About Dylan Kotzer

Dylan Kotzer is an AI systems and full-stack engineer. He builds production software, custom AI assistants, grounded retrieval systems, agent orchestration, MCP servers, private AI setups, and durable workflow automation.

I help founders, operators, and technical teams ship full-stack products, custom AI assistants, grounded retrieval systems, and long-running workflows without a chain of handoffs.

I do not just plug model APIs into apps. I build the product surface, orchestration, safeguards, infrastructure, and delivery process that make AI useful in the real world.

What Dylan Builds Now

• Production web apps and SaaS

  Customer-facing products, internal tools, and full-stack platforms with real authentication, billing, and operational requirements.

• Custom AI assistants and copilots

  Assistants that search, reason, call tools, and help teams move real work forward instead of acting like a demo chatbot.

• Grounded retrieval systems

  RAG and knowledge workflows tied to expert content, business context, and answer quality that can be defended.

• Agent orchestration and long-running workflows

  Tool-using loops, background jobs, approvals, and structured execution paths for work that does not fit in a single prompt.

• Private and local-first AI setups

  OpenClaw customization, hybrid local-cloud routing, and self-hosted assistant architecture for teams that need more control.

• AI-enabled developer workflows

  Claude Code, Codex, Cursor, repo harnesses, and guardrails that turn AI into engineering leverage instead of a chaos multiplier.

Core Services

• Product Engineering & Full-Stack Delivery

  End-to-end product work for teams that need one senior builder to own the app surface, backend, data layer, and launch path. This is the right fit when the problem is bigger than a feature ticket but smaller than staffing an entire engineering org.

  • Customer-facing web apps and SaaS products
  • Internal platforms, dashboards, and admin tooling
  • Authentication, billing, APIs, and data modeling
  • Frontend architecture that stays maintainable as scope grows
  • Launch planning, observability, and operational handoff
  • Senior implementation without coordination drag

• Custom AI Assistants & Agent Systems

  I build AI systems that do real work: assistants, grounded retrieval flows, tool-calling interfaces, and long-running workflows with the surrounding architecture they need to hold up in production.

  • Custom AI assistants and internal copilots
  • Grounded retrieval and knowledge systems
  • Tool calling, structured outputs, and workflow routing
  • Agent orchestration with approvals and fallbacks
  • Hybrid model routing - local, cloud, and cost-tiered
  • Production UX for AI features, not just raw model access

• AI Enablement, Hardening & Rescue

  When a team has moved fast with AI and now needs better judgment, structure, or reliability, I can stabilize the codebase, tighten the workflow, and turn scattered tooling into a real engineering advantage.

  • Architecture cleanup for fragile AI-heavy codebases
  • Security and permissions review for AI integrations
  • Observability, test strategy, and failure reduction
  • Claude Code, Codex, Cursor, and Copilot workflow setup
  • Repo-aware guardrails and review checkpoints
  • Practical team enablement around AI-assisted delivery

Specialized AI Services

• Private / Local AI Systems

  Local-first and hybrid assistant setups for teams that need more control, tighter data boundaries, or less vendor lock-in.

• OpenClaw Setup & Customization

  OpenClaw deployments, custom skills, permissions tuning, and workflow integrations tailored to how your team actually works.

• Agentic Workflow Automation

  Long-running, tool-using processes for research, operations, content, triage, and approvals that do not fit inside a single prompt.

• AI Developer Workflow Enablement

  Repo harnesses, usage standards, and practical setup for teams using Claude Code, Codex, Cursor, and other agentic coding workflows.

• AI Cost & Token Optimization

  Model routing, prompt caching, batching, and right-sizing so your AI features cost what they should. Spend audits with concrete before/after numbers.

• Evals & AI Observability

  Eval suites, regression tracking, and production monitoring for AI features, so quality is measured instead of guessed at.

Engagement Models

• Consulting

  Senior technical judgment for teams that need architecture direction, AI systems advice, or a clear read on where risk is hiding.

  Ideal for: Founders and technical leads who need clarity before they commit budget or headcount

  • Architecture and implementation reviews
  • AI systems feasibility and rollout guidance
  • Product and delivery tradeoff discussion
  • Written recommendations and prioritization
  • Direct async follow-up after sessions

• Project-Based

  A defined build, rescue, or systems engagement with clear scope, milestones, and a single accountable owner from start to ship.

