RAG & Knowledge Systems
Your Documents, Instantly Queryable
Stop searching. Start asking. We build retrieval systems that turn your documents, wikis, and databases into intelligent knowledge bases — with grounded answers and source citations.
Why RAG
Grounded answers
No hallucinations — responses cite sources
Your data stays yours
On-premise or private cloud deployment
Always current
Real-time indexing, not stale training data
Domain expertise
Tuned for your terminology and context
Quick Answers
What is RAG?
Retrieval-Augmented Generation. Instead of relying solely on what a model learned during training, RAG retrieves relevant documents at query time and uses them to generate grounded, cited responses.
How is this different from ChatGPT?
ChatGPT uses general training data and can hallucinate. A RAG system uses YOUR documents and cites its sources. When it says 'According to the Q3 report...', it's actually reading your Q3 report.
What documents can you index?
PDFs, Word docs, Confluence wikis, Notion, SharePoint, Slack channels, email archives, databases, code repositories — if it's text, we can index it.
Can it handle multiple languages?
Yes. We use multilingual embeddings that work across languages. Ask in German, get answers from English documents — or vice versa.
Where does the data live?
Your choice. On-premise, private cloud, or air-gapped deployment. We can work with your security requirements.
How accurate is it?
Depends on your documents and how well they're structured. We tune retrieval for your domain, but RAG is only as good as your knowledge base. We'll be honest about limitations during assessment.
What We Build
Not a chatbot plugin. A real retrieval system designed for your specific documents and use cases.
- Document ingestion pipeline — PDFs, wikis, databases, APIs
- Chunking strategy optimized for your content type
- Embedding model selection — domain-specific when needed
- Vector database deployment — Qdrant, Weaviate, Pinecone, or your choice
- Retrieval logic — hybrid search, reranking, query expansion
- Generation with citations — know exactly where answers come from
- Access control — who can query what
- Continuous improvement — feedback loops and retrieval analytics
Where RAG Shines
When you need answers from YOUR data, not the internet.
Internal Knowledge Base
HR policies, technical documentation, onboarding materials. Stop answering the same questions — let the system do it.
Customer Support
Product docs, support tickets, FAQ. Ground support responses in actual documentation, not model guesses.
Legal & Compliance
Contract analysis, regulatory requirements, policy lookup. Find relevant clauses across thousands of documents.
Research & Analysis
Scientific papers, market reports, competitive intelligence. Query your research library in natural language.
Technical Documentation
API docs, code comments, architecture decisions. Help developers find answers without reading everything.
RAG vs Fine-tuning vs Standard LLM
| Capability | RAG System | Fine-tuned Model | Standard LLM |
|---|---|---|---|
| Uses your data | Yes, at query time | Baked into weights | No |
| Source citations | Yes | No | No |
| Updates to data | Real-time | Requires retraining | N/A |
| Hallucination risk | Lower (grounded) | Medium | Higher |
| Setup complexity | Medium | High | Low |
| Cost per query | Medium | Low | Low |
Technical Approach
- Document processing: layout-aware parsing, table extraction, image OCR when needed
- Chunking: semantic boundaries, not arbitrary splits — respects document structure
- Embeddings: OpenAI, Cohere, or open-source (BGE, E5) — chosen for your domain
- Vector store: Qdrant, Weaviate, Pinecone, pgvector — based on scale and requirements
- Retrieval: hybrid search (dense + sparse), MMR for diversity, cross-encoder reranking
- Generation: Claude, GPT, or open-source — with explicit citation instructions
- Deployment: Kubernetes, Docker, or bare metal — your infrastructure or ours
Process
We start with your actual documents, not a generic demo.
1 week
Assessment
- • Analyze your document corpus — volume, types, structure
- • Understand query patterns — what questions do people ask?
- • Define success criteria — accuracy, latency, coverage
- • Scope technical requirements — security, integration, scale
2-4 weeks
Proof of Concept
- • Index subset of your documents
- • Build retrieval pipeline with tuned parameters
- • Test with real queries from your team
- • Measure retrieval quality and iterate
- • Go/no-go decision based on actual results
4-8 weeks
Production Deployment
- • Full document ingestion with incremental updates
- • Access control and authentication integration
- • UI or API deployment based on use case
- • Monitoring, logging, feedback collection
- • Documentation and knowledge transfer
What You Get
Working RAG System
Deployed and integrated with your documents. Query interface (UI or API) ready for users.
Ingestion Pipeline
Automated document processing. Add new documents, they're indexed and queryable.
Retrieval Analytics
What's being asked, what's being found, where gaps exist. Data for continuous improvement.
Documentation & Training
How it works, how to maintain it, how to improve it. Your team can own it.
Investment
Pricing depends on document volume, query load, and deployment requirements.
Assessment
Document analysis, requirements scoping, feasibility assessment, architecture recommendation
Proof of Concept
Working prototype on your documents, retrieval quality testing, performance benchmarks
Production System
Full deployment, integration, access control, monitoring. Quoted based on scope.
Maintenance & Support
By agreement
Ongoing improvements, new document sources, model updates, support.
What We Need From You
- Access to your document corpus — or representative sample for assessment
- Example queries — what do people actually ask?
- Success criteria — what makes this useful for your team?
- Security requirements — where can data live, who can access what?
- Integration context — what systems should this connect to?
When RAG Isn't the Answer
- ✗If your documents are poorly structured or contradictory — garbage in, garbage out
- ✗If you need creative generation, not factual retrieval
- ✗If the knowledge isn't in documents — RAG can't retrieve what doesn't exist
- ✗If you expect 100% accuracy — RAG improves grounding, not perfection
Ready to Make Your Knowledge Searchable?
Let's start with an assessment. We'll look at your documents, understand your use case, and tell you honestly if RAG is the right approach.
Schedule Assessment