Vector databases for RAG and semantic search
Vector databases for RAG, semantic search, and agent memory at production scale.
Migrate all kinds of workloads to vector databases
Vector databases power retrieval for RAG, recommendations, and agent memory — when schema, indexing, and ops are designed correctly.
We implement Pinecone, Weaviate, pgvector, and hybrid search patterns tuned to your latency and cost targets.
vector databases delivers versatility, ease of use, reliability, and security
vector databases offers multiple benefits, and has helped teams successfully modernize infrastructure and ship faster.
Production-ready
vector search integrated with governance, monitoring, and cost controls.
Integrable
Connect to ERP, CRM, data warehouses, and internal APIs.
Secure by design
Access control, data residency, and audit trails for enterprise AI.
Scalable
Architecture that handles growth in users, data, and model complexity.
Observable
Logging, tracing, and quality metrics for models and pipelines.
Team enablement
Documentation, runbooks, and pairing so your team extends what we ship.
Choosing the right vector databases solution for your business is crucial
Success in business depends on choosing scalable, secure, and reliable technology — engineered for your workloads.
RAG pipelines
Chunking, embedding, retrieval, and re-ranking at scale.
Hybrid search
Combine vectors with metadata filters and keyword search.
Agent memory
Persistent context stores for multi-turn agent workflows.
Operated indexes
Monitoring, reindexing, and cost optimization.
Self-serve analytics without opening the BI queue
Governed natural-language access to business data so leaders get answers without waiting on analysts or raw SQL.
Read story
3.2M monthly messages. Commerce that stays up at peak.
WhatsApp turned from a manual inbox into a production sales channel — catalog, cart, payments, and AI support at retail scale.
Read storyFrequently asked questions about vector databases development
What is vector databases used for?+
vector databases supports semantic search, RAG, recommendations, and agent memory — storing and querying embeddings at the scale your product requires.
Why do I need vector databases for my AI application?+
Vector storage enables fast similarity search over documents, products, or user behaviour — critical for grounded LLM answers and personalisation.
Is vector databases production-ready at enterprise scale?+
Yes, when indexing strategy, sharding, monitoring, and reindexing workflows are engineered alongside your application — not added as an afterthought.
How does vector databases compare to other vector solutions?+
We evaluate managed vs self-hosted options, hybrid search needs, and total cost of ownership against your latency and residency constraints.
How do you operate vector databases in production?+
SLA monitoring, backup and recovery, schema migrations, and cost dashboards — with clear ownership between data and application teams.
Move forward with vector dbs
Book a conversation — we will scope the engagement or tell you if another path is a better fit.