SWIRL is running in production across government, legal, pharma, education, and enterprise AI. No data migration. No vector database. No lock-in.
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.
An AI company offering domain-specific capabilities for safe enterprise AI use needed to give its platform genuine knowledge reach across customers' internal data — without introducing the cost, architectural complexity, data duplication risk, or stale-index problems of a vector database. Their customers' data was sensitive, distributed, and varied; a centralized embedding pipeline was a non-starter.
SWIRL was embedded as the knowledge access layer, giving the platform federated reach across enterprise sources at query time. No data leaves the source systems. No vector DB. No ingestion pipeline to maintain. The MCP server interface made integration straightforward.
Knowledge retrieval capability added to the platform in under a week. The vector database was removed from the architecture entirely, along with its associated infrastructure costs and data governance complications.
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 global pharmaceutical company's supply chain analytics team faced a talent retention crisis. Analysts were spending four weeks or more to produce a single weekly status report because the underlying data — inventory positions, supplier status, regulatory filings, demand signals — lived across siloed systems no one person could access end-to-end. Getting answers from the internal data science team took two weeks or more. Analysts were leaving. The work was unsustainable.
SWIRL was deployed across Collibra, ThoughtSpot, Oracle, Snowflake, BigQuery, and additional enterprise sources. No new data infrastructure was built. No data migrated. Analysts query all systems simultaneously from a single interface, with AI-generated synthesis ranking results by confidence and source authority.
100% of analyst test questions resolved in under one minute, self-service, without new costs or data migration. The turnover problem was solved. Analysts now focus on judgment and action rather than data retrieval — and weekly reports reflect current reality rather than week-old snapshots.
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 4.5 is available in the Azure Marketplace as a VM-based proof of concept. Note that this is the previous generation of SWIRL and does not include the knowledge authority layer, canonical version finding, semantic caching, or MCP server capabilities described in these case studies. SWIRL 5 is available by preview only.
Azure Marketplace →