  Ideal for: Teams with a real product or systems need that should move decisively in a bounded engagement

  • Scoped roadmap and milestone plan
  • Hands-on senior execution
  • Working demos and progress checkpoints
  • Launch hardening and post-ship cleanup
  • Documentation and knowledge transfer
  • Source code ownership at handoff

• Ongoing Partnership

  Reserved senior capacity for teams that need a trusted technical partner across roadmap, delivery, and AI evolution over time.

  Ideal for: Companies that want continuity, faster response, and deep familiarity with their system

  • Reserved build and advisory capacity
  • Priority access and faster turnaround
  • Ongoing codebase hardening and evolution
  • Roadmap planning and technical prioritization
  • Team enablement around modern AI workflows
  • Continuous context instead of repeated onboarding

Proof and Positioning

• 20 years Coding across IT, cloud, web, and AI. Enough range to see where systems actually break and why.
• End to end Frontend, backend, infrastructure, and observability. Fewer handoffs, fewer blind spots, and less coordination drag.
• Real receipts Multi-agent loops, grounded retrieval, and SaaS delivery. Modern AI work backed by shipped products, not theory slides.
• Senior judgment Useful AI over flashy AI. I stay current on the tooling, but I only operationalize what earns its keep.

Capability Areas

Dylan works across product engineering, AI systems, agent orchestration, private and local AI, reliability and operations, and AI development workflows.

Capabilities

• Product Engineering: Full-stack product delivery across the app surface, data layer, and launch path. Core topics include Next.js and React product builds, TypeScript-first application architecture, Node.js and Python services, API design, auth, billing, and admin tooling, SaaS workflows and internal platforms, UX that stays usable while the system grows.
• AI Systems: AI features that are grounded, measurable, and built to survive contact with real users. Core topics include OpenAI and Anthropic integrations, RAG architecture and grounded answer design, Tool calling and structured outputs, Prompt routing and context assembly, Streaming assistant UX, Guardrails, eval thinking, and answer quality controls, Model routing, prompt caching, and token spend optimization.
• Agent Orchestration: Workflow systems for work that needs multiple steps, tools, and checkpoints. Core topics include Long-running background workflows, Tool-using agent loops, Human-in-the-loop approvals, Stateful execution and retries, Multi-agent coordination patterns, Workflow observability and failure handling.
• Private / Local AI: More control, less vendor lock-in, lower inference spend, and AI systems that can run closer to your data. Core topics include OpenClaw setup and customization, Local-model workflows with Ollama-style stacks, Hybrid local and cloud model routing, Self-hosted assistant architecture, Secure permissions and integration design, Slack, Discord, and internal channel integrations.
• Delivery, Reliability, and Ops: The infrastructure, monitoring, and operational discipline that keep launches from turning into cleanup jobs. Core topics include PostgreSQL, Redis, and pragmatic data architecture, Vercel, AWS, Docker, and deployment pipelines, Monitoring, rate limiting, and incident reduction, CI/CD and release hardening, Performance, resilience, and operational guardrails, Security-minded architecture reviews.
• AI Development Workflows: Modern AI tooling made useful for real engineering teams and real repositories. Core topics include Claude Code, Codex, Cursor, and Copilot workflows, Repo-aware prompt and harness design, Long-running coding loops and agent supervisors, AI review and validation checkpoints, Team enablement and usage standards, Codebase rescue when fast AI output becomes expensive debt.

Selected technologies and system concepts

Next.js and React product builds, TypeScript-first application architecture, Node.js and Python services, API design, auth, billing, and admin tooling, SaaS workflows and internal platforms, UX that stays usable while the system grows, OpenAI and Anthropic integrations, RAG architecture and grounded answer design, Tool calling and structured outputs, Prompt routing and context assembly, Streaming assistant UX, Guardrails, eval thinking, and answer quality controls, Model routing, prompt caching, and token spend optimization, Long-running background workflows, Tool-using agent loops, Human-in-the-loop approvals, Stateful execution and retries, Multi-agent coordination patterns, Workflow observability and failure handling, OpenClaw setup and customization, Local-model workflows with Ollama-style stacks, Hybrid local and cloud model routing, Self-hosted assistant architecture, Secure permissions and integration design, Slack, Discord, and internal channel integrations, PostgreSQL, Redis, and pragmatic data architecture, Vercel, AWS, Docker, and deployment pipelines, Monitoring, rate limiting, and incident reduction, CI/CD and release hardening, Performance, resilience, and operational guardrails, Security-minded architecture reviews, Claude Code, Codex, Cursor, and Copilot workflows, Repo-aware prompt and harness design, Long-running coding loops and agent supervisors, AI review and validation checkpoints, Team enablement and usage standards, Codebase rescue when fast AI output becomes expensive debt

