Technology
Vector Databases
Implement vector search with the right retrieval strategy—embeddings, indexing, filters, and performance tuning designed for real products.
Best For
Ideal use cases
Applications requiring semantic search
RAG (Retrieval Augmented Generation) systems
Recommendation engines
Similarity search applications
Projects with large embedding datasets
What We Build
Projects we deliver
RAG systems for chatbots
Semantic search applications
Document similarity systems
Recommendation engines
Content discovery platforms
Question-answering systems
Knowledge bases with search
Personalization systems
Ecosystem
Compatible tools & integrations
Seamless Integrations
Works with your existing stack
Use Cases
Recommended use cases
Chatbots needing context from documents
E-commerce product recommendations
Content platforms with semantic search
Knowledge bases with question-answering
Applications requiring similarity search
Delivery
How we deliver
We design vector database schemas for optimal query performance
Implement proper embedding generation and storage
Set up hybrid search (vector + keyword) when needed
Optimize index configuration and query parameters
Implement proper data synchronization and updates
FAQ
Frequently asked questions
Pinecone is best for managed, production-ready solutions. Weaviate offers open-source flexibility. pgvector works well if you're already using PostgreSQL. We recommend based on your requirements.
We use OpenAI's embeddings API, sentence-transformers, or other embedding models. We choose models based on your data type and language. Embeddings are generated during indexing and stored in the vector database.
Yes. We can self-host Weaviate, Qdrant, or use pgvector with PostgreSQL. Self-hosting offers more control but requires infrastructure management. Managed services like Pinecone simplify operations.
AI
Add AI on top of this stack
Two common AI services that pair well with this technology, plus the evidence-first buyer sprint.
Regional
Delivery considerations for your region
Data and risk discovery (Australia)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (Australia)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (Australia)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (Australia)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
Want to scope this properly?
Need vector search for AU users? Share your data and queries and we’ll propose the right architecture. AUD-based engagements.
We’ll review the context and reply with a practical next step.