AI, Data & Intelligence
Vector Database Solutions
Selection, design and operation of vector databases for AI search and retrieval, from pgvector inside PostgreSQL to dedicated engines such as Qdrant, Weaviate, Milvus or Pinecone, tuned for recall, speed and cost.
Capability overview
What vector database solutions involves
Vector databases store embeddings, numerical representations of text or images, and find the most similar items quickly. They sit underneath semantic search, recommendation and retrieval-augmented generation. Choosing and tuning one well affects answer quality, response time and infrastructure cost as collections grow from thousands to hundreds of millions of vectors.
Many teams do not need a new database at all: pgvector in an existing PostgreSQL instance or vector search in OpenSearch or Elasticsearch handles moderate scale with familiar operations. Dedicated engines earn their place at higher volumes, with heavy metadata filtering or strict latency targets.
What is included
What we help with
Technology selection
Options benchmarked on your embeddings and queries for recall, latency, filtering support, operations effort and licence terms.
Index design and tuning
HNSW or IVF index parameters tuned to balance recall against query speed and memory use.
Metadata filtering
Schemas that support filters such as tenant, document type, date and permissions without destroying search performance.
Embedding lifecycle
Plans for re-embedding collections when models change, including dual indexes during migration.
Operations and resilience
Replication, backups, capacity monitoring and scaling procedures for production workloads.
How we work
How we deliver vector database solutions
Workload profiling
Collection size, growth, query rates, filter patterns and latency needs documented.
Benchmark
Shortlisted engines tested on a representative sample with realistic queries and filters.
Schema and index build
Collections, metadata fields and index settings implemented for the selected engine.
Load testing
Concurrent query and ingestion load simulated at projected peak volumes.
Operational handover
Runbooks for scaling, re-indexing, backup and restore delivered to the owning team.
Related capabilities
Related capabilities in Generative AI & LLM Engineering
Semantic Search
Search that understands meaning rather than exact keywords, for websites, product catalogues, intranets and support portals, combining vector and keyword retrieval with relevance tuning and analytics.
Enterprise Knowledge Systems
The content, structure and governance foundations that make organisational knowledge findable and AI-ready: ownership, taxonomies, metadata, knowledge graphs and lifecycle rules across repositories.
LLMOps
The operating discipline for language model applications in production: prompt and model versioning, evaluation pipelines, tracing, guardrails, cost monitoring and safe release of changes.
Generative AI Strategy
Generative AI Strategy within our generative ai & llm engineering services, scoped after a short discovery conversation.
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Questions & answers
Questions about Vector Database Solutions
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionOften not at first. If you already run PostgreSQL, pgvector can serve millions of vectors comfortably. A dedicated engine becomes worthwhile at larger scale, with complex filtering or when vector search is central to the product.
Managed services reduce operations work and suit teams without database specialists. Self-hosting gives more control over data location and cost at scale. Both options are compared in the benchmark.
Vectors from different models are not compatible, so the collection must be re-embedded. We plan this as a background migration with a parallel index and cut over once quality checks pass.
Selection and benchmarking are fixed price. Implementation is quoted per phase, and ongoing operation can be included in a managed data platform service.
Next step
Discuss vector database solutions with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.