Outcomes
Concrete outcomes from real engagements. We name the sector. We name the result. We leave out the client name until they tell us we can use it.
A company’s revenue ran through a script on their customers’ websites. Every customer change broke it. Detection took weeks. We rebuilt the loop: a heartbeat watching the integration, a classifier for known failure modes, an agent that fixes the knowns and pages on the unknowns.
Thousands of dollars per month, recovered automatically.
Pattern: SRE-style self-healing applied to revenue-critical integrations
Senior managers had stopped trusting the dashboard. The data was right; the time to read it was wrong. We rebuilt the reporting layer so the AI does the math and they do the calls — with the audit trail intact.
From 12 hours of analyst time to 20 minutes of review. Trust restored.
Pattern: separating computation from judgment
Different sector. Same shape of problem. Same shape of fix.
What we sell
Outcomes, not deliverables. A different number on the dashboard, 90 days out.
The consultancy’s judgment was the asset. The interviewing, scoring, and write-up were not. We separated them: structured intake, AI-assisted scoring against PIPEDA, Quebec Law 25, CASL, GDPR, and forthcoming Canadian AI legislation, human judgment on the recommendations.
Audit cycle cut by 85%. Rigor retained. Capacity unlocked.
Pattern: industrialising the repeatable, protecting the judgment
Three teams had three definitions of “active customer.” The CEO was getting three different numbers in the same meeting. We deployed a semantic layer that defines each KPI once — governed, versioned, consumed by every report.
Installation smooth. Adoption strong. Decision-quality results to follow.
Pattern: language problems disguised as data problems
The pattern travels. The context doesn’t.