RAG that retrieves the right context.
Retrieval-Augmented Generation pipelines that go beyond basic vector search. Hybrid retrieval, re-ranking, metadata filtering, and citation tracking — so your AI answers with sources, not hallucinations.
Answer accuracy with citation
Query-to-answer latency
Documents indexed per pipeline
Reduction in support ticket volume
What we build
Knowledge systems that actually work.
Hybrid search
Vector similarity + BM25 keyword search + metadata filtering. We combine retrieval strategies to maximize recall without sacrificing precision.
Document ingestion pipelines
PDFs, Confluence, Notion, SharePoint, Slack, email. We parse, chunk, embed, and index your documents with the right strategy for each source.
Citation & provenance
Every answer links back to source documents with page numbers. Your users verify, your legal team relaxes, your AI stays accountable.
Re-ranking & filtering
Cross-encoder re-ranking, MMR diversity, permission-aware filtering. We ensure the most relevant chunks surface — not just the closest vectors.
Enterprise knowledge bases
Multi-tenant, role-based access, incremental indexing. Knowledge systems built for organizations with real security and compliance needs.
Conversational RAG
Context-aware multi-turn conversations over your documents. The system remembers what was asked, understands follow-ups, and cites consistently.
Sound familiar?
RAG problems we solve every month.
“Our chatbot hallucinates answers that sound right but are completely wrong.”
We implement retrieval grounding with citation tracking. When the system doesn't have a source, it says so instead of making things up.
“We have 50,000 documents but our search returns irrelevant results.”
We build hybrid retrieval with re-ranking. Vector search for semantics, keyword search for specifics, cross-encoder re-ranking for precision.
“Our RAG prototype works on 100 docs but falls apart at scale.”
We architect for production — incremental indexing, chunking strategies that preserve context, and caching that keeps latency under 2 seconds at scale.
Tech stack
Tools we use in production.
Ready to build
Let's build RAG that gets it right.
45 minutes with our RAG engineers. We'll assess your document corpus, evaluate retrieval strategies, and design a pipeline that actually finds what your users need.
AI projects we delivered
