Vector & Graph Databases
Pinecone, Weaviate, Neo4j. Semantic search infrastructure and knowledge graph systems for AI-native applications — from sub-50ms vector retrieval to enterprise knowledge graphs.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Semantic Search and Knowledge Graph Infrastructure
We design and deploy vector and graph database architectures that power AI retrieval systems at scale. Low-latency 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, but not which storage pattern fits the 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, not bolted on as a vendor experiment
What We Build
| Capability | What We Deliver |
|---|---|
| Vector search | Pinecone and Weaviate deployments optimized for sub-50ms retrieval at scale |
| Knowledge graphs | Neo4j architectures for entity relationships, lineage tracking, and recommendation systems |
| Hybrid search | Combined vector + keyword search with re-ranking for maximum relevance |
| Embedding pipelines | Automated document processing, chunking, and embedding generation |
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| RAG pipeline needs sub-50ms semantic search at scale | Pinecone managed vector DB + embedding pipeline |
| Hybrid search needed (semantic + keyword + metadata filtering) | Weaviate with BM25 + vector hybrid scoring |
| Complex entity relationships, lineage tracking, or graph traversals | Neo4j knowledge graph |
| Full-text search, log analytics, or observability at scale | Elasticsearch / ELK stack |
| Cloud data warehouse for analytics, ML feature stores, or BI | Snowflake + dbt + Snowpark |
| Not sure which storage architecture fits your AI use case | AI Strategy Advisory — we map data to architecture |
Engineering Standards
- Index optimization for latency SLAs
- 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, not as a plug-in afterthought
- Organization is ready to monitor index freshness, recall quality, and cost at production scale
Specialist Capabilities
| Capability | Focus |
|---|---|
| Elasticsearch Engineering | Search infrastructure, ELK stack, log analytics, observability |
| Snowflake Engineering | Cloud data warehouse, Snowpark ML, dbt, cost optimization |
| NoSQL Engineering | Scylla, Cassandra, wide-column stores for time-series and IoT |
Related articles
The RAG Pipeline Audit: How We Diagnose Retrieval Quality Problems in 5 Days
A structured 5-day RAG pipeline audit methodology: architecture review, retrieval testing, ingestion analysis, hallucination mapping, and a priority remediation matrix.
RAGVector Database Selection for Enterprise RAG: Pinecone, Weaviate, Qdrant, and the Operational Reality
A practical comparison of Pinecone, Weaviate, Qdrant, pgvector, Milvus, and Chroma across the dimensions that matter in production: filtering, multi-tenancy, cost, and migration paths.
RAGChunk Strategy Failures in Production RAG: When Your Chunking Works in Dev and Breaks in Production
Why RAG chunking that passes dev tests collapses in production: document diversity, table handling, size failures, overlap traps, and how to build quality metrics.
Discuss your Vector & Graph Databases path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.