WE BUILT BECAUSE WE NEEDED IT
WE LEARNED THIS
We learned it the hard way while running 84 AI agents in production and spending $6,000 a day on models, we had no reliable way to measure quality, every model change was a guess and the clearest signal came from customers after something had already gone wrong
One agent took six months to get right because each round of feedback had to travel from a customer, back to our team, and into another prompt change, we knew there had to be a better way
OBSERVABILITY TOOLS WERE NOT ENOUGH
The tools we tried could store conversations and show us what happened, they could not tell us whether an output was good, why it failed, or which change would make it better
One of our founders built the entire quality system himself, then he had to maintain the rules, ground truth, datasets, and scores by hand as the product changed, the work never really ended
SO WE BUILT
With BENCH, a team connects its GitHub repository or uploads its prompts and shares as much business context as it wants, BENCH defines what good and bad outputs look like, creates the ground truth and datasets, then tests different prompts and models
The result is a tested change that can raise quality and lower cost, BENCH prepares the pull request, but the team stays in control of what ships
QUALITY KEEPS IMPROVING
A one-time quality check is not enough, products change, customers reveal new edge cases, and better models keep arriving, BENCH watches what happens in production, flags new problems, updates the checks, and prepares the next improvement
When a new model appears, BENCH can test it against the same definition of a good output, teams no longer need to choose models by instinct or rebuild their quality process every time something changes
MAKE QUALITY THE DEFAULT
Our mission is to make BENCH the default quality layer for every company building with AI, quality, cost, and model choice should be measured from the beginning, not treated as an afterthought
We want teams to build AI products knowing that quality is being maintained over time and that every model change is supported by evidence, higher quality and lower cost should be the normal result of shipping AI well