Internal Search
Find anything across your internal knowledge.
Grounded answers, with citations.
Retrieval looks across your documents, the model composes the answer, and every claim is anchored to a source your reviewers can verify.
- Cites the exact source for every assertion
- Access-checked against the asking user
- Logged end-to-end for audit + improvement
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.
Enterprise AI Features
LLM.co transforms your internal knowledge into a fully searchable, AI-powered interface—deployed privately and tailored to your organization's data. Whether you're navigating thousands of documents, emails, PDFs, meeting notes, or structured databases, our internal search solution helps your team retrieve answers fast—with zero exposure to public models or third-party platforms.
Why Organizations Use LLM.co for Internal Search
Our internal search runs entirely within your infrastructure—on-premise or in a virtual private cloud (VPC). You get all the power of generative retrieval without ever sending your internal content, client records, or IP to public AI services.
Unlike keyword-only tools, LLM.co uses embeddings and natural language processing to understand meaning, not just matches. Ask questions the way you would to a teammate—"Where are our latest procurement policies?" or "What was the final position on the Q4 pricing update?"
Search across emails, Slack threads, PDFs, spreadsheets, Word docs, meeting transcripts, internal wikis, databases, and more. Our pipeline unifies and indexes data from various sources—so you never have to guess where something lives.
Search isn't just about finding a file—it's about getting answers. Our LLMs provide grounded, cited responses using your internal data, giving your team quick summaries, insights, and suggested follow-ups sourced directly from your content.
We fine-tune search relevance using your past queries, workflows, naming conventions, and departmental priorities. Legal teams, for instance, may rank contracts higher; engineering may prioritize Git or Notion.
Internal search honors existing role-based access controls, ensuring users only see what they're authorized to. Whether data lives in HR folders or financial reporting archives, results are filtered securely by user permissions.
Key Use Cases
Allow employees to find the latest policies, internal processes, and how-to guides using conversational queries across shared drives and content systems.
Quickly retrieve policies, past audit responses, legal opinions, and communications for internal or external review—grounded in your own historical documentation.
Search across Notion, Confluence, GitHub, Google Docs, and wikis to retrieve relevant decisions, specs, and dependencies across product life cycles.
Help new employees find benefits documents, PTO policies, internal software guides, or IT troubleshooting documentation without filing a ticket.
Query past contracts, pricing discussions, client interactions, or regulatory disclosures to get instant insight from cross-functional documentation.
Built for Enterprise Privacy and Security
Your internal content is sensitive and proprietary. LLM.co ensures it stays that way. Every deployment includes:
End-to-end encryption of documents and metadata
Role-based access enforcement based on your identity provider
On-prem or VPC deployment for full data residency control
Private, containerized vector database with no cross-tenant leakage
Model Context Protocol (MCP) for explainable output and source traceability
Full audit logs of queries, responses, and data access history
Integrations & Ingestion Sources
LLM.co supports secure connectors for:
Data can be ingested as raw files or embedded in real time with update schedules you control.
Google Workspace (Docs, Sheets, Gmail)
Microsoft 365 (Outlook, SharePoint, Teams)
Slack, Notion, Confluence, Jira
Box, Dropbox, OneDrive
GitHub, GitLab
PostgreSQL, MySQL, MongoDB
Custom file storage or S3 buckets
Who Uses LLM.co Internal Search
Knowledge-heavy teams needing quick access to dense documentation
Enterprise IT & Ops managing large-scale content repositories
Legal, HR & Compliance retrieving policy and regulatory documents securely
Product & Engineering navigating historical specs, bugs, and feature decisions
Sales & Support teams referencing prior client interactions, proposals, and SLAs
Find What You Already Know—Faster, Smarter, and Securely
You don't need another knowledge silo or generic chatbot. With LLM.co, your team gets a fully private, AI-powered search engine that understands your data, respects your security, and helps you move faster.
Hybrid Retrieval and Reranking for Precision at Scale
LLM.co's search pipeline combines dense vector embeddings with BM25 keyword retrieval in a hybrid architecture that outperforms either method alone. At query time, both retrieval paths run in parallel and a cross-encoder reranking model scores candidate passages for relevance before they reach the language model—surfacing the most contextually appropriate results even across large, heterogeneous corpora. This approach eliminates the gaps of pure semantic search (which can miss exact-match terminology like product codes or legal citations) and the blindspots of keyword engines (which ignore meaning entirely). The result is higher-precision answers with cited sources, grounded in your RAG pipeline running entirely within your infrastructure.
Because the reranker and embedding models are deployed privately alongside your LLM, there is no round-trip to a third-party inference API. Organizations in regulated industries or air-gapped environments can run the full stack—embeddings, reranker, and generative model—on private GPU infrastructure without any external dependency. This makes on-prem deployment viable even for environments with strict egress controls or data residency mandates.
Permissions-Aware Retrieval Across Every Connected Source
Enterprise search is only as trustworthy as its access controls. LLM.co connectors for SharePoint, Confluence, Google Drive, Slack, and Notion inherit permissions directly from each source system at index time—and enforce them again at retrieval time. A user querying across your full knowledge corpus will only receive results from documents they are already authorized to access in the originating platform. There is no flattening of ACLs, no cross-tenant leakage, and no reliance on post-hoc filtering that could expose metadata. For a deeper look at how this interacts with multi-document workflows, see multi-document query.
Permission states sync on a configurable schedule so that when access is revoked in your identity provider or source system, those documents are excluded from results within the next sync window. Combined with full audit logs of every query, retrieved chunk, and generated response, this gives your enterprise security and compliance teams the traceability they need for SOC 2, HIPAA, and internal governance reviews—without requiring any changes to how your teams already manage access.
Common questions
01How does permissions-aware search work when data comes from multiple systems like SharePoint and Confluence?
LLM.co connectors read access control lists from each source system during ingestion and store them alongside the indexed content. When a user submits a query, the retrieval layer filters candidate documents against that user's identity before ranking or generation occurs—so results are scoped to what they are already authorized to see in the originating system. Access changes in your identity provider propagate on the next sync cycle.
02What is hybrid search and why does it matter for enterprise use cases?
Hybrid search combines dense vector retrieval (semantic similarity via embeddings) with sparse keyword retrieval (BM25 or equivalent) and merges results through a reranking model. Pure semantic search can miss exact-match terms like SKU codes, legal clause references, or internal project names. Hybrid retrieval captures both meaning and precision, which is critical when your corpus contains structured data, technical documentation, and unstructured prose side by side.
03Can LLM.co internal search operate in a fully air-gapped or no-internet environment?
Yes. Every component—the embedding model, reranker, vector database, and generative LLM—can be deployed on private on-premise hardware or within a customer-managed VPC with no outbound network dependency. LLM.co supports self-hosted inference runtimes and containerized deployment, so organizations with strict egress controls or classified environments can run the full pipeline without any external API calls. See our on-prem deployment overview for architecture details.
04How does AI-powered internal search differ from connecting a chatbot to our existing search index?
A standard search index returns ranked documents; AI-powered internal search uses a RAG pipeline to retrieve the most relevant passages and synthesize a grounded, cited answer in natural language. The model does not rely on memorized training data—it reads your actual documents at query time. This means answers stay current as your content changes, every response is traceable to a source, and the system handles multi-hop questions that require synthesizing information across several documents rather than matching a single file.
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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