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Document ingestion and chunking plan
RAG Development ServicesMoonveil system
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.
Private knowledge70 US searches/monthHandoff ready
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Private knowledge
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70 US searches/month
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Handoff ready
Pilot fit
Start where the workflow is already visible.
Moonveil combines product engineering, data pipelines, and applied AI evaluation so RAG systems can move beyond demo search.
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.
Workflow map
Use cases with source trails, reviewers, and handoff.
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Internal policy and SOP search
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Clinical or operational document review
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Financial research over filings and reports
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Support knowledge bases
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Engineering and product documentation assistants
First pilot
A narrow build path your team can actually review.
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Retrieval architecture and ranking strategy
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Answer interface with citations
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Quality checks, failure cases, and handoff notes
Related use cases
Workflows this service can support.
AI Agents for Financial Services
Research, filing monitoring, diligence, and reporting agents with source trails and human review.
Human-in-the-loop AI Agents
Design AI agents that prepare work, show sources, request approval, and escalate risky steps to humans.
Healthcare AI Workflow Automation
Patient intake, care navigation, documentation, and operations pilots built around security and review.
Related services
Keep exploring the service map.
Healthcare AI Consulting
Scope secure pilots for intake, records, care navigation, and revenue-cycle work with clear review points.
AI Agent Development
Build agents that retrieve data, call tools, prepare outputs, and hand off risky steps to humans.
Custom AI Models
Decide whether a dedicated model needs fine-tuning, post-training, RAG, or a lighter workflow layer before spending big.
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
Start with a narrow workflow and a measurable pilot.
Moonveil can scope the review gates, data flow, prototype, and production handoff.