RAG & Document AI Systems
AI that answers from your own documents and data, accurately and with citations, so your team and customers get instant answers instead of digging through files.
When the answer is buried across hundreds of documents, people stop looking, or guess. I build RAG (retrieval-augmented generation) systems that turn your own documents, wikis, and data into an AI that answers instantly and cites its sources, so the answers are trustworthy. Whether it's an internal knowledge assistant for your team or a customer-facing bot grounded in your product docs, it learns from your data and won't make things up.
What I build for document AI
Knowledge base ingestion
Pipelines that ingest your docs, PDFs, sites, and data into a vector database, kept in sync as they change.
Cited, grounded answers
Responses drawn from your content with source citations, so staff and customers can trust them.
Internal & customer-facing bots
An internal assistant for your team, a support bot for customers, or both, on web, Slack, or WhatsApp.
Hybrid LLM routing
Smart model routing that balances quality and cost, keeping API spend low at scale.
Access control & privacy
User isolation and permissions so people only see the information they're allowed to.
What document AI delivers
Cited
Answers grounded in your data
Instant
Answers from hundreds of docs
Lower
API cost via hybrid routing
Private
Per-user access control
RAG & document AI FAQs
What is RAG and why does it matter?
RAG (retrieval-augmented generation) lets an AI answer from your own documents instead of just its training data. It retrieves the relevant passages first, so answers are accurate, current, and can cite sources, not hallucinated.
What kinds of documents can it learn from?
PDFs, Word docs, spreadsheets, web pages, help centers, and database records. The system ingests them into a vector store and keeps it updated as your content changes.
Can it be private and access-controlled?
Yes. I build multi-user systems with proper user isolation and permissions, so people only see the documents and answers they're authorized to access.
How do you keep API costs under control?
With hybrid LLM routing, where cheaper models handle routine queries and stronger models handle the hard ones, plus caching and efficient retrieval, so quality stays high and spend stays low.
How much does a RAG system cost?
Most document-AI builds fall in the AI Agent Build range (from $5,000), fixed-price. Book a free automation audit to scope yours.
Ready to put rag / document ai to work?
Book a free automation audit. I'll map your highest-impact win and give you an honest, fixed-price scope. No pressure.
Book a Free Automation Audit