01
Document ingestion and chunking plan
RAG Development Services
RAG development services for trusted internal knowledge workflows.
We design retrieval-augmented generation systems that connect AI outputs to your source material. The work includes data preparation, retrieval quality, permission boundaries, and evaluation.
Search signal
rag development services: 70 US searches/month, 7 KD
Make policies, contracts, records, and technical documents searchable with source-backed answers.
Reduce hallucination risk by grounding outputs in controlled content.
Create a reusable retrieval layer for agents and internal copilots.
Use cases
Where this work fits.
Moonveil combines product engineering, data pipelines, and applied AI evaluation so RAG systems can move beyond demo search.
Internal policy and SOP search
Clinical or operational document review
Financial research over filings and reports
Support knowledge bases
Engineering and product documentation assistants
Deliverables
What you get at handoff.
02
Retrieval architecture and ranking strategy
03
Answer interface with citations
04
Quality checks, failure cases, and handoff notes
FAQ
Common questions.
What content can a RAG system use?
Common sources include PDFs, policies, contracts, records, tickets, knowledge bases, databases, and internal web pages.
How do you measure RAG quality?
We test retrieval relevance, citation accuracy, answer completeness, refusal behavior, and known failure cases against representative user questions.
Can RAG support AI agents?
Yes. RAG often becomes the knowledge layer for an agent, while the agent handles workflow steps, tool calls, and handoff logic.
Moonveil AI Inc.
Start with a narrow workflow and a measurable pilot.
Related services