Home Services Solutions Industries Technologies Case Studies Resources Blog About Get Free Consultation

AI development cluster

RAG Development

Retrieval-augmented generation is how a chatbot or assistant stays honest: it looks up, then it writes. AppsAura builds RAG when you have documents, tickets or product data that change, and you need answers that can show a source. If the corpus is ten pages, a static FAQ may be enough — we will say so.

India-based global teamStartups, SMBs and enterprises worldwide
Architecture-firstBuilt to scale cleanly
Transparent deliveryMilestones you can track
Confidential intakeNo obligation consultation

Service overview

Chunking, embeddings, metadata filters and re-ranking are the job. Access control is the job people skip: a vector store that ignores roles will leak. We keep document ACLs in the filter, not as a hope. We measure recall on real questions, not a demo query the author wrote.

Problems we solve

  • One giant chunk size that loses tables and headings.
  • No ACL on retrieval — intern sees finance PDFs.
  • Stale indexes after a CMS publish.
  • Citations that point at the wrong paragraph.

What we build

  • Ingestion pipeline with versions and deletes
  • Metadata filters (role, product, language, date)
  • Hybrid search when keyword still wins
  • Citation UI that a human can check
  • Eval harness on a question set you own
  • Rebuild and incremental update paths

Benefits

  • Answers that can be audited.
  • A corpus that product and legal can reason about.

Use cases

  • Internal runbooks for operations software.
  • Help centre grounded chat.
  • Policy documents next to a Vistarx-style compliance workflow.

Technologies

PythonPostgreSQLAWSNode.js

Industries this shows up in

Relevant case studies