Skip to main content

Built by us. Running in production.

Products and platforms we've shipped. Here's what impact and delivery look like.

AI Products
Automation
Labour Tech
SaaS
DevSecOps
Case Study

Sertus — AI for Canadian Labour Relations

Union stewards and HR teams spend hours digging through collective agreements, tracking grievance deadlines, and preparing for meetings. Most of that knowledge lives in people's heads. When they leave, it leaves with them.

Sertus searches collective agreements, flags relevant clauses, generates meeting talking points, and tracks every grievance from filing through arbitration. The AI cites its sources so stewards can verify everything.

Research that used to take hours now takes minutes. And the institutional knowledge stays in the system, not in someone's head.

S
Sertus AI
Analyzing
What are the notice requirements for this grievance?
Based on Article 12.3 of the collective agreement, written notice must be filed within 15 business days of the incident...
Source: CA §12.3 — Grievance Procedure Timeline

Cites its sources

Every answer grounded in the applicable collective agreement

Institutional memory

Past case handling captured, searchable, and never lost to turnover

Canadian data, Canadian law

Federal and provincial frameworks, data stored in Canada

Case Study

Kindgi — AI-Native Automation

Most automation platforms run on if/then rules. When a process has exceptions (and they always do), the rules break and a person steps in.

Kindgi uses AI agents instead of rigid rules. A process that used to take a team of three to route, review, and approve can run on its own. When something falls outside the rules, the agent escalates it. The rest just moves.

Workflows built by AI agents, not rigid rulesEscalation for edge cases, not everythingRuns across your existing tools
Intake
Route
Process
Review
Complete
Case Study

Verx — Dependency Security for Dev Teams

Triaging CVEs, figuring out which packages can be safely bumped together, chasing breaking changes across monorepos. Dev teams spend days on this every cycle. Nobody enjoys it.

Verx scans full dependency trees against CVE databases and clusters related packages for safe batch updates. AI agents run in isolated containers to fix breaking changes and push ready-to-review PRs. Instead of dozens of individual PRs per dependency, you get a phased upgrade plan.

It also maps blast radius: which files, imports, and downstream packages a change touches. So you know what breaks before you merge.

react@18.2.0safe
lodash@4.17.15CVE
express@4.18.2safe
axios@0.21.1CVE
next@15.5.12safe

Pick the case study closest to your problem.

We'll walk you through how we'd approach yours.