RAG Stack
Knowledge Base Engineering
RAG quality depends on the knowledge base. We engineer content structure, ownership, freshness workflows, permissions, and ingestion so assistants stay accurate and auditable—not stale or contradictory. 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
Improve RAG accuracy by fixing the source content layer
Reduce contradictions and stale answers
Add ownership and update workflows
Implement permission-aware structures
Enable incremental indexing and freshness checks
Create an audit-friendly knowledge system
Features
What we deliver
Information architecture
Structure content into consistent hierarchies (topics, products, versions) so retrieval has clean boundaries.
Ownership and update workflows
Define owners, review cycles, and change tracking so knowledge stays fresh and trustworthy.
Permission-aware organization
Design access boundaries and metadata so retrieval respects roles and tenant isolation.
Ingestion and normalization
Normalize docs, wikis, and help centers into consistent formats with stable IDs and metadata.
Freshness and drift monitoring
Detect stale content and drift using change signals and evaluation queries.
Quality playbooks
Guidelines for authorship, formatting, and content updates that keep RAG quality high long-term.
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
Support knowledge base cleanup
Reduce contradictory answers by restructuring and standardizing support content and ownership.
Enterprise internal documentation
Permission-aware knowledge architecture for SOPs, policies, and internal runbooks.
Product documentation organization
Versioned content structure so assistants answer correctly for the right product versions.
Multi-source knowledge consolidation
Consolidate Notion/Confluence/Drive/Help Center into a coherent retrieval-ready structure.
RAG quality stabilization
Fix knowledge issues that cause retrieval errors and hallucination-like behavior in answers.
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
If the knowledge base is inconsistent, stale, or poorly structured, retrieval will surface weak sources. Knowledge engineering fixes the root content layer so retrieval improves.
Not always. We usually restructure, normalize, and add ownership/workflows first, then improve high-impact content iteratively.
Yes. We can connect and normalize content from Notion/Confluence/Drive/Help Centers depending on access constraints.
We implement owners, review cycles, change tracking, and incremental indexing so updates are systematic, not ad-hoc.
Yes. We design permission-aware structures and metadata so retrieval respects access boundaries.
No. Better knowledge architecture improves human search and onboarding too, but it’s especially valuable for reliable AI outputs.
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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 knowledge base engineering?
Share your requirements for Canada delivery. CAD-based engagements.
Reply within 2 hours. No-pressure consultation.