Deploy private, secure AI systems that index your proprietary documentation — giving your team instant, cited answers grounded strictly in your own data.
RAG combines a language model with a retrieval layer over your private knowledge base — so the AI answers from your verified documents, not from general training data it may have hallucinated.
We begin by inventorying the documents, databases, and knowledge repositories your RAG system needs to cover: formats, access controls, update frequencies, and the business questions the system is expected to answer. This shapes every subsequent architectural decision — from how documents are chunked to what access control model is required.
We also identify the quality issues that will affect retrieval accuracy before they become production problems: duplicate content, inconsistent terminology, outdated documents that should be excluded, and gaps in coverage that will cause the system to acknowledge it cannot answer rather than fabricating a response.
Raw documents — PDFs, Word files, spreadsheets, wikis, Salesforce records, Slack archives — are ingested, cleaned, and split into chunks appropriate for embedding and retrieval. Chunking strategy is one of the most consequential architectural decisions in a RAG system: chunks that are too large reduce retrieval precision; chunks that are too small lose the context needed for accurate answers.
We apply format-specific preprocessing for each document type and attach rich metadata to every chunk — source document, section, date, author, access control tags — so the retrieval layer can filter by metadata before semantic ranking and the model can cite its sources precisely in every response.
Each chunk is converted to a high-dimensional vector embedding that captures its semantic meaning — allowing the retrieval layer to find relevant passages based on conceptual similarity rather than keyword matching. Embedding model selection is calibrated to your domain: general-purpose models work well for broad knowledge bases; domain-specific models can improve retrieval precision significantly for technical or legal content.
The vector index is deployed in your cloud environment — your documents never leave your infrastructure. We select and configure the vector database appropriate to your scale and query patterns, whether that is Pinecone, Weaviate, pgvector, or a cloud-native option.
When a user submits a query, the retrieval layer searches the vector index for semantically similar chunks, optionally combined with keyword search in a hybrid retrieval pattern that captures both conceptual and exact-match relevance. The top candidates then pass through a reranking model that scores them for relevance to the specific query — filtering noise that pure vector similarity retrieves but that does not actually answer the question.
This two-stage architecture — retrieve broadly, then rerank precisely — significantly improves answer accuracy compared to naive single-stage retrieval, particularly for complex multi-part questions where the relevant context is distributed across multiple document sections.
Retrieved context is injected into a structured prompt that constrains the language model to synthesize answers exclusively from the provided passages — preventing hallucination by removing the model's ability to generate from training data when retrieved context is available. Every response includes citations mapping each claim to its source document and passage so users can verify answers independently.
Document-level access controls are enforced at retrieval time — users only retrieve documents they are authorized to access, regardless of query. Automated ingestion pipelines monitor source systems for new and updated documents and re-index them incrementally, keeping the knowledge base current without manual intervention.
Any use case where you need an AI to answer questions accurately from your specific documents rather than from general knowledge is a RAG candidate.
Instant answers from internal documentation, policies, procedures, and institutional knowledge — replacing hours of searching shared drives.
Support bots that answer accurately from your product documentation, FAQs, and historical ticket resolutions rather than generating generic responses.
Natural language queries against contract libraries, identifying specific clause language, obligations, and risk provisions across large document sets.
Instant answers to regulatory and compliance questions grounded in your current policy documents, with citations to the specific provisions being applied.
Sales teams querying product specs, pricing history, case studies, and competitive positioning from a single interface without hunting across systems.
Engineering and operations teams querying architecture docs, runbooks, API references, and incident postmortems through natural language rather than keyword search.
Retrieval-Augmented Generation (RAG) combines a large language model with a retrieval system over your private knowledge base. When a user asks a question, the system retrieves the most relevant documents from your data, injects that context into the model prompt, and generates a grounded answer — not one fabricated from training data. The result is an AI that knows your business.
RAG is ideal for internal knowledge search, customer support automation, document analysis, contract review, technical documentation querying, compliance question answering, and sales enablement. Any use case where you need an AI to answer questions accurately from your specific documents rather than from general knowledge is a RAG candidate.
PDFs, Word documents, PowerPoint presentations, spreadsheets, HTML pages, Confluence and Notion wikis, Salesforce records, email archives, Slack message history, and database tables. We handle preprocessing, chunking, embedding, and index maintenance for all supported formats.
RAG architectures constrain the model to answer only from retrieved context. We also implement citation tracking so every answer includes the source document and passage it was derived from — users can verify claims directly. For high-stakes applications, we add confidence scoring and human review queues for low-confidence responses.
We build automated ingestion pipelines that monitor your source systems for new or updated documents and re-index them incrementally. Changes to a source document propagate to the retrieval index within minutes, not days. The system stays current without manual intervention.
Yes. The vector database, document store, and inference infrastructure all run in your cloud environment. Document-level access controls ensure users can only retrieve documents they are authorized to see. Queries and responses are logged within your environment for auditability — nothing leaves your infrastructure.
A focused single-domain RAG deployment — one knowledge base, one interface — typically takes three to six weeks. Multi-domain systems with complex access control, custom UIs, or integration into existing platforms take six to twelve weeks. We deliver a functional prototype within the first two weeks so you can validate retrieval quality on your actual data before committing to full production.
Stop watching your team spend hours hunting through shared drives for answers that already exist in your documentation. Let us build the system that surfaces them instantly.