How Obviant Achieved 30% Better Defense Recommendations with Pinecone Hybrid Search
Obviant, a unified defense market intelligence platform, deployed Pinecone’s hybrid search—combining dense and sparse vector retrieval—to help government agencies and defense contractors navigate fragmented acquisition data. By implementing a cascading retrieval strategy with Pinecone’s trained sparse embedding model, Obviant improved recommendation relevance by 30% while managing 120 million vectors at under 50ms query latency.
Impact
30%
Recommendation relevance improvement
>120M
Vectors managed
<50ms
P50 query latency at 40 QPS
Challenge
Obviant needed to surface accurate acquisition recommendations from 120M+ vectors of fragmented, unstructured government defense data—a task where traditional keyword search missed critical contextual relationships between programs, contracts, and regulatory documents.
Solution
Pinecone’s hybrid search was deployed with a cascading retrieval strategy combining dense semantic embeddings and Pinecone’s trained sparse embedding model, enabling Obviant to handle both conceptual and exact-match queries across its entire defense intelligence dataset.
Tools & Technologies
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Full Story
The U.S. defense market is one of the most complex procurement environments in the world. Government agencies shaping requirements and private companies seeking contract opportunities must sift through budget lines, contract award records, organizational charts, and program histories—data that is siloed, inconsistently formatted, and spread across dozens of sources. Obviant was built to close that gap, aggregating and synthesizing this fragmented data into actionable intelligence dashboards.
Delivering that intelligence at scale required more than keyword search. Obviant needed a retrieval engine capable of understanding relationships between programs, surfacing relevant contracts from years of historical data, and connecting acquisition signals across unstructured documents—PDFs, government reports, presentations, and webpages. Standard document stores could match terms but not meaning, which meant important connections were consistently missed.
Oviant deployed Pinecone as its core retrieval infrastructure, implementing a cascading hybrid search strategy that combines dense and sparse vector retrieval. Dense retrieval captures semantic meaning and conceptual relationships; sparse retrieval preserves keyword precision critical for specific program names, contract numbers, and regulatory terms. Pinecone’s trained sparse embedding model anchored the sparse side of this architecture, enabling the system to handle both nuanced conceptual queries and exact-match lookups within the same retrieval pipeline.
The architecture delivered measurable improvements. Recommendation relevance increased 30% compared to the prior system, while Pinecone’s infrastructure scales to manage over 120 million vectors across both dense and sparse indexes. P50 query latency holds under 50 milliseconds at 40 queries per second—performance that allows defense analysts to surface insights in real time rather than waiting for batch-processed results.
Oviant’s retrieval infrastructure now underpins recommendations used by both government agencies and private sector firms navigating defense acquisition. The combination of semantic depth and keyword precision has proven especially valuable in a domain where knowing the exact name of a program matters as much as understanding its strategic context.
AWS Lambda