RAG Systems
AI that actually knows your business data. Custom knowledge bases, intelligent search, and document Q&A that give real answers.
What You Get
Every engagement is custom-built for your business.
Document Q&A
Chat with your PDFs, wikis, and knowledge bases. Get answers grounded in your actual data, not hallucinations.
Intelligent Search
Semantic search that understands intent, not just keywords. Find exactly what you need across thousands of documents.
Multi-Source Ingestion
Connect to Google Drive, Notion, Confluence, SharePoint, databases — we pull from wherever your data lives.
Citation & Source Tracking
Every answer comes with citations pointing back to the exact source document and section.
Who This Is For
Knowledge-Heavy Teams
Legal, compliance, and research teams that need to find answers fast across massive document sets.
Customer-Facing Support
Build AI assistants that answer customer questions using your actual product docs and FAQs.
Internal Wikis
Turn scattered company knowledge into a searchable, conversational AI that any employee can query.
RAG systems typically achieve 95%+ accuracy within 3-4 weeks. We start with your most critical knowledge base.
Book a Free ConsultationFrequently asked questions
What is a RAG system and what does it do?
A RAG system is AI that actually knows your business data instead of hallucinating. We connect a language model to your documents, wikis, and databases so your team can chat with their content, run semantic search, and get answers — each one citing the exact source document and section it came from.
How long does a RAG system take to build?
RAG systems typically reach 95%+ answer accuracy within 3 to 4 weeks. We start with your most critical knowledge base, get retrieval and citations dialed in there, then expand to more sources. Accuracy is the bar — we tune until answers are trustworthy, not just fast.
What tools power your RAG systems?
We build RAG systems on Pinecone or Supabase for vector storage, OpenAI for generation, and LangChain to orchestrate retrieval. We ingest from wherever your data lives — Google Drive, Notion, Confluence, SharePoint, or databases — so the system reflects your real knowledge rather than a one-off export.
Who should invest in a RAG system?
RAG systems fit knowledge-heavy teams and customer-facing support. Legal, compliance, and research teams find answers fast across huge document sets; support teams build assistants grounded in real product docs; and companies turn scattered internal wikis into a searchable AI any employee can query with cited, reliable answers.
How much does a RAG system cost?
Most RAG builds land in the $15k–$30k range, reflecting the data ingestion, tuning, and accuracy work involved, though focused single-source systems can come in lower. You get a fixed quote after we scope your data sources and accuracy requirements, so there are no surprises once the build begins.