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Founded by Cyrus Nouroozi & Amir Mehr
Hey there! Meet Cyrus & Amir. In the past, they've both been lead engineers and founding CTOs. They became contributors to DSPy and discovered the future of programming with language models.
This is the story of how the Zenbase founders came to this insight, their glimpse into the future, and 2 case studies on how Zenbase has helped companies escape prompt hell and scale prompt engineering.
Prompt “engineering” is the most time-consuming, stressful, and uncertain part of programming with LLMs. With DSPy, the founders had found something profound. It promised to save them from the all-too familiar user journey they — like so many others — had experienced.
DSPy kept growing. It became Stanford NLP's #1 GitHub repo with 16K stars. The founders started hearing of folks in Microsoft, Amazon, Google, and 40+ other companies using DSPy to prototype apps with it.
They began hearing the same things all over again. Although many found DSPy elegant and intuitive, countless folks found it impossible to grok. Those who managed to build something with it had headaches productionizing it; finding it difficult to scale, make reliable, and make performant.
So, the Zenbase founders set out to create the productionized DSPy.
Zenbase lets you optimize your prompts and models. They offer:
Zenbase came into the trenches with us to improve our evals from 10% to 80%. It really felt like they were a part of our team.
— Taeib, Cofounder @ Vera-Health.ai (YC S24)
They were staying up until 3am on multiple nights trying to prompt engineer their RAG query generator to retrieve the correct documents. Their progress was uncertain. It was stressful. Zenbase calls this prompt hell.
Prompt engineering is the most uncertain, risky, and stressful part of programming with LLMs. There didn’t seem to be a way out, but with Zenbase, they saw the light at the end of the tunnel.
Zenbase makes prompting systematic and peaceful. They helped Vera go from demo to production, by optimizing the prompt of their query generator. With a product that could handle doctors’ stress tests, they could focus on selling, and go to bed at a good time.
I’ve seen a lot of AI Devtools and Zenbase is solving a problem that everyone building with AI will have when going to production. The best part is their product is so easy to use that it’s a no brainer.
— Scott, CEO @ Superfilter.ai (YC S24)
It was all going great. Superfilter had just tested their AI email copilot with their beta users of investors and startup founders, and their users were excited. They onboarded a new cohort, and their prompts broke down. It worked well for the investors and startup founders, but not everyone.
Scott and his cofounder Travis realized that prompt engineering wasn’t going to scale to accurately categorize user emails into important, action required, or ignore.
Superfilter used Zenbase's hosted API to create email categorizers that learned from users’ existing behavior. With automatic prompt engineering, they were able to scale personalized experiences for every user.
Zenbase makes personalized AI apps easier to build and scale with automated prompt engineering.
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