How Parallel Web Systems Maintains Code Quality at Scale

Parallel Web Systems builds web data APIs for AI agents, apps, and workflows, turning web search and intelligent knowledge tasks into programmable, enterprise-ready infrastructure. The Parallel Search API delivers the highest accuracy web search for AI on the market, while the Task API enables powerful agentic operations combining web search and reasoning.

Notable companies like Amp, Lindy, and Gumloop use Parallel to enable their AI agents to programmatically enrich data and search the web. At Macroscope, we also rely on Parallel to power our code review agents to retrieve the latest documentation for third party libraries from the web, reducing false positive code reviews that would otherwise stem from outdated LLM knowledge.

Moving Fast Without Breaking Things

As Parallel's engineering team grew, they hit a familiar scaling problem: how to maintain velocity while preserving code quality and visibility.

Code reviews weren't getting the attention they needed. Engineers wanted to ship fast, a critical advantage for a fast growing AI startup, but code review was becoming a bottleneck.

Additionally, keeping track of progress about how the product/codebase was evolving became challenging (due to a growing engineering team) without imposing more process overhead.

“We hate status meetings,” says Mike Jahr, a founding engineer at Parallel. Like many high-velocity teams, Parallel needed a way to stay aligned as a team without wasting time or adding process.

Streamlining Code Review

After trying other code review tools, Parallel chose Macroscope.

"It's the best AI code review tool on the market," says Mike Jahr, a founding engineer at Parallel. "Higher quality code is being checked in as a result and the team spends less time checking the nitty gritty and can instead focus on higher level issues in the code review."

Macroscope’s high signal-to-noise ratio allowed Parallel to keep engineering velocity high without sacrificing code quality, even while growing the engineering team.

Developers post a PR, iterate based on Macroscope's feedback, and then send it out for peer review once the obvious issues are addressed. Human reviewers focus on architecture, design decisions, and complex logic rather than catching bugs that should never have made it to review.

Macroscope has reviewed over 4,800 Pull Requests for Parallel, identifying over 6,000 code review suggestions, with only a 2.2% disapproval rate in the last 30 days (the % of review comments that received a 👎 by engineers).

Automatic Status

Macroscope helps Parallel’s engineering team stay focused on writing code and serving customers, rather than doing the ‘work around the work’ of sharing status updates and dealing with ticketing systems. Macroscope automatically surfaces development progress and activity for the engineering team and leadership to be on the same page.

“I personally love that we can avoid unnecessary status update meetings” – Parag Agrawal, Parallel’s CEO & Cofounder

Macroscope helped Parallel get a “win win”. Devs spend more time building, less time giving status updates. While the leadership team gets automated visibility around progress.

For Technical Teams Building Fast

For technical leaders building AI infrastructure, or any complex technical product, Parallel's experience highlights a key insight: the right tools can help you maintain quality without sacrificing velocity.

The challenge isn't choosing between speed and quality. It's finding ways to achieve both as you scale. For Parallel, Macroscope solved that problem by automating the routine parts of code review while preserving the human judgment needed for complex technical decisions.