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.
Start with evidence
Begin with a focused opportunity sprint
In 1–2 weeks, we map one consequential operation, test its automation case, and define a governed implementation plan before a larger build.
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.
Implementation Patterns
How we frame common AI workflows
Illustrative patterns only—not client case studies, endorsements, or production-result claims.
Regulated mobile data workflow pattern
Challenge: Sensitive data workflows need explicit access boundaries, traceability, and documented operating responsibilities.
Approach: Threat-model the workflow, map authorization rules, select encryption controls, and define auditable state transitions.
Validation: Test access boundaries and recovery paths, record residual risk, and obtain any required independent compliance assessment.
Large knowledge-base retrieval pattern
Challenge: Long, mixed-format source collections need traceable retrieval and safe behavior when evidence is weak.
Approach: Evaluate hybrid retrieval, reranking, citations, structured outputs, and defined fallback or human-review paths.
Validation: Use a representative offline evaluation set and report citation quality, latency, and cost under documented test conditions.
Operations automation pattern
Challenge: Approval and system-sync workflows need deterministic controls around exceptions, retries, and ownership.
Approach: Model the workflow, add validation and approval gates, and use AI only for bounded classification or extraction tasks.
Validation: Baseline manual steps, test exception paths and audit logs, then compare pilot measurements before considering wider rollout.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
Decision Guides
Not sure which to choose?
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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