0%
RAG DEVELOPMENT

Answers grounded in your actual data .

RAG (retrieval-augmented generation) is what stops LLMs from making things up. Dezvo builds production RAG pipelines — chunking, embeddings, vector stores, hybrid search, re-ranking, citation-backed answers — for SaaS, support bots, internal knowledge tools, and ceramic catalog Q&A.

See Our Work
What we build
  • Document ingestion pipelines
  • Vector stores (Pinecone / pgvector)
  • Hybrid search (BM25 + semantic)
  • Re-ranking with Cohere
  • Cited answers with sources
RAG PIPELINE

Every layer of a production-grade RAG stack.

Ingestion

Parse PDFs, web pages, Notion, Confluence, Google Drive, S3. Smart chunking by structure not byte count.

Vector store

Pinecone, pgvector, Weaviate, Qdrant. We pick the right one for your scale and budget.

Hybrid search

BM25 + semantic + metadata filters. Re-rank with Cohere. Top-K that actually contains the answer.

Cited answers

LLM generates with source citations. Click to verify. No more guessing if the bot is making it up.

FAQ

Common questions, answered.

If your question isn't here, message us — usually same-day reply.

RAG retrieves your data at query time and passes it to the LLM as context. Fine-tuning bakes knowledge into the model weights. RAG is cheaper, faster to update, and handles real-time data. Fine-tuning is for style and tone. For 90% of knowledge use cases, start with RAG.

Pinecone for managed simplicity. pgvector if you already have Postgres. Weaviate or Qdrant for self-hosting at scale. We pick based on your data volume, query patterns, and ops capacity.

Incremental indexing — only changed docs get re-embedded. Background jobs via Inngest or Trigger.dev. No full re-indexes.

RAG works beautifully for ceramic catalogs — index SKU descriptions, technical specs, certifications. Buyers ask 'show me 600x1200 GVT in marble look' and get accurate, filterable answers with images.
RELATED SERVICES

Bundle the services that work together.

AI Agent Development

Agents use RAG as one of their tools for grounded answers.

Explore
AI Chatbot Development

Chatbots powered by RAG over your knowledge base.

Explore
LLM Integration

The underlying LLM layer that RAG feeds context into.

Explore
Currently accepting projects

Ready to get started?

Tell us where you're at. Scope, quote, and timeline back within 24 hours.