RAG Stack
Vector Database Setup
We set up vector databases for RAG and semantic search with production discipline: schema design, indexing strategy, hybrid retrieval, backups, and monitoring. Built for accuracy, latency, and operational reliability. Delivery aligned to Canada teams (CAD).
Start Small
Start small in 7 days
Three pilot-friendly options that reduce risk and ship value fast. Choose one, share access, and we deliver a production-ready baseline.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Benefits
What you get
Choose the right vector store for your constraints
Improve retrieval performance and latency
Avoid data quality issues with schema discipline
Operate confidently with backups and monitoring
Support hybrid retrieval for better precision
Scale indexing safely with incremental updates
Features
What we deliver
Store selection and architecture
Pinecone vs Qdrant vs Weaviate vs pgvector guidance based on scale, budget, and hosting preferences.
Schema and metadata design
Metadata filters, stable IDs, versioning, and permission-aware fields to support accurate retrieval.
Indexing and update strategy
Batch indexing, incremental updates, and re-index patterns to keep data fresh without downtime.
Hybrid retrieval support
Combine keyword + semantic retrieval and configure query strategies for production precision.
Backups and recovery
Backup strategy and recovery plan so index loss isn’t catastrophic.
Monitoring and alerting
Dashboards for query latency, errors, and index health so issues are visible early.
Process
How we work
Discovery
Requirements gathering and planning
Design
UI/UX design and prototyping
Development
Iterative sprints with demos
Launch
Deployment and support
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
RAG assistants
Power grounded assistants over docs and knowledge bases with metadata filters and performance tuning.
Semantic search
Upgrade keyword search to semantic discovery while keeping exact-match results where needed.
Recommendation and similarity
Similarity search for products, content, and entities with scalable indexing patterns.
Permissioned retrieval
Access-aware retrieval patterns for enterprise and multi-tenant environments.
Hybrid retrieval
Use hybrid search to improve precision on short queries and exact terms.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
Secure Mobile Solution in Australian Defence Ecosystem
Problem: Secure data workflows were required in a regulated environment with strict access controls.
Solution: Hardened architecture with strict auth, encrypted storage, and audit-friendly engineering patterns.
Outcome: Deployed securely within a regulated ecosystem with clear handoff and operational guidance.
AI Knowledge Base Across 2,000+ Pages
Problem: Teams needed fast answers across long PDFs, but search was slow and results were inconsistent.
Solution: RAG with hybrid retrieval and reranking, plus grounded answers and safer fallback behavior.
Outcome: Reliable answers with <10s response times and measurable improvements on real queries.
Ops Automation with AI + n8n
Problem: Manual approvals and CRM syncing created delays and data inconsistencies across tools.
Solution: Event-driven automation with validation gates and AI-assisted classification where it improved routing.
Outcome: Reduced manual workload significantly with more reliable workflows and operator visibility.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
FAQ
Frequently asked questions
Often yes for moderate scale. pgvector can be a great choice if you’re already on Postgres and want simpler ops. For very large scale or specialized needs, dedicated vector stores can be better.
Yes. Self-hosting offers control but requires ops. We can deploy and harden self-hosted setups with monitoring and backups.
We use incremental indexing and stable IDs so updates replace old vectors cleanly without full re-ingestion.
Yes. Hybrid retrieval is often the best path for production precision when exact matching matters.
Not always, but reranking can significantly improve results for dense datasets and ambiguous queries. We recommend based on evaluation results.
Yes. Monitoring and alerting are part of production readiness for vector store deployments.
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Regional
Delivery considerations for your region
Compliance & Data (Canada)
For Canadian teams, we focus on practical privacy and security: least-privilege access, clear boundaries, and reviewable operational controls.
We can align implementation with SOC 2 / ISO-friendly practices (without claiming certification) and support documented data flows.
- SOC 2 / ISO-friendly patterns (no certification claims)
- Least-privilege access and secure session handling
- Retention/deletion and export flows where required
- PII-safe logging + access boundary documentation
- NDA and vendor onboarding docs on request
Timezone & Collaboration (North America)
We work with Canadian teams with North America overlap and meeting windows that fit your schedule.
Delivery stays predictable via weekly milestones, async updates, and clearly documented decisions.
- North America overlap and responsive communication
- Async-first updates with written scope decisions
- Weekly milestone demos and progress checkpoints
- Clear escalation path for blockers
- Tight change control with clear sign-offs
Engagement & Procurement (Canada)
We support procurement-friendly delivery: clear scope, change control, and billing cadence aligned to milestones when appropriate.
We can invoice in CAD for CAD-based engagements where required.
- CAD-based engagements and invoicing options
- Milestone-based billing and scope sign-offs
- Time-and-materials for evolving requirements
- Vendor onboarding pack on request
- Optional paid discovery to de-risk delivery
Security & Quality (North America)
We keep quality visible: clean PRs, reviewable changes, and test coverage that matches the risk of each feature.
Performance budgets and release discipline help maintain stability as the product scales.
- CI-friendly testing: unit + integration + smoke tests
- Performance budgets + bundle checks
- Structured release notes + rollback-safe deployments
- Security checklist for auth, roles, and data flows
- Observability hooks (logs + error tracking) ready for production
Want help with vector database setup?
Share your requirements for Canada delivery. CAD-based engagements.
Reply within 2 hours. No-pressure consultation.