Vector Database Consulting
Design, deploy, and optimize vector search infrastructure for AI-powered applications. From schema design to production scaling, we build the search backbone your AI systems need.
Platforms We Work With
We help you choose the right vector database for your use case, then optimize it for production.
Pinecone
Fully managed, serverless. Best for fast time-to-production with minimal ops overhead.
Weaviate
GraphQL-native with built-in vectorization modules. Great for multi-modal search.
Qdrant
Rust-based, high performance. Excellent filtering and payload storage capabilities.
ChromaDB
Lightweight, developer-friendly. Ideal for prototyping and smaller-scale applications.
pgvector
PostgreSQL extension. Perfect if you want vector search alongside your existing relational data.
What We Deliver
Schema Design
Optimal collection structure, metadata schemas, and index configuration tuned for your query patterns and data characteristics.
Indexing Strategy
HNSW vs IVF vs flat index selection. Embedding model choice, chunking strategy, and batch ingestion pipelines for millions of documents.
Query Optimization
Latency profiling, query rewriting, metadata pre-filtering, and caching layers. Sub-50ms queries at scale.
Hybrid Search
Combine vector similarity with keyword (BM25) search for superior recall. Reciprocal rank fusion and re-ranking pipelines.
Production Scaling
Sharding strategy, replica configuration, auto-scaling, and cost optimization. Handle growth without rebuilding.
Monitoring & Ops
Query performance dashboards, index drift detection, embedding freshness monitoring, and alerting for production systems.
Our Process
Discovery & Platform Selection
Analyze your data, query patterns, scale requirements, and existing infrastructure to recommend the optimal vector database platform.
Schema & Index Design
Design collection schemas, choose embedding models, define chunking strategies, and configure indexes for your specific access patterns.
Build Ingestion Pipeline
Create robust data pipelines that extract, chunk, embed, and load your documents. Incremental updates, deduplication, and error recovery included.
Query Layer & Optimization
Build the search API with hybrid search, filtering, re-ranking, and caching. Load test until queries meet latency targets under production traffic.
Deploy & Monitor
Production deployment with monitoring dashboards, alerting, backup strategy, and scaling configuration. Handoff with full documentation.
Who This Is For
Building a RAG System
You need a vector database as the retrieval backbone for your RAG pipeline. We’ll design it so your AI gives accurate, sourced answers from your documents.
Scaling Semantic Search
Your product needs semantic search across millions of items — products, articles, tickets, or documents. We’ll build search that understands intent, not just keywords.
Migrating Platforms
Outgrowing ChromaDB or hitting Pinecone cost limits. We’ll plan and execute your migration to the right platform without downtime.
Optimizing Performance
Queries are too slow, recall is too low, or costs are too high. We’ll profile, tune, and optimize your existing vector database deployment.
Frequently Asked Questions
Which vector database should I use?
How many vectors can you handle?
Do I need a separate vector database, or can I use pgvector?
What embedding models do you recommend?
Build Your Vector Search Infrastructure
Book a free consultation. We’ll discuss your data, query patterns, and recommend the optimal architecture.
Book Free ConsultationVector Database Consulting — Available Worldwide
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