0%0%When a RAG system gives wrong answers, the model is rarely the problem — the data is. Stale indexes, bad chunking, duplicate documents, lost permissions. Dezvo builds the AI data layer properly: pipelines that keep knowledge current, embeddings tuned to your domain, and vector databases that stay fast as you grow.
AI data engineering is the pipeline work that turns raw company content — documents, databases, tickets, emails — into the clean, chunked, embedded, and indexed form that retrieval systems and LLMs consume. It covers extraction, cleaning, deduplication, chunking strategy, embedding generation, vector storage, and the incremental syncs that keep it all current.
It's where most AI quality is actually won or lost: a mediocre model over excellent data beats a frontier model over garbage. If your AI assistant confidently cites a policy you retired last year, you don't have a model problem — you have a data engineering problem.
Ingestion from drives, wikis, ERPs, CRMs, and databases — scheduled and event-driven, with cleaning, dedup, and PII handling built into the flow.
Layout-aware chunking (headings, tables, clauses stay intact), domain-tested embedding models, and A/B-measured retrieval quality — not default settings.
Pinecone, pgvector, or Weaviate selected on your scale and ops preferences — with metadata filtering, namespacing, and permission fields designed up front.
Incremental updates, deletion propagation (retired docs leave the index), re-embedding on model upgrades, and freshness monitoring with alerts.