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 United States teams (USD).
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 (US)
For US teams, we build with auditability in mind: clear access boundaries, least-privilege roles, and reviewable operational controls.
We can align delivery with SOC 2 / ISO-friendly practices (without claiming certification): evidence-ready logs, secure-by-default config, and clear ownership.
- SOC 2 / ISO-friendly implementation patterns (no certification claims)
- Least-privilege access and permission boundaries
- Security review checklists for auth, payments, and data flows
- PII-safe logging + incident response playbooks (on request)
- Retention and deletion flows where required
- NDA + vendor onboarding docs on request
Timezone & Collaboration (Americas)
We support teams across the Americas with meeting windows that work for EST/CST/MST/PST.
We keep delivery predictable with weekly milestones, concise async updates, and written decisions to reduce calendar load.
- Americas overlap with EST/PST-friendly windows
- Async-first updates with written decisions
- Weekly milestone demos + change control
- Fast turnaround on blockers and clarifications
- Clear owner per workstream and escalation path
Engagement & Procurement (US)
US-friendly engagement structure: clear SOWs, milestone billing, and invoice cadence that fits typical procurement workflows.
If you need vendor onboarding artefacts, we can provide security posture summaries and delivery process documentation.
- USD invoicing and milestone-based payment schedules
- SOW + scope lock options for fixed-scope work
- Time-and-materials for evolving requirements
- Procurement-ready documentation on request
- Optional paid discovery to de-risk delivery
Security & Quality (US)
We ship with a security-first checklist and performance budgets—so releases stay stable under real traffic.
Expect clean PRs, reviewable changes, and production-ready testing from day one.
- Threat-aware checks for auth, roles, and sensitive data flows
- CI-friendly testing: unit + integration + critical path smoke tests
- Performance budgets (Core Web Vitals-minded) and bundle checks
- Structured logging + error tracking hooks (Sentry-ready)
- Rollback-safe releases and clear release notes
Want help with vector database setup?
Share your requirements for United States delivery. USD-based engagements.
Reply within 2 hours. No-pressure consultation.