Your AI is only as good
as your data
Poor data = hallucinating AI. We prepare your data for AI so it responds accurately and without errors. Regardless of format or where it's stored.
99% accuracy * Any data format * Centralized in one place
From chaos to accuracy in 4 steps
It doesn't matter where or in what format your data is. We process anything and prepare it for AI.
Data source audit
We map all your data sources - websites, documents, databases, emails, internal systems, RSS feeds, external applications, open data.
Extraction, cleaning, unification
We extract data from any format, remove duplicates, fix errors and unify structure.
Splitting and enrichment
We split data with optimal strategy and add metadata, summaries and keywords. This results in significantly better retrieval for any subsequent AI operations.
AI knowledge base integration
We can save the resulting data and upload it directly to your required system, knowledge base or vector database (e.g. Microsoft Azure, OpenAI, Qdrant, Pinecone, Voiceflow, etc.)
We process any format
PDF, Word, Excel, PowerPoint, CSV, JSON, XML, HTML, Markdown, emails, databases, APIs, RSS, OpenData, documents...
90% of AI problems start with data
Investing in AI but results don't meet expectations? The problem isn't the model or prompts. The problem is the data you're feeding your AI.
Scattered data
Data is scattered across Excel, PDFs, websites, databases, emails. AI can't find the right answer when it doesn't know where to look.
Duplicates and inconsistencies
Same information exists in 5 places in 5 different versions. AI then returns contradictory or outdated answers.
Hallucinations and inaccuracies
AI makes up facts because it works with incomplete or poorly structured data. Clients lose trust.
The difference between failure and 99% accuracy
See how data looks before and after our preparation. Quality structure = quality AI responses.
Poor quality data
Unstructured, duplicate, no context. AI hallucinates.
Úřední hodiny pondělí 8-17 úterý zavřeno
středa 8-12 a 13-17 Úřední hodiny: Po
8:00-17:00, Út: zavřeno, St: 8-12, 13-17
ÚŘEDNÍ HODINY pondělí osmá až sedmnáctá
Otevírací doba: Po 8-17 městský úřad
otevřeno od 8 do 5 odpoledne v pondělí
úterý je zavíračka středa půlden a pak
znovu od jedný hodiny odpoledne kontakt
tel. 123456789 nebo email info@mě...Prepared data
Clean, structured, with metadata. AI responds accurately.
{
// Vektorově vyhledatelná pole
"searchableFields": {
"rag_question": "Jaké jsou úřední hodiny městského úřadu?",
"content": "Úřední hodiny: Po 8-17, Út zavřeno, St 8-12 a 13-17",
"source_page_summary": "Kontaktní stránka MÚ",
"current_chunk_summary": "Otevírací doba úřadu",
"overlap_summary": "...kontaktní údaje a adresa"
},
// Filtrovatelná metadata
"metadataFields": {
"source_url": "mestsky-urad.cz/kontakt",
"category": "úřední hodiny",
"date_int": 20250115,
"language": "cs",
"chunk_index": 3
}
}
Text is not cut off mid-sentence. AI receives complete information and doesn't have to guess what follows.
AI knows exactly where to look for answers and what is just auxiliary data. No more shots in the dark.
Each piece of text has associated questions it answers. AI finds the right answer even if the user asks differently.
AI immediately understands the context. It doesn't have to read the whole document to understand what a specific piece is about.
Each block knows what came before it. AI understands context even if information is split across multiple parts.
Date, category, source. AI can search exactly where it should. "Find in documents from 2024" - done.
Even a small snippet of text knows where it came from. AI can cite the source and you know it's not made up.
How to properly split data for AI
Chunking (splitting text into smaller parts) is key for quality RAG. We use 4 strategies based on content type.
Token-Based
Basic splitting by fixed token count with overlap.
Header-Based
Respects document structure by headers (H1, H2...).
Semantic
AI analyzes meaning and splits by topics.
Agentic/LLM
LLM intelligently analyzes and creates optimal chunks.
Clean and Structured Data — the Core of Successful AI
A quality AI assistant is only as good as the data you feed it. RAGus.ai is our proprietary admin panel that serves as the central brain for all your AI products. It ensures your knowledge base is always up-to-date, clear, and accurate.
Independent Knowledge Editing
Clients can improve and correct the chatbot themselves via the admin panel without any programming required.
Transcripts and Rating
Ability to browse conversation history and mark successful or unsuccessful interactions for further learning.
Sentiment and Trend Analysis
Categorization of most common queries and monitoring user satisfaction in real-time.
Choose your way of collaboration
Professional service or self-service platform. Depends on your needs and capacity.
Professional Service
Complete turnkey data preparation. We do it for you.
Hourly rate for smaller projects
Flat rate per data source
-
Analysis and audit of all sources -
Extraction from any format -
Cleaning, structuring, enrichment -
Integration into your knowledge base
Self-service: RAGus.ai
Our SaaS platform for those who want to prepare data themselves.
Starter subscription
-
One clear dashboard for all your AI projects -
View and rate conversations in real-time -
Clear statistics and automatic reports -
Helpdesk for escalated and complex queries -
Automatic knowledge base synchronization -
Integration: OpenAI, Voiceflow, Pinecone, Qdrant -
4 chunking strategies including AI -
Feedback and custom AI training
Frequently asked questions
I want quality AI data
We'll analyze your data sources and propose the optimal solution. 30-minute consultation free of charge.
Schedule a free consultation
30-minute call with no obligation
Prefer direct contact?