Independent AI systems studio · San Diego

What does your team still do by hand every week?

There’s probably a system hiding in it.

I build custom tools, workflows, and internal systems—using AI where it earns its place and ordinary software engineering everywhere else. You get back something that handles the repetitive part, shows its work, and leaves the decisions to a person.

Most people arrive with one of these three.

“I have an idea I want built.” See what finished work looks like “The same job eats our time every week.” How a repeated process becomes a system “We built something and nobody uses it.” What it takes to finish it

01 / WHAT I BUILD

Three ways to put AI to work.

These are deliberately small projects with an end date. Your team keeps its roadmap. I take ownership of the awkward thing sitting between “we should” and “it works.”

01

For manual work that should not be manual

AI Workflow Build

I learn a repetitive, judgment-heavy process by doing it with you once—then turn those steps into a focused tool or reusable AI workflow.

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  • Writing down how the work really happens
  • AI where it helps, fixed rules where it must not guess
  • Someone signs off, and there is a record
  • Running on real work
02

For systems that need to talk to each other

Connect It Up

I connect your AI feature to the data and software it needs to do its job—without giving it the keys to everything.

  • Access to the data it needs
  • Who is allowed to do what
  • Safe connections to your tools
  • You can see what it did
03

For a demo that never made it into real use

AI Feature Rescue

The demo proved the idea. I find what is stopping it from being used for real, fix the parts that keep breaking, and leave your team something it can run without me.

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  • Why it is stuck
  • What happens when it goes wrong
  • Making it cheaper and faster
  • Handed over and running for real

02 / SELECTED WORK

Real processes.
Working systems.

Every project begins with work someone is already doing. The goal is to preserve the judgment, remove the repetition, and leave behind something dependable.

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CASE STUDY 01AI WORKFLOW CLIENT PROJECT · IN USE

PUBLIC RELATIONS / RESEARCH OPERATIONS

A media list that verifies itself.

We turned hours of research, source checking, contact collection, and spreadsheet work into one guided command—with the professional still making the calls that matter.

THE SHIFT From rebuilding the process every time To a repeatable, evidence-backed workflow
Read the case study
CASE STUDY 02AI CONTENT SYSTEM STUDIO BUILD

CREATIVE PRODUCTION / A PERSON STILL DECIDES

A visual world that doesn’t drift.

Style rules, character references, scene generation, animation, and finishing became one reusable pipeline—with the person still choosing what is worth publishing.

THE SHIFT From rebuilding the creative process for every post To a controlled system that remembers the world
Read the case study
CASE STUDY 03PRODUCTION SYSTEM STUDIO BUILD

MOTION GRAPHICS / TIMING IS THE HARD PART

A video pipeline where audio owns time.

Script, narration, timing data, motion components, and rendering became one workflow that refuses to guess where a scene belongs.

THE SHIFT From nudging every scene by hand after each change To a schedule the narration decides
Read the case study

03 / THE APPROACH

Small enough to finish.
Important enough to matter.

01

Show me what is slow.

One conversation about what is slow, stuck, expensive, or being babysat by a person.

02

We make it smaller.

I define the smallest version that is genuinely worth having, what it trades away, and exactly what “done” means.

03

I build the thing.

Short feedback loops, visible progress, and real software—not a month of meetings about software.

04

Your team can own it.

Clean handoff, clear documentation, and no mysterious black box that requires me forever.

04 / WHAT YOU END UP WITH

The handoff,
in plain terms.

You should know what lands in your hands at the end before anything gets built. This is the list, every time.

01

A working system

Running on your real inputs, not a demo file. If it has not survived the actual work, it is not finished.

02

The code and the documentation

Written so another engineer could pick it up. Where it lives and who can reach it is agreed in writing before we start.

03

A walkthrough

I show the people who will actually run it how it works, using their own work as the example—not a rehearsed one.

04

The limits, written down

What it does not do, where it stops and asks for a person, and the cases we deliberately left out.

05

What to do when a run fails

Everything fails eventually. You get the failure modes, who is responsible for checking the output, and what to do when it goes wrong—agreed before it happens.

06

What it depends on

The outside services it needs to keep running. Those bill you directly and can change their terms without asking either of us, so you should know which ones you are standing on.

05 / THE FIT

A good problem for ATWORKS looks like this:

  • ✓It has a clear business owner
  • ✓The pain already exists
  • ✓It is small enough to actually finish
  • ✓Your core team cannot prioritize it
  • ✓Useful matters more than flashy
  • דWe should do something with AI”
  • ×A chatbot nobody asked for
  • ×An endless transformation program
  • ×Replacing your whole engineering team
  • ×AI for the sake of saying AI

06 / THE PART NOBODY SAYS OUT LOUD

What you’re actually
worried about.

Nobody raises these on a first call. They are the reason projects never start, so let’s do them now.

01
I don’t know enough about this to tell whether you’re doing it right.
You don’t need to. You need to be able to judge the result, and that part you already know better than I do—whether the list is good, whether the draft holds up, whether it actually saved anyone time. I explain what I am doing in plain words, and if I ever cannot, that is a bad sign about the work, not about you.
02
It’s going to replace people on my team.
Every system I have built does the repetitive part and hands the judgment back. The person who knows the work stays the one directing it. That is not a nice sentiment, it is the design—a system that decides on its own is a system nobody trusts.
03
Our data will end up somewhere we don’t control.
We decide up front exactly what it can see, and it gets nothing else—not the whole database because that was easier to wire up. If a system should not be touched, it does not get connected.
04
We’ll be stuck with you forever.
The opposite is the goal. You get the code, the documentation, and a walkthrough. I would rather you never need to call me again than build something only I understand.
05
It’ll cost a fortune and never finish.
You get a price and a date before anything starts. If it turns out to be bigger than it looked, you hear that early—not in month four.

If your worry is not on this list, put it in the first email. I would rather answer it than have it quietly end the conversation.

07 / WHO YOU’RE HIRING

Built by someone who has spent a career making complicated systems behave.

I’m Adam Thayer, a Staff Software Engineer in San Diego. Before software, I worked in Coast Guard aviation, search and rescue, avionics, auto repair, and a handful of jobs where the answer could not just sound good—it had to work.

That is still how I approach technology: understand the real problem, respect the consequences, and build the simplest durable thing that solves it.

PLAIN LANGUAGEREAL OWNERSHIPNO TECH THEATER

Taking on select projects

What’s the useful thing
your team keeps putting off?

Bring me the problem

One sentence, or just email me. Either way, tell me what is happening now and what you want to happen instead.