data refreshed?
We set a schedule — from manual refreshes to continuous synchronization (e.g., hourly). After content changes, the system uses the new version.
What are the model options?
We can use cloud models or locally managed solutions. The choice depends on security requirements, costs, and languages.
Take advantage of a free analysis of your company.
