About

My path to product management wasn't traditional. I left college early to build businesses — and quickly realized that launching a company meant learning how to do everything myself. I picked up full-stack development through bootcamps, taught myself marketing, and learned product by necessity: when something broke, I fixed it. When no one showed up, I figured out why.

Over time, that turned into a real skillset: building, shipping, and aligning teams around things that actually work.

Today, I'm an AI Maestro focused on orchestrating how AI transforms organizations. I specialize in agent orchestration, eval-first engineering, and product strategy — tying LLMs, automation systems, and product delivery to measurable business outcomes.

My product philosophy is simple: when team goals orbit business goals, things work. When they don't, even “successful” features can quietly fail.

name: Jason Derr
role: AI Maestro

focus:
  - agent_orchestration    # multi-agent systems, tool routing, handoffs
  - eval_first_engineering # metrics before models, outcomes before outputs
  - product_strategy       # aligning AI capabilities with business impact

stack:
  models: [Claude, GPT-4, Gemini, open-source]
  infra: [LangChain, LangSmith, Vercel AI SDK]
  product: [Next.js, TypeScript, Tailwind]
  ops: [AgentOps, OpenTelemetry, Semgrep]

Experience

I've worked across zero-to-one startups, internal platforms, and AI consulting teams — where the challenge isn't just building fast, it's building with purpose. My work spans the full product lifecycle and go-to-market strategy — from sketching out early prototypes to launching production-ready systems and driving market adoption with measurable business impact.

Along the way, I've delivered projects involving:

• LLMs & Generative AI — real-time content scoring engines, prompt workflows, automation triggers
• AI Systems Planning — internal prioritization frameworks and scoring models adopted org-wide
• MLOps & Model Evaluation — surfaced high-ROI use cases, tracked pilot-to-production success
• User-Centric Interfaces — redesigned onboarding flows and automation experiences to improve adoption
• Cross-Functional Leadership — worked across engineering, design, AI research, and ops to ship fast and stay aligned

I've led distributed, multi-disciplinary teams — including engineers, data scientists, and business stakeholders — and I thrive in ambiguous spaces where technical complexity meets business pressure.

Philosophy

I build AI products that solve real problems, are strategically aligned, and deliver measurable impact.

My approach starts with business outcomes — not novelty. Whether it's planning an LLM-based feature or designing an internal scoring tool, I focus on clarity, iteration, and cross-functional execution. That means fewer “what ifs” and more “what works.”

Areas I specialize in:

• AI Product Strategy — aligning roadmap bets with business impact, not feature requests
• LLM & Automation Design — turning GenAI into workflows that users actually adopt
• System Thinking — building reusable frameworks, not one-off tools
• Metrics & Instrumentation — measuring what matters, making it visible
• Stakeholder Alignment — making sure product, engineering, and ops don't talk past each other

// How I evaluate any AI initiative
function shouldWeBuild(proposal: AIFeature): Decision {
  const impact = measureBusinessOutcome(proposal)
  const feasibility = assessTechnicalReality(proposal)
  const risk = evaluateFailureModes(proposal)

  if (impact.confidence < 0.7) return "needs_more_research"
  if (feasibility.timeline > "90_days") return "reduce_scope"
  if (risk.unmitigated > 0) return "add_guardrails"

  return "ship_it"
}