Private AI Knowledge Systems for Canadian Teams
A RAG knowledge system is an AI assistant that answers from your own documents with cited, auditable sources instead of guessing. MicroPyramid builds private RAG-powered copilots, support assistants, and semantic document search for Canadian public sector, legal, and SaaS teams: query your institutional knowledge in natural language, with source citations and role-based access.
Why Canadian Organisations Need Private RAG
Canadian federal departments, large regulated enterprises, and provincial health authorities are rarely comfortable feeding sensitive records into public AI APIs. Personal information shared with a third party is still yours to answer for, and for many Canadian organisations the simplest answer is keeping data within Canada's borders entirely.
Private RAG systems solve this cleanly. Documents are indexed and retrieved within your own environment; LLM inference runs on-premise or in AWS ca-central-1 (Canada Central). Nothing leaves Canada, and access controls and audit logs show exactly who saw what.
We've been building secure, production AI systems for 12+ years. We know that "privacy compliant" for a Canadian federal agency or a large regulated enterprise means more than a terms-of-service checkbox. It means auditable architecture, documented data flows, and a system your risk and compliance teams can review. That's how we build.
What We Build for Canadian Teams
Six types of private RAG-powered knowledge systems, each shaped for Canadian data-residency and audit needs
Internal Knowledge Copilot
Give your Canadian team a private retrieval assistant over internal policies, SOPs, and compliance guides, with citations, role-based access, and audit trails your compliance team can review.
- Document ingestion pipeline
- Semantic retrieval with citations
- Role-based access control
AI Support Assistant
Turn your support docs, product FAQs, and ticket history into an intelligent first-line assistant, built for Canadian SaaS companies managing bilingual or multi-provincial customer bases where accurate, cited answers matter.
- Knowledge ingestion & indexing
- Retrieval-backed answers
- Fallback & escalation logic
Enterprise Document Search
Replace keyword search with semantic retrieval across contracts, regulatory filings, policy documents, and compliance records, built for the document-intensive realities of Canadian public sector, legal, and professional services teams.
- Semantic search & ranking
- Multi-format document support
- Filters & faceted navigation
Professional Services Knowledge Q&A
Secure retrieval over engagement files, methodology guides, proposal libraries, and quality manuals for Canadian consulting and engineering firms, with cited answers that cut research time.
- Engagement file retrieval
- Role-based access controls
- Audit logging
Private Document Q&A
Access-controlled Q&A over sensitive documents (client files, crown corporation records, legal briefs, and board papers) deployed in AWS ca-central-1 (Canada Central) so the data stays in Canada.
- On-premise or private cloud
- Canadian data residency (ca-central-1)
- Audit logging
Secure RAG with Citations
Every answer is attributed to its source with page-level citations, auditable, trustworthy, and safe for Canadian regulated sectors from federal public service to provincial health authorities and law firms.
- Source-attributed answers
- Confidence scoring
- Hallucination mitigation
Custom RAG, Microsoft 365 Copilot, or Glean? How to Choose
Now that Microsoft 365 Copilot is rolling out across Canadian enterprise and the federal government, the real question isn't "AI or not". It's which approach fits your data, your residency obligations, and how much you want to own. Here's the honest breakdown.
Custom RAG (what we build)
Own it outright
A private retrieval system grounded in your own documents, with page-level citations, your own access rules, and deployment in AWS ca-central-1 (Canada Central) or on-premise. You own the source code and IP, with no per-seat licence, and a fully on-premise option where data must never leave your own environment.
Choose it when
your knowledge lives outside Microsoft 365, you need Canadian data residency or on-premise, you want answers embedded in your own product, or you need auditable citations and access control you govern.
Microsoft 365 Copilot
Productivity layer
Generative AI woven through Word, Outlook, Teams, and SharePoint. Strong when your knowledge already lives inside Microsoft 365 and generic, conversational answers are good enough for the task.
Choose it when
your content is already in M365, you accept per-seat licensing, and you don’t need custom citations, bespoke access rules, or residency guarantees beyond what the tenant gives you.
Glean
Horizontal search
A SaaS enterprise-search platform with prebuilt connectors across many tools. Useful for large organisations wanting cross-app search out of the box, accepting a third-party platform in the data path.
Choose it when
you’re a large org that wants connector-based search across many SaaS tools immediately and you’re comfortable with a vendor platform processing your index.
In practice many Canadian teams run both: Microsoft 365 Copilot for everyday productivity inside the Office suite, and a custom RAG system for the regulated, sovereign, or product-embedded knowledge Copilot can't reach. We'll tell you when off-the-shelf is the right call, including when not to hire us.
Best Fit For
- you have policies, regulatory docs, compliance manuals, or internal knowledge Canadian teams need to query quickly
- answers need citations and audit trails your risk and compliance teams can review
- you require data residency: all data stays in AWS ca-central-1 or your own Canadian infrastructure
- you need retrieval-backed answers grounded in your own regulated data, not a public internet model
Not the Right Fit When
- you mainly need AI embedded inside an existing product workflow rather than a standalone knowledge system
- your source content is thin, inconsistent, or not yet ready to index
- you expect autonomous answers without guardrails in regulated public-sector or legal workflows
- the goal is a public-facing generic chatbot with no grounding in your own documents
If you need AI embedded inside an existing product workflow, start with AI Feature Development instead.
