AI engineering for teams that ship
I build custom AI tools that take repetitive work off your engineering team's plate—from code review to release day.
Built for your stack. Tested on your work. Yours to keep.
From a new pull request to a focused handoff.
Pull request, linked issue, team conventions
Relevant tests and a focused code review
Explain the issue. Suggest a fix.
Your team makes the call.
Press for progress
Your best engineers have better things to do than repeat themselves. Give them the tools to get back to building.
01 / What I build
The best place to start is a task your team repeats every week. I build the tools to take it off their hands—and the tests to know it's working.
Give repeatable tasks to an agent that knows your codebase, connects to your systems, and has clear limits on what it can change.
Bug triage / Codebase migrations / Internal knowledge
Less time moving work from one queue to another.
Put AI where your developers already work: assistants tuned to your conventions, focused code reviews, and CI failures with useful explanations.
Coding assistants / Code review / CI & releases
Keep your team focused on the work that needs them.
Turn manual content and data tasks into reliable pipelines, with checks, retries, and visibility into quality and cost.
Content generation / Data extraction / Localization
Handle more volume without more manual steps.
Test prompts, models, and agents against examples from your actual work. Catch regressions before a change reaches your team or customers.
Test datasets / Model comparisons / Quality gates
Make AI decisions with evidence you can inspect.
02 / How we work
One focused problem. Working software early. A clear way to decide whether it's worth taking further.
We look at where your team's time goes and choose one problem worth solving.
A clear scope and success criteriaI build the first tool in your environment, with early feedback from the people who will use it.
Working software in your stackWe check quality, time saved, and cost against the starting point. Then decide what comes next.
Results you can measureYour team gets the code, documentation, and training. I can stay involved as you need me.
A handoff your team can build on03 / Case studies
Faster AI experiences. Answers within reach. Richer mobile worlds. Releases the team can own. Four examples of turning engineering bottlenecks into working systems.
01 / Unlock capacity for a paid feature
A paid multi-image feature needed more capacity than fal.ai’s 40 in-flight images could support. In an afternoon, I moved primary image generation to Azure, taking concurrent capacity to 1,100 images. Routing across 11 regions, regional hedging, and fal.ai retained as fallback gave the business room to scale the feature.
Inside Azure, a stalled region triggers a second regional attempt. Hung regions are temporarily benched. If the Azure provider fails, the pipeline can fall back to another provider. Watch requests fan out across regions, open a hedge when one stalls, and use provider fallback when needed.
Requests fan out across available regions. Several images are in flight at once.
02 / Put answers in the team’s hands
The team relied on analytics dashboards and hand-built reports. I built a BigQuery foundation that brought product activity, game data, AI costs, and reliability data into reach—and a natural-language workflow for exploring it.
The agent uses a documented table catalog, joins, and metric definitions to query the warehouse. Shared conventions keep cohort definitions, reporting days, and staff exclusions consistent. Leadership, design, and engineering can explore the same foundations.
Natural-language analytics
Compare the pipeline’s speed and cost before and after the latest release.
| Metric | Earlier | Latest | Change |
|---|
The team can explore product behavior, AI costs, and reliability without needing someone to build a custom report first.
New dashboards and recurring reports can reuse the same data and metric definitions, giving future product and AI decisions a consistent starting point.
Explore product behavior, investigate bugs, and inspect AI costs in natural language.
03 / Make richer worlds fit on mobile
Image-heavy worlds were pushing older phones into memory limits. I built an on-demand texture-conversion pipeline, working with the client engineer to deliver GPU-ready images. That freed memory for the world itself, without a bulk conversion of every unused asset.
04 / Make shipping a team capability
A release took 10–13 manual commands. The wrong build still reached production and broke the service. Shipping depended on people coordinating the right code, environment, announcements, and ticket updates by hand.
I connected deployment records, AI-generated release notes, Discord announcements, and Jira transitions. Staging checks and exact build-tag propagation addressed the failure modes that had made releases fragile.
I used agentic workflows to implement the core integrations. Hardening and additional service integrations followed, turning the initial delivery into shared infrastructure.
Release notes, announcements, and ticket transitions happen as part of the workflow.
A release record connects the deployed code to its changes and tickets.
Teammates adopted the tooling and extended it to client releases and review previews.
Adoption in practice
Across the releases we handled together, my teammate’s share grew from 15.0% to 44.1%.
04 / The person behind the button
Most of my career has been in mobile gaming, where the next release is always around the corner.
That's where I learned to care about the work behind the work: build pipelines, internal tools, and all the small improvements that help a team move faster.
Turbo Button brings that same approach to AI. I work directly with your team to build useful tools, test them against real work, and make them part of how you ship.
Game studios are a natural fit. So is any engineering team with too much repetitive work between an idea and a release.
Work with meLet's find your turbo button
A slow review cycle? A manual release step? A queue that keeps growing?
Tell me about it. I'll reply personally, and we'll work out a useful first step.
Let's talk hello@turbobutton.ai