How Braintrust turns customer requests into code with Codex
Quick Answer
Braintrust leverages OpenAI's Codex with GPT-5.5 to accelerate coding processes, enabling engineers to efficiently transform customer requests into functional code.
Quick Take
Braintrust leverages OpenAI's Codex with GPT-5.5 to accelerate coding processes, enabling engineers to efficiently transform customer requests into functional code. This integration enhances experimentation speed and reduces development time, significantly benefiting both engineers and clients.
Key Points
- Engineers use Codex with GPT-5.5 for faster coding and experimentation.
- The integration allows for quick transformation of customer requests into code.
- Development time is significantly reduced, enhancing overall productivity.
- This approach benefits both engineers and clients by streamlining workflows.
📖 Reader Mode
~2 min readBraintrust is the observability and eval platform for shipping quality AI products.
With Codex, its engineers can now take customer feature requests and create preview branches to show working ideas to customers in minutes.
In one month, half of the Braintrust team moved to Codex. For founder and CEO Ankur Goyal, the biggest change is not just faster coding. It’s a faster feedback loop with customers.
“It sounds simple, but Codex can literally print more text in the terminal without getting slow, and other models just can’t replicate that,” says Goyal.
“The biggest gain is speed.”
—Ankur Goyal, Founder and CEO
Turning customer requests into previews in minutes
Speed is often treated as a property of a tool, one that exists separate from its core functionality, but for Goyal, the speed difference “changes the way I interact with Codex compared to other models.”
With Codex, the Braintrust team can integrate iteration into its development workflow rather than letting requests sit and wait. “Codex unlocked our ability to try out customer feature requests in real time,” says Goyal. “Previously, if someone gave us a feature request, it would enter a backlog and get prioritized later.”
Instead, the team can copy and paste requests into Codex, create a preview branch, and show the completed request to the customer in minutes. “The really cool thing with Codex is that we get to iterate and ideate on feature requests with the customer in real time,” says Goyal.
“The more code we write, the more customer problems we can solve, and Codex is the most effective way to do that right now.”
—Ankur Goyal, Founder and CEO
Speed makes autonomous problem-solving possible
For Goyal, Codex changes how much setup it takes to try new ideas. “With other models, I would have to try to prompt the model to solve a specific problem,” he says. Slower tools require more hands-on guidance, which raises the cost of experimentation.
“With Codex, I’ve shifted to writing a test that demonstrates a problem, creating a sandbox environment, and then letting Codex run in that environment,” Goyal says. “This is a novel use case for me, and I can run experiments because of the speed.”
That speed gives the Braintrust team more room to experiment. Instead of prompting step by step, engineers can define the problem, let Codex work in a controlled environment, and move faster from idea to working solution.
“The really cool thing with Codex is that we get to iterate and ideate on feature requests with the customer in real time.”
—Ankur Goyal, Founder and CEO
— Originally published at openai.com
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