Vector & Graph Databases

Semantic Search and Knowledge Graph Infrastructure

We design and deploy vector and graph database architectures that power AI retrieval systems in production: Pinecone queries, Weaviate hybrid search, and Neo4j knowledge graphs spanning complex entity relationships.

Typical engagement starts when

• a RAG or search system is live enough that relevance, latency, and freshness have become product issues rather than research questions
• the team knows it needs semantic search or graph traversal and needs the storage pattern matched to workload and operating constraints
• retrieval quality is weak because chunking, metadata, ranking, and storage choices were treated as separate problems
• product or engineering leadership needs the storage layer justified as architecture instead of bolted on as a vendor experiment

What We Build

When to Use This

Engineering Standards

• Index optimization for latency objectives
• Automated embedding refresh pipelines
• Query performance monitoring and alerting
• Backup and disaster recovery for stateful databases

These controls matter because retrieval systems fail when freshness, latency, and relevance drift quietly over time. A database choice that looked fine in a proof of concept becomes expensive once the query path is in production.

Common failure patterns we fix

• vector database selection happening before the team defined retrieval quality targets, metadata strategy, or ranking behavior
• embeddings and indexes going stale because refresh pipelines were never designed as part of the production path
• semantic search launched without hybrid search, filtering, or reranking, leaving users with plausible but weak answers
• graph initiatives modeled as a demo taxonomy with no traversal patterns, ownership model, or downstream use case
• retrieval stacks optimized for benchmark latency while recall, explainability, and cost drift in production

What you leave with

• a storage architecture matched to the actual retrieval or graph problem instead of generic database enthusiasm
• indexing, refresh, and query paths designed with explicit latency, relevance, and cost expectations
• monitoring and operating rules for freshness, recall, and failure handling after launch
• retrieval infrastructure the internal team can extend without rebuilding the stack every time the corpus changes

Best Fit

• Team already has a retrieval or graph use case with clear latency, relevance, or relationship requirements
• Product needs semantic search, hybrid search, metadata filtering, or graph traversals as part of core behavior
• Engineering team wants the storage layer treated as part of system architecture instead of a plug-in afterthought
• Organization is ready to monitor index freshness, recall quality, and cost at production scale

Specialist Capabilities