Vector database architecture and implementation for AI applications that actually understand context — not just match keywords.
From RAG-powered chatbots to intelligent recommendation engines, we design and deploy vector database systems that transform how your applications find, match, and surface information.
Most teams know they need semantic search. Few know how to build it production-ready.
Choosing the wrong vector DB, embedding model, or indexing strategy can cost months and £10Ks to fix
Queries that work on 1K vectors grind to a halt at 1M. Latency kills user experience
Sending sensitive data to third-party APIs without proper isolation or hybrid search
Unoptimised vector stores that balloon hosting bills and require constant re-architecture
These aren't just technical problems — they're business risks that derail AI initiatives.
End-to-end consultancy from architecture to deployment — built on open-source and cloud-native technologies you control.
"We don't sell you a black-box SaaS. We build systems you own, understand, and can evolve."
See How It WorksProven applications where semantic search and similarity matching deliver measurable ROI
Power AI assistants with grounded, contextually relevant responses retrieved from your knowledge base
OpenAI · Anthropic · LangChain · LlamaIndex
Surface semantically similar items users actually want — beyond basic category matching
E-commerce · Media · Learning platforms
Find relevant documents by meaning, not just keywords. Code search that understands intent.
Legal · Technical docs · GitHub-scale codebases
Multimodal search across visual and audio content using CLIP-style embeddings
CLIP · Whisper · Custom models
A battle-tested process for building reliable, scalable semantic search systems
1–2 sessions
Map your data sources, query patterns, latency requirements, and compliance needs
1 week
Select vector DB, embedding model, indexing strategy, and deployment topology
2–3 weeks
Working prototype with your data, benchmarked for latency and accuracy
2–4 weeks
Scaled implementation with monitoring, backup, and integration into your stack
1 week
Documentation, training, and handover so your team can operate independently
Timelines vary by complexity. Fixed-price quotes issued after the Discovery phase.
We work with the leading vector databases and embedding providers — no lock-in, no bias.
How we built a semantic memory system that transformed internal operations
"Digenio architected and deployed a vector database system that lets us search across thousands of client conversations with pinpoint accuracy. What used to take hours of manual review now takes seconds."
Four approaches that cover everything from architecture audits to fully managed vector database operations.
Everything you need to know before booking a consultation
Book a free 30-minute consultation. We'll assess your use case, recommend an approach, and give you a clear understanding of scope and investment — no commitment required.
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