Trust is built on measurement
Turn vibes into numbers.
Our Approach
How we build and release systems.
Proof
We believe that systems need to be instrumented and measured in order to be trusted and understood.
Composability
We believe that small, flexible, and measurable units achieve the best results.
Collaboration
We believe in using shared artifacts and goals to drive collaboration.
Feedback Loops
We believe in using production data to drive continuous improvement.
Ownership
We believe in establishing clear ownership and accountability guidelines around systems released to production.
Run your requirements
Create living artifacts that drive the work and measure its progress
Product requirements → Golden seed
Distill your requirements into a seed, grow it into a dataset you can build toward and measure against.
Acceptance criteria → Scorers
Each criterion becomes a grader, so every result is scored against what you defined as “good”.
Vision → Benchmark
Your vision becomes the bar those grades are read against, so progress is a number, not a feeling.
A shared, usable truth
Product
Turns goals and acceptance criteria into the dataset and scorers that define "good."
Engineering
Builds the agent and ships against those scorers, catching regressions before users do.
Leadership
Gets quality and ROI as benchmark numbers showing changes over time.
Align development with production
Build evaluations before you ship. Keep them honest after you do.
Pre-production
Run your prompt against the dataset with your scorers, and read the benchmark before anything ships. You understand quality going into your launch.
Production
The same scorers keep grading real traffic, so a regression surfaces as an alert instead of a support ticket. You know quality is holding up.
Remove conflicts of interest
Distribute the work so everyone can do their best.
No-code evaluations
Anyone defines "good" in plain language and runs the evaluation themselves. No Python, no judge harness, no waiting on engineering.
Prompt extraction
Prompts live versioned outside the codebase. Product can tune, test, and roll back without a deploy, while engineering keeps a clean runtime.
Swap models
Because quality is measured, you can drop in a new model, run your evaluations, and see the impact — then ship the swap without a rewrite.
Upgrade your organization’s beehive
Economics
The cost of running AI applications is only going up. Make sure you’re allocating headcount and token budgets effectively, before they cut into your bottom line.
Collaboration
When anyone can do anything, hiring, team formation, and organization structures need to be reconsidered.
Accountability & Responsibility
If “a computer can never be held accountable”, who in your organization is? Build a clear picture of owners, decision makers, and first responders.
Regulation
There’s more scrutiny around AI systems than ever before, make sure you’re in line with your obligations.
Get in touch
Let us know how we can help