Search and vector databases are the backbone of AI applications, semantic search, and enterprise knowledge systems. We work with Elasticsearch, OpenSearch, Pinecone, Weaviate, pgvector, and Milvus—whichever fits your use case. Perth-based, working across Australia.
Search databases like Elasticsearch and OpenSearch index your content for fast full-text search. Think e-commerce product search, log analytics, document search. Anywhere you need to find things by keywords or filters.
Vector databases like Pinecone, Weaviate, and Milvus work differently. They store data as mathematical embeddings—numerical representations that capture meaning. So you can search for "staff member who speaks Mandarin" and find the right person even if "Mandarin" never appears in their profile. That's semantic search. It's what powers RAG applications, recommendation engines, and image similarity search.
Hybrid solutions like pgvector (a PostgreSQL extension) and Elasticsearch with dense vectors give you both—keyword search and semantic understanding in one system.
We help businesses pick the right database, get it running properly, and tune it for production. Building an AI application? Modernising clunky legacy search? Adding semantic smarts to an existing system? That's what we do.
These databases show up in more places than you'd expect.
Search across documents, emails, wikis, and databases. Find the policy document you need in seconds, not hours.
RAG systems that answer questions from your documents. Ask in plain English, get accurate answers with sources.
Search that understands "warm jacket for Perth winter" and returns relevant results—even without exact keyword matches.
Full-text search across billions of log entries. When the app breaks at 3am, you need to find the problem fast.
"Customers who viewed this also viewed..." Vector similarity makes products similar by meaning, not just category.
AI agents need access to your knowledge base. Vector search gives them accurate context to answer questions and take action.
We work across all the major platforms. Each has tradeoffs—we help you pick the right one.
Elasticsearch is the industry standard for distributed search. If you've used decent product search on an e-commerce site, or seen a DevOps team debugging from logs, Elasticsearch was probably behind it.
We set up Elasticsearch clusters for full-text search, log analytics, and application search. That includes designing the cluster architecture, building custom analysers for Australian content (proper handling of "colour" vs "color", local place names, that sort of thing), and tuning relevance so results actually make sense.
We also set up Kibana dashboards for monitoring search performance—so you can see what people are searching for and whether they're finding it.
Need ML-powered relevance ranking? Our AI development team handles that too.
Regular databases can do basic search. But when you need speed, scale, or semantic understanding—you need something purpose-built.
Millions of documents, milliseconds to find what you need. Not minutes. Milliseconds.
Vector databases get meaning, not just keywords. Search for "staff who can speak Mandarin" and find the right people even if that phrase never appears in their profile.
Start with thousands of documents now. Scale to billions later. These systems grow with you.
Role-based access, encryption at rest and in transit, audit logging. The compliance boxes your IT team needs ticked.
Native embedding support means these databases plug straight into RAG systems, recommendation engines, and AI search. No workarounds.
Self-hosted, cloud-managed, or hybrid. Deploy where it makes sense for your compliance and budget.
Five phases from requirements to handover. No surprises.
What are you actually searching for? How much data? We figure out the requirements and recommend a database that fits.
Cluster sizing, indexing strategy, how it connects to your apps. We spec the whole thing before writing code.
Deploy the database, configure indexes, wire up the embedding pipeline, build the API layer. The actual building.
Move your existing data over. Set up ongoing sync if needed. Make sure nothing gets lost in the process.
Tune performance, cut unnecessary costs, document everything, train your team. Then hand over the keys.
See how we've helped Australian businesses with search and AI infrastructure
Leading Australian Insurance Provider
Multi-Location Healthcare Network
National Retail Chain
A vector database stores data as mathematical vectors (embeddings) that capture meaning—not just words. Search for "staff who speak Mandarin" and find the right people even if that exact phrase never appears anywhere. That's semantic search. It's what powers RAG systems, recommendation engines, and AI-powered assistants. If you want any of that, you need a vector database.
Elasticsearch is brilliant at keyword search with filters and aggregations. Type "red shoes size 10" and it finds exact matches fast. Vector databases like Pinecone excel at similarity—finding things that are conceptually related even without keyword overlap. The lines are blurring though: Elasticsearch now supports vectors, Weaviate does hybrid search. We help figure out which fits your use case.
Deep dives on vector databases, RAG architecture, and when to use what.
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