The IRS paid Palantir $1.8M to build SNAP, an AI platform that selects audit targets, now in pilot phase.
The IRS contracted Palantir for $1.8 million to build the 'Selection and Analytic Platform' (SNAP), a tool designed to identify high-value audit, tax collection, and criminal investigation cases. The agency cited fragmentation across 100+ legacy systems as the core problem SNAP addresses. The tool is currently in a pilot program and is limited to existing IRS data. Palantir has held IRS contracts since 2014, totaling over $200 million.
SNAP is a case-selection engine built on top of decades of fragmented IRS data systems — a classic enterprise data unification problem solved with Palantir's ontology-based architecture. The technical challenge here isn't the ML model; it's ingesting, normalizing, and ranking signals across 700+ legacy methods. Developers building compliance or fraud detection tools can study this as a blueprint for legacy data modernization at scale.
If you're building a fraud detection or case prioritization feature, benchmark your current scoring pipeline against Palantir's public ontology documentation — specifically how they handle multi-source entity resolution without a unified schema.
Go to developers.palantir.com and navigate to the Foundry or Ontology SDK section
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