Responsible AI in customer support
A checklist for Canadian support teams using AI for drafting, summarization, and self-serve knowledge base updates.
GovernanceSector
Request this brief
Structured briefs with Canadian context
Research that helps you decide
Our research briefs are designed to be practical: they include definitions, evaluation questions, and operational guidance. Each brief is written so that product, security, legal, and leadership stakeholders can align quickly and document decisions.
What’s inside each brief
- Summary, definitions, and known limitations
- Canada-specific considerations (privacy, governance, sector needs)
- Evaluation checklist and suggested pilot metrics
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Brief library
These examples show how we structure material. In real engagements, we tailor sections to your sector and constraints. Use the tags to skim quickly, then open related insights for more narrative context.
Data residency and deployment options
Trade-offs between hosted AI, private cloud, and hybrid approaches with practical questions to ask vendors.
InfrastructureGovernance
Related insights
AI for education and training
Guidance for educators and learning teams on accuracy, attribution, and safe use in Canadian settings.
Sector
Ask for a tailored version
Evaluation essentials for generative AI
A practical testing plan: quality, robustness, privacy exposure, and user experience monitoring.
Governance
Add to a playbook
Compute costs and capacity planning
A guide to estimating cost drivers for inference, embedding, and retrieval workloads in production.
Infrastructure
Discuss a forecast
Public sector procurement questions
A question set to compare vendors: transparency, auditability, security controls, and documentation.
SectorGovernance
Request a landscape review
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When to request a custom brief
Request a custom research brief when your team needs to make a decision that will be hard to reverse, such as selecting an AI platform, deploying customer-facing automation, or defining a policy for staff usage. Custom briefs reduce confusion by presenting a shared vocabulary and a written set of assumptions and trade-offs.
We also help teams build a lightweight evaluation harness: a set of test prompts, acceptance criteria, and monitoring metrics that can be reused as models and vendors change. This keeps decision-making consistent and helps prevent silent drift in quality or safety.