Proof Beyond the Demo.
The patterns behind a strong AI product are learned in production: durable architecture, clear ownership, usable workflows, security maturity, and delivery teams that can keep moving.
These projects span HR Tech, security, recruiting, and enterprise SaaS. The founder outcome is consistent: less ambiguity, stronger technical decisions, and products built to survive real users.
Agency Scale-Up
CTO Leadership — Startup to 30+ Engineers
Scaled an agency from a small founder-led shop into a 30+ person engineering organization. Built the hiring, mentoring, architecture review, and delivery habits that help founders turn ambiguous product goals into stable execution without losing technical quality.
MunsterMind
AI-Native HR Analytics Platform
Architected an AI-native HR analytics SaaS that turned resumes, interviews, and qualitative hiring signals into usable product workflows. The work required more than an impressive LLM demo: data ingestion, model boundaries, explainability, user trust, and product decisions people could act on.
munstermind.io
Elite Reverse Recruiting
Scalable React/Node Architecture
Designed the technical foundation for a reverse recruiting platform that scaled from early traction to a 50+ person operation. Real-time workflows, AI-assisted job-fit scoring, Node services, and Terraform-managed infrastructure proved the value of durable architecture founders can keep building on.
Security & Compliance SaaS
From MVP to Enterprise Platform
Helped build the foundations for a security and compliance SaaS that grew into an enterprise platform. The work centered on integration reliability, CI/CD, E2E testing, backend contracts, and 20+ connectors built with the operational rigor regulated customers expect.
Start with the plan that makes the build investable.
Before you hire, raise, or scale development, get a founder-ready blueprint for scope, architecture, cost, risk, timeline, and delivery. AI approach is included when the product needs it.