AI Development
RAG Development Services
We build Retrieval-Augmented Generation (RAG) systems that answer from your documents—not guesses. Expect clean ingestion, tuned retrieval, citations, and an evaluation loop that improves accuracy over time. Delivery aligned to Canada teams (CAD).
Overview
What this service is
RAG combines search with LLM responses: it retrieves relevant source passages and then generates an answer grounded in those sources.
We engineer the full stack—connectors, chunking, embeddings, hybrid search, reranking, and access control—so retrieval is reliable in real conditions.
You get monitoring and evaluation tests so your team can iterate without breaking quality as content and prompts evolve.
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
Lower hallucinations with source-grounded answers
Better retrieval quality via hybrid search + reranking
Safer UX with citations and low-confidence fallbacks
Freshness via incremental indexing pipelines
Permission-aware access for internal/tenant data
Measurable accuracy via eval sets and regression tests
Features
What we deliver
Ingestion pipelines
Connect PDFs, docs, wikis, websites, help centers, tickets, and databases with clean normalization and metadata.
Chunking + metadata strategy
Right-sized chunks with stable IDs, versioning, and filters so retrieval stays accurate as content changes.
Vector database setup
Schema, indexing, backups, and performance tuning for Pinecone, Qdrant, Weaviate, or pgvector based on your constraints.
Hybrid search + reranking
Combine keyword + semantic retrieval and rerank results to improve precision for exact terms, error codes, and product names.
Citations + excerpt UX
Answers include sources and highlighted passages so users can verify and drill into the original content.
Evaluation + monitoring
Golden queries, regression checks, retrieval metrics, and dashboards so quality improves continuously after launch.
Process
How we work
Discovery
Sources, permissions, query goals, and evaluation criteria.
Ingestion
Connectors, parsing, normalization, and metadata.
Retrieval
Embeddings, vector DB, hybrid search, and reranking.
Answer UX
Citations, excerpt UI, and fallback behavior.
Evals + launch
Regression tests, monitoring, and rollout plan.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Support knowledge base assistant
Answer tickets from docs and policies with citations and escalation when evidence is weak.
Internal SOP / runbook search
Search across internal procedures and knowledge while respecting access rules and audit needs.
Product docs + developer assistant
Explain APIs, error codes, and configuration with precise citations and up-to-date versioning.
Compliance / policy Q&A
Ground answers in official policy docs, track sources, and provide safe “I can’t confirm” fallbacks.
Sales enablement assistant
Find the right case studies, pricing rules, and product positioning fast—without misinformation.
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
It depends on your constraints. Pinecone is managed and operationally simple, Qdrant/Weaviate are great for self-hosting, and pgvector is strong if you’re already on Postgres. We recommend based on scale, cost, and infra preferences.
Yes. We can implement per-user or per-tenant filtering, source-level permissions, and auth integration so retrieval respects access rules.
We improve retrieval quality, require citations/excerpts, add low-confidence fallbacks, and test with evaluation queries that reflect real user needs.
We build incremental indexing and scheduled refresh pipelines so new or updated docs are reflected without full re-ingestion.
Yes. Hybrid retrieval and reranking are often the biggest accuracy unlocks for production RAG systems.
A first production RAG system often ships in 3–6 weeks depending on sources, permissions, and UX complexity.
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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 RAG development?
Book a service call with Canada timezone overlap (North America overlap). CAD-based engagements.
Reply within 2 hours. No-pressure consultation.