As cloud venture monitoring software program monday.com’s engineering group scaled previous 500 builders, the staff started to really feel the pressure of its personal success. Product traces had been multiplying, microservices proliferating, and code was flowing quicker than human reviewers might sustain. The corporate wanted a means to evaluate hundreds of pull requests every month with out drowning builders in tedium — or letting high quality slip.
That’s when Man Regev, VP of R&D and head of the Development and monday Dev groups, began experimenting with a brand new AI instrument from Qodo, an Israeli startup targeted on developer brokers. What started as a light-weight take a look at quickly grew to become a vital a part of monday.com’s software program supply infrastructure, as a new case study launched by each Qodo and monday.com at the moment reveals.
“Qodo doesn’t really feel like simply one other instrument—it’s like including a brand new developer to the staff who truly learns how we work," Regev informed VentureBeat in a current video name interview, including that it has "prevented over 800 points per thirty days from reaching manufacturing—a few of them might have brought on severe safety vulnerabilities."
In contrast to code era instruments like GitHub Copilot or Cursor, Qodo isn’t attempting to write new code. As an alternative, it makes a speciality of reviewing it — utilizing what it calls context engineering to perceive not simply what modified in a pull request, however why, the way it aligns with enterprise logic, and whether or not it follows inside finest practices.
"You’ll be able to name Claude Code or Cursor and in 5 minutes get 1,000 traces of code," mentioned Itamar Friedman, co-founder and CEO of Qodo, in the identical video name interview as with Regev. "You’ve 40 minutes, and you may't evaluate that. So that you want Qodo to truly evaluate it.”
For monday.com, this functionality wasn’t simply useful — it was transformative.
Code Assessment, at Scale
At any given time, monday.com’s builders are delivery updates throughout a whole lot of repositories and providers. The engineering org works in tightly coordinated groups, every aligned with particular elements of the product: advertising and marketing, CRM, dev instruments, inside platforms, and extra.
That’s the place Qodo got here in. The corporate’s platform makes use of AI not simply to test for apparent bugs or model violations, however to consider whether or not a pull request follows team-specific conventions, architectural tips, and historic patterns.
It does this by studying from your personal codebase — coaching on earlier PRs, feedback, merges, and even Slack threads to perceive how your staff works.
"The feedback Qodo provides aren’t generic—they mirror our values, our libraries, even our requirements for issues like characteristic flags and privateness," Regev mentioned. "It’s context-aware in a means conventional instruments aren’t."
What “Context Engineering” Truly Means
Qodo calls its secret sauce context engineering — a system-level method to managing every part the mannequin sees when making a call.
This contains the PR code diff, after all, but in addition prior discussions, documentation, related recordsdata from the repo, even take a look at outcomes and configuration information.
The thought is that language fashions don’t actually “suppose” — they predict the subsequent token primarily based on the inputs they’re given. So the high quality of their output relies upon virtually solely on the high quality and construction of their inputs.
As Dana Effective, Qodo’s group supervisor, put it in a blog post: “You’re not simply writing prompts; you’re designing structured enter underneath a hard and fast token restrict. Each token is a design determination.”
This isn’t simply concept. In monday.com’s case, it meant Qodo might catch not solely the apparent bugs, however the delicate ones that usually slip previous human reviewers — hardcoded variables, lacking fallbacks, or violations of cross-team structure conventions.
One instance stood out. In a current PR, Qodo flagged a line that inadvertently uncovered a staging atmosphere variable — one thing no human reviewer caught. Had it been merged, it may need brought on issues in manufacturing.
"The hours we might spend on fixing this safety leak and the authorized situation that it will convey could be way more than the hours that we scale back from a pull-request," mentioned Regev.
Integration into the Pipeline
In the present day, Qodo is deeply built-in into monday.com’s growth workflow, analyzing pull requests and surfacing context-aware suggestions primarily based on prior staff code opinions.
“It doesn’t really feel like simply one other instrument… It appears like one other teammate that joined the system — one who learns how we work," Regev famous.
Builders obtain options throughout the evaluate course of and stay accountable for last choices — a human-in-the-loop mannequin that was vital for adoption.
As a result of Qodo built-in instantly into GitHub by way of pull request actions and feedback, Monday.com’s infrastructure staff didn’t face a steep studying curve.
“It’s only a GitHub motion,” mentioned Regev. “It creates a PR with the assessments. It’s not like a separate instrument we had to study.”
“The aim is to truly assist the developer study the code, take possession, give suggestions to one another, and study from that and set up the requirements," added Friedman.
The Outcomes: Time Saved, Bugs Prevented
Since rolling out Qodo extra broadly, monday.com has seen measurable enhancements throughout a number of groups.
Inner evaluation reveals that builders save roughly an hour per pull request on common. Multiply that throughout hundreds of PRs per thirty days, and the financial savings shortly attain hundreds of developer hours yearly.
These aren’t simply beauty points — many relate to enterprise logic, safety, or runtime stability. And since Qodo’s options mirror monday.com’s precise conventions, builders are extra seemingly to act on them.
The system’s accuracy is rooted in its data-first design. Qodo trains on every firm’s personal codebase and historic information, adapting to totally different staff kinds and practices. It doesn’t rely on one-size-fits-all guidelines or external datasets. Every little thing is tailor-made.
From Inner Software to Product Imaginative and prescient
Regev’s staff was so impressed with Qodo’s affect that they’ve began planning deeper integrations between Qodo and Monday Dev, the developer-focused product line monday.com is constructing.
The imaginative and prescient is to create a workflow the place enterprise context — duties, tickets, buyer suggestions — flows instantly into the code evaluate layer. That means, reviewers can assess not simply whether or not the code “works,” however whether or not it solves the proper downside.
“Earlier than, we had linters, hazard guidelines, static evaluation… rule-based… you want to configure all the guidelines," Regev mentioned. "However it doesn’t know what you don’t know… Qodo… feels prefer it’s studying from our engineers.”
This aligns carefully with Qodo’s personal roadmap. The corporate doesn’t simply evaluate code. It’s constructing a full platform of developer brokers — together with Qodo Gen for context-aware code era, Qodo Merge for automated PR evaluation, and Qodo Cowl, a regression-testing agent that makes use of runtime validation to guarantee take a look at protection.
All of this is powered by Qodo’s personal infrastructure, together with its new open-source embedding mannequin, Qodo-Embed-1-1.5B, which outperformed choices from OpenAI and Salesforce on code retrieval benchmarks.
What’s Subsequent?
Qodo is now providing its platform underneath a freemium mannequin — free for people, discounted for startups by Google Cloud’s Perks program, and enterprise-grade for firms that want SSO, air-gapped deployment, or superior controls.
The corporate is already working with groups at NVIDIA, Intuit, and different Fortune 500 firms. And thanks to a current partnership with Google Cloud, Qodo’s fashions are accessible instantly inside Vertex AI’s Mannequin Backyard, making it simpler to combine into enterprise pipelines.
"Context engines can be the huge story of 2026," Friedman mentioned. "Each enterprise will want to construct their very own second mind if they need AI that truly understands and helps them."
As AI programs turn into extra embedded in software program growth, instruments like Qodo are displaying how the proper context — delivered at the proper second — can rework how groups construct, ship, and scale code throughout the enterprise.
Disclaimer: This article is sourced from external platforms. OverBeta has not independently verified the information. Readers are advised to verify details before relying on them.
