Retrieval-Augmented Generation
Ground your LLM in your own documents and knowledge.
Cloud, on-prem, or at the edge.
Same model, same governance, same control plane — sized and operated for the environment that fits your security, latency, and cost profile.
- On-prem for full data sovereignty
- Private cloud (AWS · Azure · GCP) for elastic scale
- Edge for offline + low-latency environments
At LLM.co, we build private RAG pipelines that connect your LLMs to internal documents, wikis, policies, emails, contracts, and databases—ensuring every answer is grounded in your truth, not just the model's training data.
What is RAG?
Retrieval-Augmented Generation (RAG) combines large language models with real-time information retrieval—so the AI doesn't guess, it looks things up.
Instead of relying solely on a model's memory (which can be outdated or incomplete), RAG injects relevant, up-to-date content from your private knowledge base into each response. The result? More accurate, traceable, and business-aligned answers—even in high-stakes, compliance-sensitive environments.
Why LLM.co for RAG
Built for data-sensitive enterprises: Law firms, finance, healthcare, government.
Custom indexing tuned to your structure and metadata. Optional pairing with agentic AI, chatbots, or internal search.
Designed for modular deployment: Integrate RAG into your chatbot, Slack assistant, helpdesk, or search UI.
Smarter AI Starts With Accurate & Trusted Knowledge
RAG is how enterprises move from guessing to knowing—bridging the gap between AI and your private data. With LLM.co, your organization gains the power of retrieval-grounded LLMs that are accurate, auditable, and secure.
Reduce hallucination. Increase trust. Automate with confidence.
Key RAG Features with LLM.co
Private & Secure: All ingestion, indexing, and retrieval happens in your VPC or on-prem—no public API calls, no vendor data access.
Model Agnostic Architecture: Works with fine-tuned open-source models (like LLaMA, Mistral, Mixtral) or commercial models in your environment.
Modular Source Chunking: We intelligently segment documents to optimize retrieval relevance and reduce prompt bloat.
Seamless Integration: Connect to SharePoint, Confluence, Notion, Google Drive, file servers, or custom knowledge systems.
Email/Call/Meeting Summarization: LLM.co enables secure, AI-powered summarization and semantic search across emails, calls, and meeting transcripts—delivering actionable insights without exposing sensitive communications to public AI tools. Deployed on-prem or in your VPC, our platform helps teams extract key takeaways, action items, and context across conversations, all with full traceability and compliance.
Security-first AI Agents: LLM.co delivers private, secure AI agents designed to operate entirely within your infrastructure—on-premise or in a VPC—without exposing sensitive data to public APIs. Each agent is domain-tuned, role-restricted, and fully auditable, enabling safe automation of high-trust tasks in finance, healthcare, law, government, and enterprise IT.
Internal Search: LLM.co delivers private, AI-powered internal search across your documents, emails, knowledge bases, and databases—fully deployed on-premise or in your virtual private cloud. With natural language queries, semantic search, and retrieval-augmented answers grounded in your own data, your team can instantly access critical knowledge without compromising security, compliance, or access control.
Multi-document Q&A: LLM.co enables private, AI-powered question answering across thousands of internal documents—delivering grounded, cited responses from your own data sources. Whether you're working with contracts, research, policies, or technical docs, our system gives you accurate, secure answers in seconds, with zero exposure to third-party AI services.
Custom Chatbots: LLM.co enables fully private, domain-specific AI chatbots trained on your internal documents, support data, and brand voice—deployed securely on-premise or in your VPC. Whether for internal teams or customer-facing portals, our chatbots deliver accurate, on-brand responses using retrieval-augmented generation, role-based access, and full control over tone, behavior, and data exposure.
Offline AI Agents: LLM.co's Offline AI Agents bring the power of secure, domain-tuned language models to fully air-gapped environments—no internet, no cloud, and no data leakage. Designed for defense, healthcare, finance, and other highly regulated sectors, these agents run autonomously on local hardware, enabling intelligent document analysis and task automation entirely within your infrastructure.
Knowledge Base Assistants: LLM.co's Knowledge Base Assistants turn your internal documentation—wikis, SOPs, PDFs, and more—into secure, AI-powered tools your team can query in real time. Deployed privately and trained on your own data, these assistants provide accurate, contextual answers with full source traceability, helping teams work faster without sacrificing compliance or control.
Contract Review: LLM.co delivers private, AI-powered contract review tools that help legal, procurement, and deal teams analyze, summarize, and compare contracts at scale—entirely within your infrastructure. With clause-level extraction, risk flagging, and retrieval-augmented summaries, our platform accelerates legal workflows without compromising data security, compliance, or precision.
Common questions
01How does RAG actually reduce hallucinations in LLMs?
Traditional LLMs rely solely on their pre-trained knowledge, which can be outdated or incomplete—leading to hallucinations. RAG reduces this risk by retrieving relevant, real-world content (like internal PDFs or wiki pages) and injecting it into the model's prompt at runtime. This grounds responses in factual, verifiable information from your own knowledge base.
02What types of content can be used in a RAG pipeline?
RAG can ingest and index a wide range of documents: PDFs, DOCX, TXT, HTML, slide decks, spreadsheets, emails, support tickets, internal wikis, and more. If it contains text, we can embed it semantically and make it searchable by your LLM—securely and privately.
03Is RAG deployment secure for sensitive or regulated environments?
Yes. LLM.co's RAG pipelines are deployed inside your virtual private cloud (VPC) or on-prem environment, ensuring that no data leaves your infrastructure. We support full encryption, access control, and audit logging, making the solution safe for healthcare, legal, financial, or government workflows.
04Can we use our own LLM, or are we locked into a specific model?
