Vector Databases
Semantic search and retrieval systems. We help organisations implement vector databases for AI-powered search, retrieval-augmented generation and similarity matching at scale.
Why vector databases matter
Vector databases are the backbone of modern AI search and retrieval, enabling semantic understanding that keyword search cannot match.
Semantic search
Find information by meaning rather than keywords. Vector search understands context and intent for dramatically better results.
RAG foundation
The critical infrastructure layer for retrieval-augmented generation, providing fast, accurate document retrieval for grounded AI responses.
Scale and performance
Purpose-built for high-dimensional vector search at scale with sub-second query times across millions of embeddings.
Use cases for vector databases
Enterprise search
Semantic search across internal documents, wikis and knowledge bases that understands what users mean, not just what they type.
RAG pipelines
Store and retrieve document embeddings for retrieval-augmented generation, grounding AI responses in your data.
Recommendation systems
Product, content and service recommendations based on semantic similarity and user behaviour embeddings.
Duplicate detection
Identify near-duplicate content, support tickets and records using vector similarity for deduplication workflows.
Image search
Visual similarity search using image embeddings for asset management, e-commerce and content moderation.
Anomaly detection
Identify outliers and anomalies by measuring vector distance from normal patterns in security and fraud detection.
Vector database platforms
We work with a range of vector database solutions, choosing the right one based on your scale, infrastructure and requirements.
Pinecone
Fully managed vector database with serverless and dedicated options. Fastest time to production for new projects.
Weaviate
Open-source vector database with hybrid search combining vector and keyword approaches. Self-hosted or managed.
pgvector
Vector search extension for PostgreSQL. Ideal when you want to keep vectors alongside existing relational data.
Frequently Asked Questions
Which vector database should we use?
It depends on your requirements. Pinecone for managed simplicity, Weaviate for open-source flexibility, pgvector for PostgreSQL integration. We help you choose and implement the right solution.
How can The Bot Forge help with implementation?
Our team provides end-to-end support, from vector database selection and setup to embedding pipeline development and ongoing optimisation.
What are the typical use cases?
Vector databases are commonly used for semantic search, RAG pipelines, recommendation systems, duplicate detection and similarity matching in AI applications.
Build with Vector Databases
Ready to implement semantic search and retrieval for your AI applications? We can help you choose, deploy and optimise vector databases.