Why Canadian Teams Work With Us
12+ years of delivery experience, shaped to fit Canadian privacy law and CAD commercial terms
Privacy by Design, Data in Canada
Data minimisation, access controls, and audit trails are designed in from discovery, with default deployment in AWS ca-central-1 (Canada Central) and an on-premise option when data must stay inside your own environment.
CAD Billing via Stripe
Invoiced in Canadian dollars via Stripe. No US-dollar conversion, no cross-border FX overhead, straightforward, transparent commercial terms for Canadian businesses and public-sector procurement.
Engineers with Direct Access
You talk to the engineers building your system, not a project coordinator. The same team that ran discovery writes the code, answers questions on Slack, and shows up to sprint reviews.
How We Deliver
A focused, low-risk process designed to get Canadian teams from problem to working system fast
Discovery & Scoping
Map Canadian use cases, identify data sources, define privacy and access requirements, and set success metrics
Data Preparation
Document ingestion, chunking strategy, embedding pipeline, and vector index, hosted in ca-central-1 by default
RAG Architecture
Retrieval system design, LLM selection (private or API), prompt engineering, and context management
Build & Deploy
UI integration, accuracy testing, staged deployment, and monitoring, with full handover documentation
RAG & AI Technology Stack
We select models and infrastructure based on your Canadian data-residency, privacy, and performance requirements, not on defaults
AI & Retrieval
Data & Storage
How to Get Started
We recommend a Discovery Sprint: low risk, clear output, a privacy and access-control review, and a foundation for everything that follows
RAG Discovery Sprint
Map your use case, assess data sources, and get an architecture and privacy-aware implementation roadmap
- Use-case mapping & data review
- Architecture recommendation
- Privacy and access-control review
- Implementation roadmap
Knowledge Copilot MVP
Full build of a retrieval-based assistant with UI, source citations, and Canadian data residency
- Document ingestion pipeline
- Retrieval + LLM integration
- Web interface with access control
Ongoing RAG Expansion
Continued iteration on your AI knowledge system as your data, regulations, and use cases evolve
- Additional data sources
- Quality & accuracy improvements
- Analytics & monitoring
AI Work in Production
Two products we built and run ourselves, plus a client platform with AI feedback shipped inside the product.
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Read the case studyFrequently Asked Questions
Straight answers to what Canadian founders, CTOs, and compliance leads ask before building a RAG knowledge system.
What is a RAG knowledge system?
A RAG (retrieval-augmented generation) knowledge system is an AI assistant that retrieves the most relevant passages from your own documents and uses them to generate an answer with cited sources, instead of relying on what a language model memorised from the public internet. Because every answer is grounded in your content and attributed to its source, it stays accurate, auditable, and current as your data changes, which is what makes it safe for Canadian regulated, public-sector, and enterprise work.
Can you build a RAG system with full Canadian data residency?
Yes. We deploy by default in AWS ca-central-1 (Canada Central, Montréal), with ca-west-1 (Calgary) as a second Canadian region, so your documents and embeddings never leave Canada, and we can run the entire system on-premise or in your own private cloud where a federal department or DPO requires it. Data minimisation, role-based access, and full audit logging are built in from day one.
How do you stop the AI from hallucinating or inventing answers?
Every answer is grounded in retrieved passages and attributed with page-level citations, so a user can verify the source before trusting it. We add confidence scoring, fallback and escalation logic when retrieval is weak, and an evaluation pass on your real questions before launch, so the system says “I don’t know” or escalates to a human rather than making something up. For Canadian government, health, and legal teams, that auditability is the difference between a usable tool and a compliance risk.
Can it work for large regulated enterprises, government, or bilingual teams?
Yes. Cited, access-controlled retrieval is a strong fit for large or regulated enterprises, law firms, and Canadian federal and provincial public bodies: secure Q&A over compliance manuals, regulatory guidance, policy libraries, and clinical or legal documents, with source attribution so nothing gets misquoted. Canadian data residency, audit logging, and per-team access controls are designed in, not bolted on. We can deliver bilingual (English and French) interfaces and retrieval for Quebec and federal users, something most vendors in this market overlook.
You’re an offshore team. How do you work with Canadian teams and handle data sovereignty?
Two different concerns, and we answer both. On working together: you work directly with the engineers who scoped your system on Slack, not an account-manager relay, and we invoice in Canadian dollars via Stripe with no US-dollar conversion overhead. On sovereignty: we deploy your system in AWS ca-central-1 or on-premise, so your data stays in Canada. Where data must never leave your own environment, we run the whole system on-premise or in your own private cloud.
What drives the cost of an AI knowledge system?
Cost is driven by the number and messiness of your data sources, how much cleaning and chunking the documents need, your access-control and audit requirements, whether you deploy in AWS ca-central-1 or fully on-premise, whether you need bilingual EN/FR support, and how deeply the copilot integrates with your existing systems. We scope the smallest valuable version first in a discovery sprint and give you a fixed estimate in Canadian dollars before any build begins, so there are no surprises. Pricing is handled directly in conversation, not published as a one-size band.
Do we own the source code and IP?
Yes. You own all source code and intellectual property we build, committed to your repositories as we go, so there is no vendor lock-in and no per-seat platform rent if you later bring the system fully in-house.
Ready to Build Your Canadian Knowledge System?
Start with a free discovery call. We'll assess your use case, your privacy requirements, and your data sources, and propose a concrete first step with no obligation.