Arupjyoti Nath
Started in engineering. Ended up in marketing. Now building AI into the gap between the two.
Based in Burlington, Ontario. Open to senior marketing, AI operations, and marketing technology roles.
Capabilities
How I work
Real work
My background spans a few different areas — depending on what you're looking for, some of this will matter more than the rest. Here's a starting point:
Before AI agents
In 2022, before AI agents existed, the same problem needed solving — route the right lead to the right person, automatically, with no manual reporting. Built in Parabola. The logic is the same. The technology caught up.
The Parabola system was deterministic — rule-based filters, fixed logic, predictable outputs. It worked well for structured data. The AI agents apply the same core instinct to unstructured work: one input, intelligent routing, the right output to the right person — but now with a confidence gate, an LLM making classification decisions, and a human-in-the-loop fallback when certainty is too low to act. Same problem. Smarter solution.
AI Agents
The best sign that an agent is working well is that the person using it doesn't think about it.
Live demos
Choose how you want to explore:
↓ Click any tool to run it live — real AI output, not a demo.
How I Build
Regulated Environment
Here's what that actually looks like.
Live Tool
Pick the thing that slows your team down most. I'll tell you what an AI workflow would look like for it — including the governance layer if you're in a regulated environment.
Thinking
The one thing that determines whether a team actually uses AI — or quietly stops.
The first workflow I built without a confidence gate failed in three weeks. The AI wasn’t producing bad output. It was producing mediocre output, consistently, and nobody could tell the difference until they checked.
People started checking everything manually. Then editing heavily. Then stopped using it. If you’re rewriting 80% of the output, it’s not automation — it’s just extra work with extra steps.
"People don't stop using AI because it's bad. They stop because they can't tell when to trust it. A confidence score fixes that."
A confidence gate is simple. Every output gets a score. Below a threshold, it goes to human review. Above it, it goes straight through. The AI flags its own uncertainty instead of hiding it.
What it does psychologically matters more than what it does technically. When people know the system will flag what it’s unsure about, they stop worrying about what might slip through. That’s what gets you from three early adopters to the whole team using it.
In a regulated environment, this becomes the compliance architecture. Anything that might contain a misleading claim or an imprecise disclosure routes to the reviewer automatically. The AI isn’t bypassing compliance — it’s making compliance faster by only surfacing what actually needs a human to look at it.
The teams that actually get AI to stick all did the same thing: they built the governance layer first. The confidence gate, the audit trail, the kill-switch — not constraints. The conditions that make it safe to let the system do more.
Credentials
Completed in 2025 and 2026.
Background
I started in electrical engineering, which meant I spent years learning how to think about why things break before I ever thought about how to sell anything. It turned out that instinct — trace the problem to its source, don't patch the symptom — was more transferable than I expected.
After a Master's in Electronics Business Technology at the University of Ottawa and some early roles in analytics and campaign work, I spent a decade in marketing where the results were actually my responsibility. Paid media at 417 Automotive — where the cost per deal dropped 35%. Analytics and reporting at Schneider Electric. Demand generation at an early-stage SaaS startup where paid traffic grew 62% and sales leads followed at 57%. None of it was advisory work.
At Zymbyo, I was CMO and CPO at the same time — which meant owning the marketing function, the product roadmap, a team of five, and 15+ client accounts simultaneously. I built a product-led growth motion that acquired 29 clients across the US and Canada through product value alone — no outbound sales — and 7 of them converted to paid within six months. That's a 24% free-to-paid rate against a 2–5% industry average. I also built Zymbyo's AI platform from scratch — from the first integration to a production system with 21 live tools, 6 agent architectures, and 1 fully deployed production agent.
The team-building side mattered as much as the results. At Zymbyo, that meant recruiting into a startup with no inherited playbook — setting direction, maintaining accountability, and keeping a lean team productive under real pressure. At 417 Automotive and Schneider Electric, it meant working across functions and reporting to senior leadership where the work either spoke for itself or it didn't.
I'm looking for a role where commercial accountability, AI implementation, and team-building sit in the same seat — and where the work is measured by what it produces, not what it resembles. Burlington, Ontario. Open to remote and hybrid.