Greater AI autonomy requires rigorous verification.
We test how far AI can take a change, where it fails, and what evidence is needed to trust the result.
ButterOpsCase studies
Case studies
Here is what changed, how we measured it, and what each team kept.
Performance Livestock Analytics · client~50 people
PLA
A brownfield codebase and a 12-person engineering team. Throughput roughly quadrupled while reverts fell. It was proven on real runs, not on review alone, so leadership could take on more at once and pull the roadmap forward two quarters.
PHPGoSwiftReact/TypeScript
~4×
merged changes a month with the same 12 engineers, while reverts fell from 1.4% to 0.25%
2 quarters
roadmap projects pulled forward, and a full backlog cleared before year end
~15×
faster: a pre-AI ML model behind product insights, rewritten with byte-for-byte parity
33%
projected savings on the AWS bill
Read the PLA case study
Our learning playgroundOur own product
ButterTrip
The group travel app we build and maintain across web, iOS, and Android, and our learning playground. We test new models, tools, and techniques on real work, then adapt what we learn to your company.
WebiOSAndroid
Greater AI autonomy requires rigorous verification. We learn what makes the result dependable.
The right setup lets more people build. Clear boundaries and reliable verification make that possible.
Read the ButterTrip case study
What we have learned
A continuing playground for learning what works with AI.
We explore new approaches through real building and maintenance, then help clients adapt what we learn.
The right setup lets more people build.
ButterTrip helped us learn how someone without an engineering background can build real features, with clear boundaries and reliable verification.
How we work
We verify that the work holds up in real systems.
Define what the change must preserve.
Turn requirements into checks that establish whether the new behavior is correct. At PLA, this work also uncovered long-standing bugs.
Run it against realistic systems and data.
Exercise the whole process in an environment that reflects the real application. At PLA, running the change found seven bugs that AI review had missed.
Check what happens when operations overlap.
Examine the rules for work happening at the same time, where tests and review can miss failures. At PLA, this exposed a bug in the existing system.
Measure reliability alongside speed.
At PLA, roughly 4× the engineering throughput came with reverts falling from 1.4% to 0.25%.
Cost experience
Get more from your AI budget.
We help you understand usage, choose suitable plans, and manage costs as adoption grows. Flat subscriptions can support substantial work when their limits and controls fit your needs.
One engineer’s monthly usage
Across PLA and ButterTrip, flat subscriptions covered this usage. The comparison is with API list prices, not an enterprise quote or a before-and-after saving.
API list-price equivalent~$25,000
Actual subscriptions$700
Free45 minutesWith Tyler and Julian
Let’s talk about what your team could do with AI.
In a free, 45-minute conversation, we learn how your company works and what isn’t working, sketch what working together could look like, and agree on next steps. If it makes sense, a written proposal follows within 24 to 72 hours.