SWIRL runs in production in government, legal, pharma and higher education. In every one of them the data stayed where it was, under the permissions that were already there: no migration, no second copy to secure, no lock-in.
The clause your firm approved, drawn from iManage, NetDocuments, M365 and research sources, with no index to subpoena.
Explore SWIRL Legal →Cited answers across policies, models and client records without a second store to bring into scope, and nothing to purge when a retention obligation lands.
Talk to Us →Search and answers across clinical, research and operational systems, with each system's own access control deciding what a user sees.
Talk to Us →Deployed inside your boundary, with any LLM including on-prem, and no copy of the record leaving the system of record.
Talk to Us →This federal data services organization built its business by aggregating and curating hundreds of proprietary and government data sources into a differentiated intelligence product. Over time those same silos became a liability - decision-makers waited days for cross-source analysis, and technical support engineers couldn't quickly resolve questions that crossed repository boundaries. A vector database or purpose-built search engine would have cost millions and required moving data they were legally and contractually prevented from centralizing.
SWIRL was deployed in Azure Government (Azure.gov), connecting federated search and AI-assisted synthesis across all curated data sources at the point of query. No data moved. No new ingestion pipelines. All existing security, access controls, and compliance posture were preserved.
Decision cycles that took days now take minutes. Support engineers resolve cross-source questions in real time. The organization's data assets became compoundingly more valuable without changing how they are stored or governed.
A major law firm relied on an aging federated search platform spanning Microsoft 365, dozens of curated SharePoint collections, elite legal and specialized publishers, matter management systems, and precedent databases. The legacy system was expensive to maintain, couldn't surface AI-assisted synthesis, and couldn't keep pace with the firm's growing knowledge footprint or evolving attorney workflow.
SWIRL replaced the legacy federated search layer across M365, SharePoint, legal databases, and internal repositories. Attorneys and paralegals now receive ranked, canonical answers drawn from the firm's complete knowledge base, with AI-generated synthesis available at the point of query - all within the firm's existing security perimeter.
The legacy platform was retired. Complex legal research that previously required manual cross-repository searches now resolves in a single query. Knowledge that lived only in certain SharePoint collections or with senior attorneys became firm-wide.
A leading business school needed to provide faculty, researchers, and administrative staff with unified search across a sprawling knowledge ecosystem - Microsoft 365, ServiceNow, internal data catalogs, departmental websites, the university library catalog, periodical indexes, and more. Siloed access was a constant friction point for research productivity and operational efficiency alike.
SWIRL deployed as the unified search and AI synthesis layer, connecting to all systems without data migration or changes to existing repository governance. Faculty query the institution's full knowledge base from a single interface; ServiceNow records, periodical indexes, and M365 files appear together, ranked by relevance and confidence.
Research time for faculty reduced significantly. IT helpdesk tickets for "I can't find" queries eliminated. Institutional knowledge that previously required knowing which specific system to search became accessible to everyone.
A leading federal systems integrator had built a powerful content management system for air-gapped, classified environments. Analysts could manage content within the CMS but had no way to search, curate, or surface data from other sources within the compartment - or to expose that broader knowledge to internal AI systems. Building a custom search capability inside a classified environment was a multi-month, multi-vendor proposition.
The integrator connected SWIRL via the SWIRL MCP server interface - one engineer, under one day. SWIRL enabled federated search across GCC High and Low networks, internal data repositories, and classified content sources, all within the air-gapped environment. Data never leaves the compartment.
Full-spectrum knowledge access operational in under a day. Internal AI systems now access the complete compartment knowledge base via the MCP interface. The integrator added the capability to their platform offering and is deploying it across additional customer environments.
Want to explore SWIRL independently? SWIRL Enterprise 5 - including the version clustering, approved answers, semantic caching and MCP server described in these case studies - deploys privately with one click on Railway, into your own workspace, on your own subscription. Or talk to us and see it run on your own stack.
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