How Prado 5×’d Deploys and Cut Review Time by 30% Using Optibot + Insights

Prado had built a reputation for helping food and meal-prep brands deliver high-quality subscription experiences at scale. Their e-commerce platform made it easy for customers to plan, customize, and receive recurring meals without friction. But behind the scenes, their engineering team was struggling with a familiar problem: velocity.

Manual code reviews, unpredictable deploys, and a lack of visibility into team performance slowed development to a crawl. “We were releasing to production once a day — sometimes only two or three times a week,” recalls Granger Blacket, Prado’s CTO.
For a fast-moving startup, that pace wasn’t enough.

The company needed a better way to review code faster, ensure quality, and understand engineering output in real time — without adding more headcount.

The Problem

Manual review bottlenecks and limited visibility slowed delivery

As Prado’s customer base expanded, so did its codebase. The number of pull requests climbed, but the review process couldn’t keep up.

“The big reviews — nobody wanted to touch them,” says Blacket. “Smaller ones were easy to approve, but we’d end up with long pickup times and unpredictable releases.”

What started as a manageable workflow became a bottleneck:

• Large PRs piled up, waiting for human reviewers.
• Deployments lagged, creating tension between speed and safety.
• Team metrics were murky, making it difficult to see which processes actually drove performance.

Engineers found themselves stuck in a loop of waiting — waiting for reviews, waiting for feedback, waiting to deploy. Meanwhile, leadership lacked the visibility to pinpoint where and why things were slowing down.

“Our feedback loops weren’t scaling with the company. We needed a system that could keep up with how fast we were moving.”
— Granger Blacket, CTO, Prado

The Solution

AI-powered reviews and real-time engineering insights

To accelerate their delivery pipeline and eliminate review bottlenecks, Prado turned to Optimal AI — adopting both Optibot, the AI-powered code review agent, and Insights, the engineering analytics platform.

“We wanted something that didn’t just speed up reviews but actually made them better,” explains Blacket. “Optibot gave us that and more.”

Optibot reviews every pull request automatically within seconds, flagging risky logic, suggesting improvements, and linking changes back to original issues in Jira or GitLab for full context.

The first time Prado’s team used it, Optibot caught a pagination edge case and recommended a simple, clean fix — complete with implementation details.

“That’s when it clicked,” says Blacket. “It wasn’t just output; it was a conversation. It felt like having another senior engineer on the team.”

At the same time, Insights provided Prado with continuous visibility into team metrics:

• Cycle time and review duration
• Deployment frequency by environment
• PR activity and review quality trends

Together, Optibot and Insights transformed Prado’s workflow.
Now, once a PR is submitted, Optibot reviews it instantly. Engineers address its comments before human reviewers step in, reducing noise and keeping merges clean. Insights then tracks how long reviews take, how fast merges happen, and how often deploys go out — turning guesswork into measurable performance.

“Our new standard is simple: Optibot reviews every PR. You resolve its feedback before anyone else touches it. That change alone sped us up dramatically.”
— Granger Blacket, CTO, Prado

The Results

5–6× daily deploys, 30% faster PR cycles, and safer releases

The impact was immediate. With Optibot handling first-pass reviews and Insights providing real-time visibility, Prado’s engineering team saw measurable gains across speed, quality, and confidence.

• Staging deployments increased from 1–2 per day to 5–6 per day — a 5× improvement.
• Average PR cycle time dropped by roughly 30%.
• Every PR received an immediate review, eliminating idle time and ensuring code safety.
• Engineers resolved Optibot’s feedback before handing PRs to humans, creating cleaner merges and fewer regressions.

“I still contribute code myself, and even my cycle time dropped by about 30%,” says Blacket. “It’s like we added a full-time senior engineer dedicated to reviews.”

Beyond speed, the cultural shift was equally powerful. What started as cautious experimentation turned into daily adoption — Optibot’s recommendations became part of the team’s rhythm.

“There was resistance at first, like with any AI tool,” admits Blacket. “But once people saw it catching real issues and improving code, the team leaned in. Now, everyone checks Optibot before merging.”

The combination of faster feedback loops and complete visibility gave Prado’s leadership the clarity they’d been missing. Instead of wondering where things slowed down, they could now see patterns instantly and make informed decisions to keep the team moving.

The Impact in Numbers

Scaling Faster, Smarter, and With More Confidence

With Optibot and Insights, Prado’s engineering organization turned code review from a bottleneck into a competitive advantage.
Deployments are faster, quality is higher, and the team has full trust in their process.

“I feel safer knowing every PR has that extra layer of review,” says Blacket. “If you’re trying to speed up to keep up, you need Optibot.”

By embedding AI into their daily development cycle, Prado created a foundation for continuous delivery that scales — not just for speed, but for sustainability.

Today, the company ships features multiple times a day, catches issues earlier, and spends more time building what matters: better experiences for their customers.