Ground your AI in truth.
RapidData builds retrieval-augmented generation (RAG) systems that ground LLMs and agents in your governed enterprise knowledge — so answers are accurate, current and citable, not hallucinated.
Accurate AI starts with retrieval.
RAG development connects large language models to your own knowledge so they answer from your governed documents and data rather than guessing. The result is accurate, current and citable AI — the foundation of trustworthy enterprise GenAI and agents.
We build the full RAG pipeline: ingestion, chunking, embeddings, vector and hybrid search, re-ranking, and grounded generation with citations — all permissioned so users only retrieve what they are allowed to see.
We evaluate retrieval quality continuously and tune the pipeline for accuracy, latency and cost, then deploy and operate it on your infrastructure.
RAG Pipeline Engineering
We build robust ingestion-to-answer pipelines tuned for your content and use case.
Ingestion & chunking
Parse and structure documents and data sources.
Embeddings & vector search
High-quality semantic and hybrid retrieval.
Re-ranking
Improve precision with re-ranking models.
Grounded generation
Answers with citations back to source.
Permissioned & Governed Retrieval
Retrieval respects access control so users only see what they're entitled to, with full audit.
Access-aware retrieval
Enforce permissions at retrieval time.
Freshness
Keep the index current as knowledge changes.
Quality evaluation
Measure retrieval precision and recall.
Audit
Log sources used for every answer.
Deploy & Operate
Deploy on your infrastructure and operate with observability and cost control.
Deploy anywhere
Cloud, on-premise or sovereign.
Observability
Monitor retrieval quality and latency.
Cost control
Caching and efficient indexing.
Managed run
Operate and tune under SLA.
Related capabilities & platforms.
Frequently asked questions
What is RAG (retrieval-augmented generation)? +
A technique that grounds large language models in your own knowledge: relevant documents are retrieved and given to the model so it answers from your sources rather than guessing.
Why do we need RAG? +
RAG makes AI accurate, current and citable, dramatically reducing hallucination — essential for trustworthy enterprise GenAI and agents.
Does RAG respect our access controls? +
Yes. We build access-aware retrieval so users only retrieve content they are permitted to see, with full audit.
What sources can RAG use? +
Documents, wikis, databases, ticketing, code, and other governed enterprise sources.
How do you keep the knowledge current? +
We keep the index fresh as content changes, so answers reflect the latest knowledge.
Where can it be deployed? +
On AWS, Azure, GCP, on-premise or a sovereign-cloud region.
Make your AI answer from your knowledge.
Talk to our team about a RAG pipeline grounded in your governed enterprise data.