Chris Marcus

I help companies building AI products in regulated industries - insurance, healthcare, fintech - solve the technical leadership problem that stalls most of them: finding someone who understands AI deeply enough to make real architectural decisions, has shipped in environments where compliance isn't optional, and has scaled engineering organizations past the messy middle stage.

That combination is rare. Most AI-fluent technical leaders haven't worked in regulated industries. Most regulated-industry CTOs are still figuring out how to move beyond POC-stage AI. And most people who claim both haven't actually built and shipped an AI-native product themselves.

I have. Here's how.

The Challenge

Independent insurance agencies generate billions in premiums but run on legacy management systems built decades ago. Producers waste hours manually reviewing policy data that modern AI can analyze in seconds. The market needed a platform purpose-built for independent agencies that combines deep insurance data integration with production-grade AI analysis.

What I Built

The Tech Stack

Why This Matters

Building agentCanvas.ai wasn't just a product exercise - it was a forcing function to develop a repeatable operating model for AI-native engineering. Before writing a line of product code, I spent three to four months designing prompting standards, repo structure conventions, task decomposition workflows, and quality control processes that allowed me to lead a development “team” composed entirely of Claude Code and Cursor agentic coding assistants.

This was not autocomplete. It was architecting reliable, repeatable workflows around AI agents to ship production software at a pace that would normally require a full engineering team. The result is a proven playbook for AI-assisted development at production quality - one I bring to every engagement.

Multi-LLM Orchestration with n8n

The platform orchestrates multiple LLM calls in parallel using n8n as the workflow engine. Each policy submission triggers up to three concurrent AI workflows: lead scoring, agent analysis, and consumer communication. Workflows are configurable per agency and can target different model vendors (Anthropic, OpenAI) based on cost and capability requirements. Results are stored in tenant-isolated databases and optionally dispatched to partner systems via outbound webhooks.

Open Source: Headless Claude Automation Template

The AI-assisted engineering workflows I developed while building agentCanvas evolved into something worth sharing. I distilled the core pipeline into an open-source template that anyone can use to set up fully autonomous software delivery with headless Claude Code agents running in GitHub Actions.

The template implements a multi-agent pipeline with separated roles: a PM agent that decomposes requirements into stories in Linear, dev agents that implement features and open pull requests, a review agent that evaluates every PR for security and architecture issues, and a fix agent that iterates on review feedback automatically. Each agent runs in its own GitHub Actions VM with a fresh context window scoped to its job - no context pollution between roles, and each self-destructs when done.

What makes this different from other agentic coding setups is the separation of concerns and the safety model. Branch protection blocks all direct edits to main. A destructive command blocker catches force pushes, recursive deletes, and database drops before they execute. Credential deny rules prevent agents from reading secrets. All GitHub Actions are pinned to SHA, and the fix agent caps at five iterations before escalating to a human. The result is a system where you write a requirements doc, run a single command, and review pull requests - two human touchpoints for the entire delivery cycle.

View the template and get started

Building AI products is hard. Building them in regulated industries is a different game entirely. It requires someone who has been through HIPAA audits, PCI-DSS certification, and carrier compliance reviews - not someone who will figure it out along the way.

At Polly, I led PCI-DSS certification (full SAQ D, 329 requirements) as both Merchant and Service Provider, with zero data-breach incidents across five years. At WellSky, I architected a HIPAA-compliant platform serving 55,000 healthcare users. I understand that in regulated industries, compliance isn't a box you check after launch - it's a design constraint that shapes every architectural decision from day one.

This is where I differ from most AI-focused CTOs. I don't treat compliance as friction. I treat it as a feature - because in insurance, healthcare, and fintech, it's what unlocks enterprise contracts and keeps them.

Scaling a technology organization requires a leader who has been through it before - someone who knows what breaks when you go from 10 to 50 to 200 people, and how to build systems and teams that survive that growth.

At Polly, I was the fourth employee. Over four years, I grew the company from 4 to 220 people, built the engineering and product organization to 50, and managed a $6.5M P&L. I led technical due diligence through $184M+ in venture raises, including a Goldman Sachs-led $110M Series C. I've sat across the table from institutional investors and walked them through architecture decisions, scalability plans, and risk models.

At Amplo (formerly Veruna), I inherited an outsourced development operation and transitioned it to a fully in-house engineering team, then built a greenfield P&C Agency Management System that doubled SaaS revenue year over year. Key customers included USAA, AmFam, and Berkley.

I've done the zero-to-one build. I've done the scale-up. I've done the late-stage fundraise. Whether the engagement is fractional or full-time, I bring the experience of having been through every stage of the journey.

Before InsurTech, I spent a decade in enterprise software across healthcare, banking, and telecom - building the foundation in complex systems, large-scale data, and regulated environments that everything since has been built on.

WellSky (2011-2016): Led a five-year rewrite of a HIPAA-compliant Home Health and Hospice platform into enterprise SaaS on Azure/.NET, managing teams of up to 25 engineers. Reduced query latency 70% and boosted engineering velocity 30% through Scrum and CI/CD adoption.

Vertek: Director, Software Engineering - Enterprise Telecom (AT&T, Telstra)

CV Systems: Technology Director - Enterprise Banking (JPMorgan, Federal Reserve Bank, Mellon)

GE Healthcare: Lead Software Engineer - Inpatient Radiology (Cleveland Clinic, Kaiser, Mayo Clinic)