~/production_ai_systems

I ship AI that solves real bottlenecks

I help founder-led product teams figure out where AI is actually useful, scope the right system, and ship it into a live workflow in 2-6 weeks. No agency layers. No handoff loss. No prototypes that never make it to production.

Tell me your bottleneckit's literally just me · I reply fast
01//where_im_useful

Why AI projects stall after the demo

handoff_tax

Strategy says one thing, design another, engineering a third. By the time it ships, the original business problem has been diluted.

prototype_graveyard

Weeks disappear into prompts, mockups, and internal demos that collapse when they hit real data, permissions, and edge cases

ownerless_risk

When nobody owns scope, UX, and implementation together, reliability problems show up late, expensively, and in production.

You don't need a team

You need one person who gets it.

02//risk_reduction

How the build gets de-risked

01

bring the workflow, not an AI wishlist

We start with the internal bottleneck that is already costing time, money, or attention, then cut away anything that does not move that constraint.

02

I scope the smallest version worth shipping

The system, integrations, approval points, failure paths, and UX get defined before code so the work does not drift into vague experimentation.

03

we test it inside the real workflow

Real data, real users, and visible edge cases. Not a sandbox demo that falls apart when it meets the business.

04

you leave with something your team can run

A production system, tested handoff documentation, and the operational safeguards needed to run it with confidence.

03//single_owner_advantage

Why one accountable operator works better

◆

Better judgment upfront

You don't need another round of AI theater. You need someone who can tell what's real, what's fluff, and what's worth shipping.

◇

Less translation loss

The product thinking, UX, and implementation stay in one head, so the workflow does not get rewritten at every handoff.

○

Clear accountability

If scope slips or reliability drops, ownership is obvious. The same person scoped and built it, so fixes happen fast.

04//faq

Questions worth asking

What is the right kind of problem for this?
An internal workflow that already exists, already hurts, and is expensive to leave manual. Repetitive decisions, operational handoffs, support-heavy flows, and engineering workflows that are not yet AI-ready are usually the strongest fit.
When am I the wrong fit?
If you want a large retained team, open-ended experimentation, or a vague AI exploration project with no clear owner, I am the wrong fit.
Aren't you a single point of failure?
I build for handoff. From day one, you own the source code, the deployment environment, and the documentation. I build on standard, maintainable web frameworks—no proprietary black boxes. Any competent engineer on your team can take over the codebase.
How do you keep AI systems reliable?
Narrow scope, grounded context, explicit tool use, clear fallback paths, and human review where it matters. The goal is dependable throughput inside a real workflow, not a clever demo.
What do I actually get at the end?
A working system in production, tested handoff documentation, and the operating context your team needs to run it and keep improving it.
How are engagements priced?
Around a defined outcome, not open-ended hourly drift. Most engagements land between $5K-$25K depending on scope, integrations, and reliability requirements.