RAG Systems & Knowledge Bases
RAG Systems That Connect Your LLM to Live Internal Knowledge
Retrieval-augmented generation pipelines that connect your LLM to live internal knowledge: documentation, CRM data, product catalogues, support tickets, and more, so answers are grounded in your actual data.
74%
Of tickets resolved without a human agent
91%
CSAT held post-deployment
4,000+
Tickets/month handled by one deployment
6 weeks
Typical MVP deployment
Why "just uploading your docs" doesn't work
Production RAG is engineering, not a file upload
Retrieval-augmented generation gives an LLM access to your external knowledge base at query time, so the model can answer questions about your product, your clients, or your processes with accurate, up-to-date information instead of relying solely on what it learned in training.
Building a production-quality RAG system is significantly more complex than dropping a PDF into a chatbot interface. Chunking strategy, embedding model selection, retrieval quality, context window management, and freshness all need deliberate engineering decisions.
Chunking
Split to preserve semantic meaning
Embeddings
Matched to your domain and queries
Hybrid search
Dense + sparse retrieval, re-ranked
Freshness
Re-indexed on a schedule or trigger
What's included
Every layer of a production RAG pipeline
Chunking Strategy Design
Documents split to preserve semantic units, not naive paragraph breaks that fragment meaning.
Embedding Model Selection
The model that vectorises your text, matched to your domain and how your users actually query.
Hybrid Search & Re-ranking
Dense + sparse retrieval combined with a re-ranking step, since cosine similarity alone surfaces irrelevant results.
Context Window Management
Retrieved chunks fit within the model's context limit without losing the surrounding context that makes them interpretable.
Freshness & Sync Pipelines
Your knowledge base is a live document. We re-index updated content on a schedule or trigger, automatically.
Vector Database Deployment
Pinecone or Weaviate deployed and tuned for your scale, latency, and hosting requirements.
Ecommerce: Support Automation
74%
of tickets resolved without a human agent
We deployed a RAG-powered support system for a D2C brand handling 4,000+ tickets a month. It retrieves from order data, product documentation, and policy pages to resolve order tracking, returns, and product queries autonomously, escalating only complex cases. CSAT held at 91% post-deployment.
FAQ
Common questions about RAG systems
Uploading a PDF to a generic chatbot interface skips the engineering work that makes retrieval reliable: how you chunk documents, which embedding model you use, whether retrieval surfaces the actually-relevant passage, whether that passage fits the model's context window with enough surrounding information to be interpretable, and whether the index stays current as your source documents change. Naive RAG implementations often retrieve confidently wrong context, which is worse than no context at all.
We deploy Pinecone and Weaviate most often, chosen based on your scale, latency requirements, and whether you need a managed service or self-hosted control. For smaller knowledge bases or rapid prototypes we sometimes use lighter-weight options. The vector database is one piece of the pipeline: chunking strategy and retrieval quality tuning matter more than which database you pick.
Your knowledge base is a live document, not a one-time upload. We build re-indexing pipelines that sync on a schedule or trigger, so when a document changes, gets added, or gets archived, the retrieval index reflects that within your defined freshness window, whether that's real-time, hourly, or nightly, depending on how often your source content changes.
We've built RAG systems for product documentation, internal support knowledge bases, sales enablement libraries, legal document Q&A, and internal policy search, each requiring a different retrieval architecture. A support ticket knowledge base has different freshness and chunking needs than a legal document corpus, and we architect accordingly rather than applying one template everywhere.
Ready to ground your LLM in your own data?
Tell us what knowledge base you want your AI grounded in. We'll scope a RAG pipeline for it in one call.
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