Selected engagements
These case sketches describe representative pilots—not vanity metrics. Client names are anonymized where confidentiality requires; outcomes reflect measured baselines agreed at kickoff.
Each engagement paired retrieval-augmented generation with human review, PIPEDA-aware data handling, and enablement so internal teams could operate the assistant after handoff.
Professional services · Internal RAG
A mid-size Toronto law firm indexed 14 years of matter notes, practice guides, and precedents behind a private assistant. Associates queried billing guidelines and conflict checks with citations to source paragraphs. Override rate on high-risk categories stayed below eight percent after six weeks; time spent searching shared drives dropped by roughly a third in surveyed practice groups.
Retail · Support copilot
A Canadian retailer with stores in four provinces deployed an agent-assist copilot grounded in return policies, warranty exceptions, and provincial consumer rules. Claude-backed drafts pulled from a refreshed Confluence library we helped restructure. Average handle time on policy-heavy tickets fell nineteen percent; escalations to legal review decreased because agents saw cited policy sections before sending replies.
Nonprofit · Documentation ops
A national charity consolidated fragmented PDF guidance into a tagged repository with quarterly owner reviews. We added a lightweight internal ChatGPT-style interface for program officers—not customer-facing—with logging and PIPEDA consent language in the onboarding flow. Documentation freshness scores improved from ad hoc to ninety-four percent current on scheduled audits.
SaaS · Discovery & guardrails
A B2B software vendor planned Microsoft Copilot rollout across support and success teams. We mapped data residency requirements, classified ticket fields that must never enter prompts, and delivered a guardrail specification adopted by their security review. Pilot scope narrowed to agent-assist only—avoiding a risky customer-facing launch before sources were cleaned.
Representative sectors include financial-adjacent professional services, multi-province retail, national charities, and venture-backed SaaS. Engagements are scoped after discovery; we do not publish fixed package pricing because source quality and integration depth vary widely. Book a discovery call to discuss a pilot shaped to your sources and reviewers.
We baseline before launch: search success rates, ticket handle time, reviewer burden, or internal survey scores—whatever matches the use case. LLM outputs are evaluated against held-out question sets written by client subject-matter experts, not generic benchmarks alone.
Pilots include a kill criteria section. If retrieval quality or adoption misses agreed thresholds, we recommend pause or redesign rather than pushing a production launch to meet a calendar date.
We share weekly readouts during implementation: retrieval hit rate, citation accuracy on the evaluation set, override reasons tagged by reviewers, and qualitative notes from enablement sessions. That transparency helps Canadian leadership teams decide whether to expand scope, adjust sources, or redirect budget toward documentation cleanup instead of model tuning.
Case summaries are illustrative composites where anonymization applies. Past performance does not guarantee similar results; your sources, staff workflows, and regulatory context will differ.
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