Multimodal Vector Search
Enterprise-grade vector search architecture processing billions of vectors with sub-50ms p95 query latency
Overview
Enterprise-grade vector search architecture processing billions of vectors across text, images, audio, video, and structured data with sub-50ms p95 query latency. Advanced document parsing engine handles complex file structures (PDFs, Office documents, HTML, Markdown) with 98%+ extraction accuracy, including OCR for scanned documents with 50+ language support. Structure-aware chunking preserves semantic boundaries optimizing embedding quality. Addresses domain-specific challenges through custom-trained embeddings (768-1024 dimensional vectors) optimized for semantic understanding, enabling 94%+ relevance accuracy at scale. Intelligent document versioning with delta indexing reduces overhead by 90% through incremental updates, tracking changes with <100ms detection latency and enabling temporal search queries across document history. Scalable distributed indexing infrastructure handles 10M+ documents with horizontal scaling, supporting real-time updates and maintaining 99.9%+ uptime. Integrates seamlessly with existing AI pipelines through RESTful APIs and SDK support for Python, JavaScript, and Go.
Key Features
Large-Scale Dataset Processing
Distributed indexing architecture handles billions of vectors across multiple data modalities with horizontal scaling. Advanced partitioning strategies (sharding, replication) ensure consistent performance as datasets grow from millions to billions of vectors. Real-time indexing pipeline processes 50K+ new documents per hour while maintaining sub-second search latency.
10M+ documents indexed | Billions of vectors processed | 50K+ documents/hour indexing rate | Linear scaling performance
Efficient Indexing Algorithms
Hybrid indexing approach combining HNSW (Hierarchical Navigable Small World) and IVF-PQ (Inverted File Index with Product Quantization) algorithms optimizes memory usage by 60% compared to naive implementations. Adaptive index selection based on query patterns and dataset characteristics ensures optimal performance for both exact and approximate nearest neighbor search.
60% memory reduction | 10x faster index construction | 99.9% recall at 10 nearest neighbors | Sub-100ms index updates
Fast Multi-Modal Retrieval
Unified vector space enables cross-modal search: query with text and retrieve images, or search images to find similar video content. Custom fusion strategies combine embeddings from different modalities, achieving 94%+ relevance accuracy. Query processing pipeline includes semantic understanding, intent classification, and result ranking with explainability.
Sub-50ms p95 latency | 94%+ relevance accuracy | 100K+ QPS throughput | Cross-modal retrieval with 89% precision
Multi-Format Data Support
Native support for text (plain, markdown, structured), images (JPEG, PNG, WebP), audio (MP3, WAV, FLAC), video (MP4, WebM), and structured data (JSON, CSV). Automatic format detection and preprocessing pipelines extract semantic features using domain-specific encoders. Custom embedding models fine-tuned for each modality ensure optimal representation quality.
5+ data formats supported | 15+ file types processed | 99% format detection accuracy | Real-time preprocessing
Advanced Document Parsing & Extraction
Intelligent document parsing engine handles complex file structures including PDFs (text, scanned, forms), Office documents (Word, Excel, PowerPoint), HTML, and markdown with 98%+ extraction accuracy. OCR capabilities process scanned documents and images with multi-language support (50+ languages). Document structure extraction identifies headers, sections, tables, lists, and metadata automatically. Smart chunking strategies preserve semantic boundaries (sentence, paragraph, section-level) optimizing embedding quality. Entity extraction identifies key information (dates, names, locations, organizations) from unstructured text. Language detection and normalization ensure consistent processing across multilingual documents.
98%+ extraction accuracy | 50+ languages supported | OCR for scanned documents | Structure-aware chunking | Entity extraction | Multi-format parsing (PDF, Office, HTML, Markdown)
Document Versioning & Change Tracking
Intelligent version control system tracks document changes with delta indexing, re-indexing only modified content to reduce overhead by 90%. Change detection monitors file modifications, additions, and deletions in real-time across connected data sources. Version history enables temporal search queries ('find version from last month'), with automatic retention policies and archival. Document lineage tracking maintains relationships between versions, supporting audit requirements and compliance workflows. Incremental updates process only changed documents, reducing indexing costs while maintaining search accuracy.
90% indexing overhead reduction via delta updates | Real-time change detection across all sources | Temporal search with version history | <100ms change detection latency | Full document lineage tracking
Business Impact
Superior Search Relevance
94%+ relevance accuracy compared to 67% with traditional keyword search (41% absolute improvement)
Users find relevant results faster, reducing search iterations by 60% and improving task completion rates by 45%
Reduced Infrastructure Costs
60% memory optimization and efficient indexing reduce infrastructure costs by 40% compared to naive vector search implementations
Lower total cost of ownership enables scaling to larger datasets without proportional cost increases, improving ROI by 2.3x
Enhanced User Experience
Sub-50ms query latency and semantic understanding reduce average search time from 8 seconds to 1.2 seconds (85% improvement)
Faster discovery of relevant content increases user engagement by 38% and improves conversion rates in e-commerce and content platforms
Scalable Architecture
Horizontal scaling supports growth from 1M to 10B+ vectors with linear performance characteristics and 99.9%+ uptime
Future-proof infrastructure eliminates need for architectural redesigns as data volumes grow, reducing technical debt and migration costs
Performance Metrics
query Latency
Sub-50ms p95, 35ms p50, 120ms p99 across 10M+ document index
throughput
100K+ queries per second with distributed deployment, 25K+ QPS per node
accuracy
94%+ relevance accuracy, 89% precision at top-10 results, 96% recall for semantic queries
scalability
Linear scaling from 1M to 10B+ vectors, 10M+ documents indexed, 50K+ documents/hour indexing rate
memory Efficiency
60% memory reduction vs naive implementations, 2.4GB per 1M vectors (768-dim), 4.8GB per 1M vectors (1024-dim)
availability
99.9%+ uptime with distributed deployment, <100ms failover time, zero-downtime index updates
Technical Specifications
architecture
Distributed vector database with HNSW and IVF-PQ indexing, horizontal sharding across compute nodes, delta indexing for incremental updates
embedding Dimensions
768-1024 dimensional vectors with quantization support (INT8, FP16, FP32)
supported Modalities
Text, images (JPEG/PNG/WebP), audio (MP3/WAV/FLAC), video (MP4/WebM), structured data (JSON/CSV)
document Parsing
PDF parsing (text, scanned, forms), Office documents (Word, Excel, PowerPoint), HTML, Markdown | OCR with 50+ language support | Structure extraction (headers, sections, tables, lists) | Smart chunking (sentence, paragraph, section-level) | Entity extraction (dates, names, locations, organizations) | 98%+ extraction accuracy
version Control
Document versioning with temporal search, delta indexing with 90% overhead reduction, real-time change detection (<100ms), automatic retention policies, full document lineage tracking
api Interfaces
RESTful HTTP API, gRPC for high-throughput, Python/JavaScript/Go SDKs with async support
integration
Vector database connectors (Pinecone, Weaviate, Qdrant), Elasticsearch/OpenSearch compatibility layer, webhook support for change notifications
Get Started with Multimodal Vector Search
Ready to transform your business with multimodal vector search? Contact our team to learn more.