RAG Stack
Hybrid Search & Reranking
We improve retrieval quality for RAG and search systems using hybrid search, reranking, and query strategies—validated with evaluation sets so results are measurable and stable.
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
Increase precision for short and exact queries
Reduce irrelevant retrieval that causes bad answers
Improve consistency across content changes
Make search quality measurable with eval sets
Support multilingual and domain-specific terms
Improve UX with better ranking and snippets
Features
What we deliver
Hybrid retrieval configuration
Combine keyword and semantic retrieval and tune weights based on your query distribution.
Reranking
Rerank candidate results for higher precision, especially in dense corpuses and ambiguous queries.
Query strategies
Query rewriting, metadata filters, and structured retrieval strategies for better relevance.
Evaluation sets
Golden queries and scoring to measure improvements and prevent regressions over time.
Snippet and citation UX
Better snippet selection and citations so users can verify results quickly.
Monitoring and iteration
Ongoing telemetry and feedback loops for continuous search quality improvements.
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
Improve RAG answer accuracy
Better retrieval results reduce hallucinations and increase answer relevance with citations.
Upgrade product documentation search
Improve precision for exact terms, error codes, and feature names.
Internal knowledge search
Help teams find answers faster with ranked results and permission-aware filtering.
Sales enablement search
Find the right content and messaging quickly without misinformation.
Compliance and policy search
Improve relevance and traceability for policy-heavy corpuses where accuracy matters.
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
Decision Guides
Not sure which to choose?
FAQ
Frequently asked questions
Not always, but it often improves precision for exact terms and short queries. We validate with evaluation sets before recommending a final approach.
Reranking is often the biggest accuracy unlock for production retrieval, but it adds cost. We recommend based on evaluation results and budget constraints.
We define golden queries and scoring criteria, then track metrics over time to ensure quality improves and stays stable.
Reranking can add latency. We design for performance by tuning candidate sizes, caching, and choosing cost-effective ranking strategies.
Yes. Hybrid retrieval and reranking can be implemented across common vector stores and search backends.
Yes. Search UX (snippets, filters, citations) is often as important as retrieval quality for user trust.
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