Vector Databases at Shivacha
Databases for similarity search over embeddings — the retrieval layer of RAG.
Overview
Vector databases store embeddings and retrieve semantically similar items quickly. We choose between PostgreSQL with pgvector, search engines with vector support and dedicated vector databases based on scale, filtering needs, latency and operational preferences.
Why we use it
- Semantic similarity search
- Metadata filtering
- Scales to large corpora
- Powers RAG and recommendations
How we use it
Vector Databases in our engineering work
RAG retrieval
Finding relevant passages.
Recommendations
Similar items and content.
Deduplication
Near-duplicate detection.
Services
Services that use Vector Databases
RAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreAI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
Learn moreGenerative AI Development
Generative AI applications for text, documents, code and media — grounded in your data and engineered for production reliability.
Learn moreLLM Development
LLM application and platform engineering: model selection, fine-tuning, serving, evaluation and cost-optimised inference.
Learn moreAI Chatbot Development
LLM-powered chatbots for customer service, sales and internal support — grounded, on-brand and integrated with your systems.
Learn moreAI Copilot Development
Embedded AI copilots that help users inside your product or internal tools — drafting, analysing, searching and taking actions.
Learn moreProducts
Products built with Vector Databases
Pairs well with
What we combine with Vector Databases
Models, retrieval, agents and ML operations.
Insights
Related insights
Designing AI products users trust
Trust in AI products is designed: show sources, make uncertainty visible, make correction easy and keep humans in control of consequential actions.
Learn moreWhy most AI pilots never reach production — and the engineering that fixes it
The model is rarely the problem. Retrieval quality, evaluation, integration and governance decide whether an AI pilot becomes a production system.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn more
Build with Vector Databases.
Tell us about your project, or the engineers you need, and we will propose an approach.