AI Search / Matching Engine: Unnanu Search Precision Data Discovery for Enterprises

Unnanu Search AI engine processes enterprise data with semantic accuracy. It handles structured and unstructured sources, achieving 99.7% match precision across silos. This guide covers architecture, benchmarks, and integrations.
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Enterprise AI Search Engine Definition

Unnanu Search applies NLP, vector embeddings, and graph analytics to query documents, databases, and streams. It resolves intent, synonyms, and relationships beyond keywords. Supports on-prem, cloud, and hybrid environments with sub-second latency.

Unnanu Search Engine: Precision Data Orchestration for Enterprises

Unnanu Search engine integrates with enterprise data platforms to enable semantic discovery and matching across silos. Patented AI processes structured and unstructured sources, delivering 99.7% precision in sub-100ms latency.

  • Semantic Indexing: Generates vector embeddings from text, metadata, and entities—reducing query time 90% vs. manual methods.
  • Hybrid Query Processing: Combines BM25, dense retrieval, and reranking—improving relevance scores 40%.
  • Graph Analytics Layer: Links entities across datasets for relational matches; activates 35% more dark data (68% of enterprise total unused).
  • Real-Time Pipeline: Ingests via Kafka and Delta Lake; supports petabyte-scale with 35% cost reduction.

These components align with 2025 benchmarks: 2.5 quintillion bytes daily global creation, 85% AI adoption by 2026 (Gartner), $15.3B market at 14.2% CAGR. Monitor via Prometheus or Tableau exports. Free trial available. Contact: enterprise@unnanu.com.

Unnanu Search Engine: Precision Data Orchestration for Enterprises

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