/ AI Systems Engineering

Your AI demo worked.
Production is a different story.

Most AI failures aren’t AI problems.

They’re what systems engineering exists to solve — bad boundaries, missing observability, architecture that can’t evolve. I fix the foundation so your team stops firefighting and starts shipping.

token costs

$5K/mo → $400/mo

onboarding time

3 months → 1 month

stability

PoC running 3 months untouched

/ the real problem

Your team isn’t bad at AI. They’re bolting a probabilistic system onto infrastructure that wasn’t designed for it.

I see the whole system — from code to infrastructure to the processes around it. You get architecture that teams can operate and evolve — not fragile experiments held together by prompt tweaks and mumbo jumbo.

Sound Familiar?

  • ■ 01 Two engineers fixing prompts instead of shipping features +

    I design agent architectures with guarded execution, deterministic fallbacks, and clear observability — so the system runs itself instead of consuming your team.

  • ■ 02 Token bill 10x what you budgeted +

    I cut model spend through architecture decisions: caching, model routing, request batching, and eliminating redundant calls. Typical result: cost reduction by several times without quality loss.

  • ■ 03 RAG that hallucinates on your own data +

    I build retrieval pipelines with proper indexing, evaluation loops, and quality gates. Sometimes the answer is that you don’t need RAG at all — and a simpler system works better.

  • ■ 04 Afraid to touch your working system to add AI +

    I integrate AI into existing services and data flows without destabilizing what already works. Legacy is not a dirty word — it means your system survived production.

  • ■ 05 AI broke and nobody knows where to look +

    I build observability into AI systems from day one: structured logging, evaluation metrics, drift detection, and clear runbooks for when things go wrong.

  • ■ 06 Team can build features but not AI systems +

    I coach teams in architecture thinking, delivery tradeoffs, and implementation patterns for AI. The goal is to transfer capability, not create dependency on me.

/ how it works

  1. 01

    System Discovery

    I ask uncomfortable questions. Map your real constraints, data realities, and operational bottlenecks — not what the pitch deck says.

  2. 02

    Architecture Blueprint

    A practical target architecture with a migration path you can actually execute. Not a 40-page document nobody reads.

  3. 03

    Implementation Sprint

    I build critical components with your team and transfer context. You own the system, not me.

  4. 04

    Operational Hardening

    Monitoring, quality signals, runbooks, performance guards. The boring stuff that keeps systems alive at 3am.

FAQ

We already have architects. Why do we need you? +

Your architects are great at your domain. I bring the specific intersection of systems engineering, platform engineering, and production AI that most teams don’t have in-house. I work with your architects, not around them.

Do you write code or just make diagrams? +

Both. And more. I design the architecture, help your team build the critical components, and transfer the full context. You get a working system and a structured model that reflects reality — not a PDF gathering dust or pretty diagrams that nobody can execute on.

What if our problem isn’t really about AI? +

Even better. About a third of my engagements start with discovering the real problem isn’t what the client thought. I’m a practicing systems engineer — my background spans architecture, platform, product engineering, delivery, team structure, and process design. AI is one tool in the box. If your bottleneck is somewhere else, I’ll find it and fix it.

How fast can you start? +

Usually within a week. Discovery call takes 30 minutes. If we’re a fit, I send a scoped proposal within 2-3 days.

/ next step

Stop babysitting your AI system.

30-minute call. You describe the situation. I tell you if I can help and how. No pitch deck, no sales theater.

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