The problem
Large organizations sit on enormous internal knowledge bases — policy manuals, procedure libraries, historical case files, product documentation, contract repositories, internal wikis, research archives. Finding a specific answer inside that knowledge is a daily, expensive problem.
The obvious solution — a retrieval-augmented AI assistant that answers questions from the corpus — has an equally obvious problem. Most RAG systems in the market are built on public AI APIs, which means every query and every retrieved document fragment traverses a third-party service. For commercially sensitive knowledge, legal matter content, regulated data, or internal strategic documentation, that architecture is disqualifying.
Skyview Labs builds RAG systems that don’t have this problem.
What we build
A typical private RAG engagement delivers a conversational interface that lets a defined user population — internal staff, regulated clinicians, customer service teams, legal professionals, state agency employees — ask questions in natural language and receive answers grounded in the organization’s own documents.
The system is designed around three non-negotiables:
1. Data stays in the private cloud. Documents are embedded, indexed, and retrieved inside our infrastructure. Queries are processed by self-hosted language models in the same environment. No document content is transmitted to a public API.
2. Answers cite their sources. Every response includes links back to the underlying documents, so users can verify and so legal or compliance teams can audit what the system said and why.
3. Access mirrors your real permissions. If a user shouldn’t be able to read a document directly, the RAG system doesn’t retrieve it for them. Permissions are enforced at the retrieval layer, not bolted on as an afterthought.
Architecture
Ingestion. Documents flow from source systems — SharePoint, file shares, document management systems, case management platforms — into our private cloud. We handle cleaning, chunking, and enrichment.
Embedding and indexing. A vector database inside our perimeter stores embeddings alongside structured metadata. Permissions, document provenance, recency, and access classifications are preserved at the index layer.
Retrieval. When a query arrives, we retrieve candidates, apply permissions, re-rank based on relevance and recency, and prepare a response context.
Generation. A self-hosted language model generates the response grounded in the retrieved context. For workloads that benefit from a higher reasoning ceiling on specific query types, we route selectively to external reasoning APIs — transparently, and with the client’s knowledge of exactly what content flows where.
Audit. Every query, every retrieval, every generation is logged for audit. Compliance teams can answer “what did the assistant tell whom, based on what documents, on what date” months or years later.
Where it fits
- Legal teams searching historical matter content without sending document text to a third-party API.
- Regulated healthcare operations querying clinical protocols, formularies, or patient history within HIPAA-appropriate infrastructure.
- State and local government making policy, regulation, and historical decision archives searchable for constituent-facing staff.
- Internal policy and compliance functions that need staff to find the right answer in a 10,000-page policy corpus, not the nearest answer.
- Customer service operations grounding AI assistants in product documentation and historical case resolutions.