AI-Powered Enterprise Search

Atolio uses a proprietary hybrid search algorithm that combines dense vector semantic search, personalized ranking, and traditional keyword search techniques, as well as extensive metadata query capabilities while being fully permission-aware with real-time updates.
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Running both paths in parallel means neither failure mode applies: Atolio handles specific lookups and broad conceptual questions within the same query pipeline, without requiring a separate fallback.
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The index runs on Vespa, a distributed search engine that supports hybrid retrieval natively without separate infrastructure for vector and keyword stores. All search execution happens within your private cloud environment. Queries, documents, and result sets never transit through Atolio-operated infrastructure or reach external model providers.

Atolio uses a bring-your-own-model (BYOM) architecture: AI capabilities run on model providers you already have contracted, not on Atolio-managed model infrastructure. Atolio routes queries to your designated model endpoints (OpenAI, Anthropic, Azure, AWS Bedrock, Google Vertex, etc. as well as select open weights models) using credentials you control, which also gives you flexibility to change providers if you so choose. Every response is grounded in content retrieved from your Atolio index.
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Atolio also supports Model Context Protocol (MCP), allowing external AI agents and tools to query the Atolio index as a knowledge source, making your organizational knowledge retrievable by AI workflows that operate outside the Atolio UI.
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Because Atolio does not host or proxy model API calls, token usage is billed directly between your organization and your model provider. Atolio has no visibility into prompt content or completions. Vector embeddings used for semantic search are generated at index time and can be produced by self-hosted or provider-managed embedding models, depending on your deployment configuration.

Atolio is built for enterprise-scale deployments and has been validated in environments with tens of millions of indexed documents across dozens of connected sources. The indexing layer runs on Vespa, a distributed search engine designed for high-volume production use. The indexing pipeline is decoupled from the query-serving layer, so large re-indexing jobs and connector updates do not degrade search performance for end users. Horizontal scaling is available within private cloud deployments to handle increased document volume or query throughput.
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Initial full-history indexing completes within the implementation timeline agreed during onboarding. Ongoing delta sync runs continuously and stays within the SLA windows defined per connector. Query latency is consistently sub-second for standard enterprise workloads.

Atolio's search capabilities can be surfaced through custom interfaces using the Platform API, which exposes all core search and retrieval functionality as API endpoints. Engineering teams can build branded internal portals, custom AI assistant interfaces, or third-party application integrations that query the Atolio index without using the Atolio-branded UI. The API returns the same results, with the same permission enforcement and relevance model, as the native interface.
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For teams that want UI customization without full custom development, Atolio's interface supports configuration of visible sources and branding elements through the admin dashboard.

Collections are saved, reusable content groupings that act like “folders” to scope searches and AI-generated answers to a defined slice of your organization's knowledge. Users can bookmark and organize files in one place from all of their connected applications to create their own curated workplace experience. Collections can be created around project artifacts, team-specific information, personal interests, topics, and more. Once saved, users search within that Collection or use it to scope AI summaries to only the content it contains.
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Collections are useful for organizing knowledge by project, team, or workflow. An RFP response team can scope a Collection to security documentation, prior proposals, and product specs. A subject matter expert can maintain a Collection that surfaces their most current content for colleagues who rely on their domain knowledge.
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All permission controls apply within Collections. Users cannot create a Collection that surfaces content they are not authorized to access, and results shown to other users are filtered by each viewer's individual permissions, not the Collection creator's.

Atolio identifies subject matter experts automatically from behavioral signals across all connected knowledge sources – no manual tagging required. At the center of this is the Collaboration Graph, which maps relationships between people, content, and topics based on actual work activity: who authored or significantly contributed to content on a given topic, who collaborates frequently with others in that area, and what topics and systems a person has been most active in recently. Because signals are drawn from across all indexed sources (Slack, Jira, Google Drive, Confluence, and others) expertise is inferred from your entire knowledge environment, not any single tool.
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When a user searches for a topic, Atolio surfaces not only the most relevant documents but also a Related People panel: the individuals whose activity is most closely tied to that content area. This surfaces institutional knowledge that doesn't appear in formal org charts or official directory profiles, including contributors who are deeply knowledgeable but not in visible leadership roles.
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SME identification is permission-aware – users only see people associated with content they're already authorized to access – and updates continuously as new activity is indexed.