Why Clients Hire Dylan Kotzer

• Senior Judgment, Faster Delivery

  I move quickly with modern AI tools, but I know where speed helps and where it creates expensive debt. That means faster delivery without the usual gamble.

• AI That Survives Production

  Models are the easy part. The hard part is grounding, routing, fallbacks, permissions, observability, and behavior that still makes sense after launch.

• One Accountable Builder Across the System

  You get someone who can own the product surface, backend, infrastructure, and AI layer together instead of turning every problem into another handoff.

• Architecture, Delivery, and Rescue in One Pair of Hands

  If a codebase was shipped too fast, I can audit it, stabilize it, and keep momentum going without starting over or creating more chaos.

Working Style

• Useful over flashy

  I care more about systems that help the business than features that sound futuristic but collapse under real operating conditions.

• Honest technical judgment

  If a plan is overcomplicated, overhyped, or pointed at the wrong problem, I will say so early. Good advice should save you money, not just win agreement.

• Operable after launch

  I want the finished system to be understandable, observable, and maintainable by the people who have to live with it after the deadline passes.

Best Fit

I am a strong fit when you need more than a fast implementation and less than a bloated delivery chain.

• A senior builder to own an ambiguous project from product shape through launch.
• A custom AI feature that has to work with real documents, workflows, and business constraints.
• A private or local-first assistant setup with more control, automation, and security than an off-the-shelf tool provides.
• A rescue pass on a codebase that moved quickly with AI and now needs hardening, structure, and judgment.
• A technical partner who can talk through business tradeoffs and then execute the architecture personally.

If you only need the cheapest possible implementation, I am probably not the right fit. I am best used when quality, judgment, maintainability, and fewer expensive mistakes matter more than lowest-bid output.

Frequently Asked Questions

What is your typical project timeline?

It depends on scope. A marketing site ships in 2-4 weeks. A SaaS product with auth, billing, and AI might take 2-3 months. I'll give you a real estimate after we talk, not a range designed to manage expectations.

Do you offer ongoing support after project completion?

Yes, retainer or on-demand. Bug fixes, feature additions, and maintenance. Most clients start project-based and move to a retainer once they see how the working relationship goes.

What technologies do you work with?

For AI: Anthropic/Claude, OpenAI, the Vercel AI SDK, RAG pipelines, MCP servers, and agent orchestration with tools like Claude Code and OpenClaw. For web: React, Next.js, TypeScript, Node.js, and Python. For infrastructure: AWS, Vercel, and Docker. Full list on the About page.

What does an AI agent project actually look like?

Usually one of three shapes: a grounded assistant that answers from your real data (RAG), a workflow agent that executes multi-step processes with tool calling and approvals, or an internal copilot wired into your existing systems. I'll tell you honestly which parts are production-ready today and which are hype - and where model routing, caching, and hybrid local/cloud setups can cut your token spend. Most first versions ship in 3-6 weeks.

Contact Information

• Location: Remote / Worldwide
• Response Time: Within 24 hours
• Email: [email protected]

Social Profiles

• LinkedIn: https://www.linkedin.com/in/dylan-kotzer/
• GitHub: https://github.com/dkotzer
• Twitter: https://twitter.com/dylankotzer

Contact Dylan through /contact or by email at [email protected].

Summary

Dylan Kotzer is an AI systems and full-stack engineer for founders, operators, and technical teams that need one accountable senior builder. His offer spans product engineering, custom AI assistants, grounded retrieval, agent orchestration, private and local-first AI, AI developer workflows, and codebase hardening. Engagements include consulting, project-based builds, and ongoing partnerships.