As cloud project tracking software monday.com’s engineering organization scaled past 500 developers, the team began to feel the strain of its own success. Product lines were multiplying, microservices proliferating, and code was flowing faster than human reviewers could keep up. The company needed a way to review thousands of pull requests each month without drowning developers in tedium — or letting quality slip.

That’s when Guy Regev, VP of R&D and head of the Growth and monday Dev teams, started experimenting with a new AI tool from Qodo, an Israeli startup focused on developer agents. What began as a lightweight test soon became a critical part of monday.com’s software delivery infrastructure, as a new case study released by both Qodo and monday.com today reveals.

“Qodo doesn’t feel like just another tool—it’s like adding a new developer to the team who actually learns how we work," Regev told VentureBeat in a recent video call interview, adding that it has "prevented over 800 issues per month from reaching production—some of them could have caused serious security vulnerabilities."

Unlike code generation tools like GitHub Copilot or Cursor, Qodo isn’t trying to write new code. Instead, it specializes in reviewing it — using what it calls context engineering to understand not just what changed in a pull request, but why, how it aligns with business logic, and whether it follows internal best practices.

"You can call Claude Code or Cursor and in five minutes get 1,000 lines of code," said Itamar Friedman, co-founder and CEO of Qodo, in the same video call interview as with Regev. "You have 40 minutes, and you can't review that. So you need Qodo to actually review it.”

For monday.com, this capability wasn’t just helpful — it was transformative.

Code Review, at Scale

At any given time, monday.com’s developers are shipping updates across hundreds of repositories and services. The engineering org works in tightly coordinated teams, each aligned with specific parts of the product: marketing, CRM, dev tools, internal platforms, and more.

That’s where Qodo came in. The company’s platform uses AI not just to check for obvious bugs or style violations, but to evaluate whether a pull request follows team-specific conventions, architectural guidelines, and historical patterns.

It does this by learning from your own codebase — training on previous PRs, comments, merges, and even Slack threads to understand how your team works.

"The comments Qodo gives aren’t generic—they reflect our values, our libraries, even our standards for things like feature flags and privacy," Regev said. "It’s context-aware in a way traditional tools aren’t."

What “Context Engineering” Actually Means

Qodo calls its secret sauce context engineering — a system-level approach to managing everything the model sees when making a decision.

This includes the PR code diff, of course, but also prior discussions, documentation, relevant files from the repo, even test results and configuration data.

The idea is that language models don’t really “think” — they predict the next token based on the inputs they’re given. So the quality of their output depends almost entirely on the quality and structure of their inputs.

As Dana Fine, Qodo’s community manager, put it in a blog post: “You’re not just writing prompts; you’re designing structured input under a fixed token limit. Every token is a design decision.”

This isn’t just theory. In monday.com’s case, it meant Qodo could catch not only the obvious bugs, but the subtle ones that typically slip past human reviewers — hardcoded variables, missing fallbacks, or violations of cross-team architecture conventions.

One example stood out. In a recent PR, Qodo flagged a line that inadvertently exposed a staging environment variable — something no human reviewer caught. Had it been merged, it might have caused problems in production.

"The hours we would spend on fixing this security leak and the legal issue that it would bring would be much more than the hours that we reduce from a pull-request," said Regev.

Integration into the Pipeline

Today, Qodo is deeply integrated into monday.com’s development workflow, analyzing pull requests and surfacing context-aware recommendations based on prior team code reviews.

“It doesn’t feel like just another tool... It feels like another teammate that joined the system — one who learns how we work," Regev noted.

Developers receive suggestions during the review process and remain in control of final decisions — a human-in-the-loop model that was critical for adoption.

Because Qodo integrated directly into GitHub via pull request actions and comments, Monday.com’s infrastructure team didn’t face a steep learning curve.

“It’s just a GitHub action,” said Regev. “It creates a PR with the tests. It’s not like a separate tool we had to learn.”

“The purpose is to actually help the developer learn the code, take ownership, give feedback to each other, and learn from that and establish the standards," added Friedman.

The Results: Time Saved, Bugs Prevented

Since rolling out Qodo more broadly, monday.com has seen measurable improvements across multiple teams.

Internal analysis shows that developers save roughly an hour per pull request on average. Multiply that across thousands of PRs per month, and the savings quickly reach thousands of developer hours annually.

These aren’t just cosmetic issues — many relate to business logic, security, or runtime stability. And because Qodo’s suggestions reflect monday.com’s actual conventions, developers are more likely to act on them.

The system’s accuracy is rooted in its data-first design. Qodo trains on each company’s private codebase and historical data, adapting to different team styles and practices. It doesn’t rely on one-size-fits-all rules or external datasets. Everything is tailored.

From Internal Tool to Product Vision

Regev’s team was so impressed with Qodo’s impact that they’ve started planning deeper integrations between Qodo and Monday Dev, the developer-focused product line monday.com is building.

The vision is to create a workflow where business context — tasks, tickets, customer feedback — flows directly into the code review layer. That way, reviewers can assess not just whether the code “works,” but whether it solves the right problem.

“Before, we had linters, danger rules, static analysis... rule-based... you need to configure all the rules," Regev said. "But it doesn’t know what you don’t know... Qodo... feels like it’s learning from our engineers.”

This aligns closely with Qodo’s own roadmap. The company doesn’t just review code. It’s building a full platform of developer agents — including Qodo Gen for context-aware code generation, Qodo Merge for automated PR analysis, and Qodo Cover, a regression-testing agent that uses runtime validation to ensure test coverage.

All of this is powered by Qodo’s own infrastructure, including its new open-source embedding model, Qodo-Embed-1-1.5B, which outperformed offerings from OpenAI and Salesforce on code retrieval benchmarks.

What’s Next?

Qodo is now offering its platform under a freemium model — free for individuals, discounted for startups through Google Cloud’s Perks program, and enterprise-grade for companies that need SSO, air-gapped deployment, or advanced controls.

The company is already working with teams at NVIDIA, Intuit, and other Fortune 500 companies. And thanks to a recent partnership with Google Cloud, Qodo’s models are available directly inside Vertex AI’s Model Garden, making it easier to integrate into enterprise pipelines.

"Context engines will be the big story of 2026," Friedman said. "Every enterprise will need to build their own second brain if they want AI that actually understands and helps them."

As AI systems become more embedded in software development, tools like Qodo are showing how the right context — delivered at the right moment — can transform how teams build, ship, and scale code across the enterprise.