Hybrid, semantic and vector search that makes your RAG systems and agents accurate - and your search experiences fast. We architect, tune and operate Elasticsearch for relevance, latency and cost.
Whether users type into a search box or ask an assistant, the answer is only as good as what gets retrieved. We make that layer precise, fast and affordable.
Judgment lists, offline metrics and A/B tests - so ranking changes are proven, not guessed.
Hybrid keyword + vector search that feeds RAG systems and agents the right context.
Query, index and shard design tuned for low latency as data and traffic grow.
High availability, data tiering and right-sized infrastructure that does not overspend.
The search layer behind assistants and agents that must answer from your data.
Product discovery that understands intent, handles typos and converts.
Fast, precise search over large collections of reports, filings and content.
Semantic search across help centers, wikis and internal knowledge.
Scalable log and event analytics with sensible retention and cost.
Version upgrades, re-architecture and platform moves without downtime.
Audit your cluster, queries and relevance, and agree on latency, quality and cost targets.
Prove the changes - hybrid retrieval, new mappings, tuned ranking - against your real queries.
Roll out safely with benchmarks, monitoring and zero-downtime migration where needed.
Continuous relevance and performance tuning - or a clean handover with runbooks and training.
Hands-on help designing, building and running Elasticsearch: cluster architecture, hybrid and vector search, relevance tuning, RAG retrieval for AI applications, performance and cost optimization, security, upgrades and migrations, and troubleshooting.
Yes. Elasticsearch supports dense vector search alongside its keyword engine, which makes it a strong fit for hybrid retrieval in RAG systems and agent tools. If you already run Elasticsearch, you can often add semantic retrieval to it rather than introducing a separate vector database.
It depends on scope and duration. We offer fixed-scope projects, time-and-materials engagements and monthly retainers. Book a call and we will give you a clear estimate for your situation.
A focused performance or relevance assessment typically takes 1-2 weeks. Architecture redesigns, AI search builds or migrations typically take 4-12 weeks depending on data volume and complexity. We agree on timelines and success metrics up front.
Yes. We offer ongoing retainers covering monitoring, performance and relevance tuning, upgrades and troubleshooting, plus training so your team can become self-sufficient.
Tell us about your cluster and what you want it to do. In a free 30-minute call we will review where you are, what it would take, and how we would measure success.
