RAG implementation for production AI.
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.
Common questions
RAG & Knowledge Systems, answered.
What is retrieval-augmented generation?
RAG is a pattern where a model answers from documents you supply rather than from what it memorised during training. The system retrieves the relevant passages first, then generates an answer grounded in them. Done properly it also returns the sources, so a reader can check the answer instead of trusting it.
Why does RAG work in a demo and fail in production?
Demos use clean documents and questions the builder chose. Production brings scanned PDFs, near-duplicate policy versions, tables that lose their structure on extraction, and questions nobody anticipated. Most production failures are retrieval failures: the right passage was never fetched, so the model filled the gap with something plausible.
How do you stop a RAG system from making things up?
Three layers. Retrieval that reliably surfaces the right passage, hybrid vector and keyword search rather than embeddings alone. Grounding that forces every claim back to a retrieved span. And a refusal path, so the system says it does not know instead of improvising. None of it is verifiable without measurement, which is why we build an evaluation set alongside the pipeline.
Do we need a vector database?
Not always. Postgres with pgvector handles a large share of production workloads without adding another system to operate, and we published a benchmark comparing it against a managed vector database. A dedicated vector store earns its place at high scale or when you need features Postgres does not offer. Start with what your team already runs.
How long does a production RAG build take?
A working pipeline over your documents takes weeks, not months. Getting it accurate enough for regulated users takes longer, and that time goes into ingestion quality, chunking, retrieval tuning and evaluation rather than into the model. Budget for the second half, since that is where most projects underestimate.
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
