01 Instructions
Stop restating the same requirements.
We turn recurring requirements and corrections into project instructions and verification checks, reducing repeated intervention from you.
AIRE
AI reliability engineering.
A more robust AI development workflow, with less second-guessing and less pressure to check every small change.
For people building with AI.
Your coding agents need repeated corrections. Reviews produce noise. A workflow that worked yesterday behaves differently today. AI reliability engineering (AIRE) takes a data-driven, evidence-based approach to improving those workflows.
We gather and analyze information from your project history to identify friction points and broader issues behind recurring problems. Those findings guide the workflow improvements we implement and test.
We work with AI-first startups, vibe-coders and vibe-coding startups, international marketing agencies, and web development studios that need dependable AI-generated code.
- Approach
- Project-history analysis and evidence-based improvements
- Engagement
- Time-boxed research and implementation
- Deliverables
- Implemented and tested workflow improvements, and code-change recommendations where relevant
- Pricing
- Provided after a conversation or initial evaluation
- Repeat work
- Allow real-life usage between rounds
What we improve
Less correction. More dependable output.
02 Skills
Make a good workflow reusable.
Reusable implementation and review skills built around your project’s requirements. Invoke them directly or let agents use them within the workflow.
03 Personas
Separate implementation from review.
Specialist agent personas for implementation and review, with distinct instructions and checks tailored to your project. Repeatable review with less manual direction.
04 Model and effort
Know when more effort is worth it.
Tested model and reasoning-effort recommendations for your project, balancing output quality, cost and time.
What we deliver.
Implemented and tested workflow improvements, selected through project-history analysis. The number and mix depend on the findings and agreed time frame.
We may also recommend changes to your codebase that help AI reliably produce correct code.
We deliver changes in an agreed format—for example, a pull request to your repository—with a description of the work and relevant evidence. Contributions follow your agreed approval process.
- Instructions and skills
- Project-specific requirements and reusable workflows
- Agent personas
- Specialist implementation and review instructions
- Model and effort
- Tested recommendations for your project
- Code-change recommendations
- Where relevant, changes that help AI reliably produce correct code
- Delivery
- Agreed format and approval process
How pricing works.
We agree a time frame, then research, implement and test as many useful improvements as possible within it. Pricing is provided after a conversation or an initial evaluation.
On larger projects, the process can repeat. We recommend leaving a few weeks to a few months between rounds so real-life usage can accumulate and inform the next engagement.
If you are bringing us in as a subcontractor, you keep the client relationship. We agree the scope, delivery format and approval process with you.
- Engagement
- Agreed time frame for research and implementation
- Prices
- After a conversation or initial evaluation
- Further rounds
- Allow a few weeks to a few months of real-life usage
Start a project
Tell us where the workflow gets stuck.
Send us a short description of your project, how you use AI for development and what you want to improve. We will discuss the work and provide pricing after a conversation or initial evaluation.