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 Kingdom teams (GBP).
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 (UK/EU)
For UK teams, we default to GDPR-first thinking: data minimisation, purpose-limited storage, and clear access boundaries.
We can work under a DPA (template available on request) and implement practical retention/deletion flows when needed.
- GDPR-first patterns (minimise, restrict, document)
- DPA template available on request
- Retention/deletion and export flows where required
- Least-privilege access and secure session handling
- PII-safe logging + secure-by-default configuration
- NDA available for early-stage discussions
Timezone & Collaboration (UK/EU)
We align to UK time and EU overlap (GMT/BST with CET-friendly windows) for fast feedback cycles.
We keep the process lightweight: async updates, clear priorities, and written decisions to avoid ambiguity.
- UK/EU overlap with GMT/BST windows
- Async-first delivery with documented scope
- Weekly milestones and structured demos
- Clear escalation path for blockers
- Tight change control with clear sign-offs
Engagement & Procurement (UK)
We support typical UK procurement flows with clear scopes, change control, and invoice cadence.
If you prefer a discovery-first engagement, we can run a short paid discovery to lock requirements before build.
- GBP-based engagements and invoicing options
- Discovery-first option to reduce delivery risk
- Milestone-based billing when appropriate
- Transparent change control and sign-offs
- Vendor onboarding pack on request
Security & Quality (UK/EU)
We build for reliability and maintainability: clean PRs, tight review loops, and test coverage that matches risk.
Performance budgets and release checklists keep launches predictable—especially when multiple stakeholders review changes.
- CI-friendly testing: unit + integration + smoke tests
- Performance budgets + bundle checks (Core Web Vitals-minded)
- Structured release notes and 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?
Get a clear plan for United Kingdom teams—scope, timeline, and next steps. GBP-based engagements.
Reply within 2 hours. No-pressure consultation.