You're not locked in. Our RAG architecture is model-agnostic, meaning it works with both open-source models (like LLaMA, Mistral, or Mixtral) and licensed/commercial models deployed within your environment. We tailor the system to match your performance, privacy, and compliance needs.
05What's the typical implementation timeline for a RAG system?
Most RAG systems can be implemented in 4 to 8 weeks, depending on the volume and format of data, desired integrations (e.g., SharePoint, Notion, Confluence), and complexity of the use case. Our team handles ingestion, vectorization, search configuration, and LLM integration—all within a private, secure environment.
06What is the difference between basic RAG and hybrid search RAG?
Basic RAG retrieves passages using only dense vector similarity (semantic embedding), which works well for conceptual queries but can miss exact terms or identifiers. Hybrid search combines dense vector retrieval with sparse BM25 keyword matching in a single query, then merges the ranked lists. For enterprise document corpora—where users query both by concept and by specific contract clause numbers or regulatory codes—hybrid search consistently returns more relevant context, reducing hallucinations and improving answer precision.
07How does a reranker improve RAG accuracy in enterprise settings?
After initial retrieval returns a candidate set of passages, a cross-encoder reranker scores each passage against the full original query using attention-based comparison rather than embedding distance alone. This second-pass scoring catches passages that are semantically adjacent but contextually irrelevant, and elevates passages that are highly relevant but cosine-similar to many others. The practical effect is fewer hallucinations and tighter cited answers, which matters especially in legal, clinical, and financial workflows where a wrong source reference carries real risk.
08Can enterprise RAG work in a fully air-gapped or offline environment?
Yes. LLM.co deploys RAG pipelines that operate with zero internet dependency—embedding models, vector stores, and LLMs all run on local hardware or within a private network segment. Document ingestion, query processing, and response generation happen entirely on-prem. This architecture is suited to defense, government, and highly regulated healthcare or financial environments where no traffic may traverse a public network.
09How are citations and source traceability handled in your RAG system?
Every LLM response generated through LLM.co's RAG pipeline includes structured metadata linking each claim to the specific document, page, and chunk that supplied it. Retrieval scores are preserved in the audit log alongside the query, retrieved passages, and the final prompt sent to the model. This chain of evidence allows compliance teams to verify AI outputs against source documents and satisfies the traceability requirements of governance and audit frameworks without post-hoc reconstruction.
10How does enterprise RAG differ from simply fine-tuning a model on our data?
Fine-tuning encodes knowledge into model weights at training time, which means the model cannot reflect new documents without retraining and cannot cite specific sources at inference time. RAG retrieves live content from your knowledge base at query time, so updates to your documents are immediately searchable without any model retraining. For most enterprise use cases—especially those requiring current, verifiable, source-attributed answers—RAG is faster to deploy, cheaper to maintain, and more auditable than fine-tuning alone. The two approaches can also be combined: a fine-tuned model paired with a RAG retriever often yields the best accuracy.
RAG Pipeline Architecture: From Ingestion to Grounded Response
A production-grade enterprise RAG pipeline moves through four deterministic stages: document ingestion and chunking, embedding generation, hybrid vector retrieval, and LLM synthesis with cited output. LLM.co engineers your chunking strategy—fixed-size, semantic, or hierarchical—to match your document types, whether contracts, clinical notes, or policy manuals. Embeddings are generated inside your environment using open-weight models, then stored in a private vector store such as pgvector on Postgres, Weaviate, or FAISS, depending on your data volume and infrastructure constraints.
At query time, hybrid search fuses dense semantic vectors with sparse BM25 keyword matching to maximize recall across technical and colloquial phrasings. A cross-encoder reranker then re-scores candidate passages against the original query before the LLM sees them—measurably reducing hallucination by filtering out high-cosine but low-relevance chunks. Every answer surfaces document titles, page references, and retrieval scores so compliance and audit teams can trace any AI response to its source. Explore how this pairs with internal search and multi-document query deployments.
Vector Database Selection for Private Deployments
Choosing the right vector database is an infrastructure decision, not just a performance one. For enterprises already running Postgres, pgvector adds semantic search without a new data tier and keeps embeddings inside your existing access-control perimeter. Weaviate supports native hybrid search and multi-tenant isolation suitable for regulated environments. FAISS is the high-throughput option for air-gapped deployments where no managed service is acceptable. Pinecone fits teams that want a fully managed index—provided their data privacy policy permits a hosted service.
LLM.co's architecture is vector-store-agnostic. We select and configure the backend that aligns with your latency targets, document corpus size, and on-prem or hybrid cloud deployment model, and we apply the same encryption-at-rest, access-logging, and key-rotation policies to the vector index as to the source documents themselves.
Agentic RAG: Multi-Step Retrieval for Complex Knowledge Work
Standard RAG answers a single query with a single retrieval pass. Agentic RAG wraps that retrieval in a reasoning loop: the model evaluates whether the retrieved context is sufficient, issues follow-up sub-queries if not, and iterates until it can produce a well-grounded answer—or reports that the knowledge base lacks the required information. This pattern is especially effective for knowledge base assistants handling compound research questions, and for contract review workflows that require cross-referencing multiple clause types across lengthy documents.
LLM.co implements agentic RAG with deterministic guardrails: each retrieval step is logged, every tool call is auditable, and the agent is scoped strictly to your authorized data sources. No call leaves your VPC. This makes agentic patterns viable in governance and audit-sensitive organizations that would otherwise rule out autonomous AI workflows entirely.
Private AI On Your Terms
Tell us your use case and constraints — on-prem, cloud, or edge — and we'll map a compliant deployment within one business day.
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