Squirro Search Engine Subscription

The Library of Congress is awarding a sole-source contract to Squirro Americas Inc. under FAR 6.302-1 and 41 U.S.C. § 6101 for the continued provision of its Squirro Cognitive Search platform, including the Insight Engine and AI Studio, along with ongoing AWS hosting and operational support services. The contract is required to maintain uninterrupted functionality for CRS.gov, a mission-critical system serving congressional researchers, as no other vendor can replicate the deeply customized integration of Squirro’s technology with the Library’s existing Oracle, SharePoint, and PoolParty infrastructure. The solution supports up to 10,000 users or 50,000 searches per month and is hosted on AWS in the us-east-1 region across Development, Test, Staging, and Production environments. The contract includes a six-month base period beginning September 28, 2026, and a six-month option period extending through September 27, 2027, to bridge the gap until a long-term replacement is procured, with a total estimated value exceeding $900,000. Operational support is governed by a Gold Support package requiring P1 incident responses within two business hours, emergency patch deployment, and mandatory regression testing before any software updates. The procurement bypasses public competition due to the unique and irreplaceable nature of Squirro’s system, with a waiver of public notice issued under FAR 5.202(a)(1) on grounds of operational urgency and the risk of service disruption. The contract is managed by the Library’s Contracts Services office in Washington, DC, with Veronica Price as the primary point of contact, and no formal line-item pricing or subcontracting plan is disclosed. No physical packaging, marking, or military standards apply, as all deliverables are digital services. The award is justified on technical uniqueness, mission necessity, and unacceptable risk of delay, not on cost or price competitiveness, and no contract clauses, COR, or key personnel are specified beyond the support-level performance expectations.