As cloud venture monitoring software program monday.com’s engineering group scaled previous 500 builders, the crew started to really feel the pressure of its personal success. Product strains had been multiplying, microservices proliferating, and code was flowing quicker than human reviewers might sustain. The corporate wanted a solution 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 Progress and monday Dev groups, began experimenting with a brand new AI software from Qodo, an Israeli startup targeted on developer brokers. What started as a light-weight take a look at quickly turned a essential a part of monday.com’s software program supply infrastructure, as a brand new case examine launched by each Qodo and monday.com at present reveals.
“Qodo doesn’t really feel like simply one other software—it’s like including a brand new developer to the crew who really learns how we work," Regev advised 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 triggered severe safety vulnerabilities."
In contrast to code technology instruments like GitHub Copilot or Cursor, Qodo isn’t making an attempt to jot down new code. As an alternative, it makes a speciality of reviewing it — utilizing what it calls context engineering to grasp not simply what modified in a pull request, however why, the way it aligns with enterprise logic, and whether or not it follows inside greatest practices.
"You’ll be able to name Claude Code or Cursor and in 5 minutes get 1,000 strains of code," stated Itamar Friedman, co-founder and CEO of Qodo, in the identical video name interview as with Regev. "You will have 40 minutes, and you’ll'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 Evaluation, at Scale
At any given time, monday.com’s builders are transport updates throughout a whole lot of repositories and providers. The engineering org works in tightly coordinated groups, every aligned with particular components 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 verify for apparent bugs or type violations, however to guage 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 grasp how your crew works.
"The feedback Qodo provides aren’t generic—they replicate our values, our libraries, even our requirements for issues like function flags and privateness," Regev stated. "It’s context-aware in a manner conventional instruments aren’t."
What “Context Engineering” Truly Means
Qodo calls its secret sauce context engineering — a system-level method to managing all the pieces the mannequin sees when making a choice.
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 concept is that language fashions don’t actually “suppose” — they predict the subsequent token based mostly on the inputs they’re given. So the standard of their output relies upon virtually fully on the standard and construction of their inputs.
As Dana Nice, Qodo’s group supervisor, put it in a weblog submit: “You’re not simply writing prompts; you’re designing structured enter beneath a set token restrict. Each token is a design determination.”
This isn’t simply idea. In monday.com’s case, it meant Qodo might catch not solely the apparent bugs, however the delicate ones that sometimes 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 triggered issues in manufacturing.
"The hours we’d spend on fixing this safety leak and the authorized situation that it will deliver could be rather more than the hours that we scale back from a pull-request," stated Regev.
Integration into the Pipeline
As we speak, Qodo is deeply built-in into monday.com’s growth workflow, analyzing pull requests and surfacing context-aware suggestions based mostly on prior crew code critiques.
“It doesn’t really feel like simply one other software… It appears like one other teammate that joined the system — one who learns how we work," Regev famous.
Builders obtain strategies throughout the evaluate course of and stay in charge of closing choices — a human-in-the-loop mannequin that was essential for adoption.
As a result of Qodo built-in straight into GitHub by way of pull request actions and feedback, Monday.com’s infrastructure crew didn’t face a steep studying curve.
“It’s only a GitHub motion,” stated Regev. “It creates a PR with the checks. It’s not like a separate software we needed 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.
Inside 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 strategies replicate monday.com’s precise conventions, builders usually tend 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 crew kinds and practices. It doesn’t depend on one-size-fits-all guidelines or exterior datasets. All the things is tailor-made.
From Inside Software to Product Imaginative and prescient
Regev’s crew 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 straight into the code evaluate layer. That manner, reviewers can assess not simply whether or not the code “works,” however whether or not it solves the suitable downside.
“Earlier than, we had linters, hazard guidelines, static evaluation… rule-based… you must configure all the principles," Regev stated. "But it surely 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 technology, Qodo Merge for automated PR evaluation, and Qodo Cowl, a regression-testing agent that makes use of runtime validation to make sure take a look at protection.
All of that 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 beneath a freemium mannequin — free for people, discounted for startups by means of 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 due to a current partnership with Google Cloud, Qodo’s fashions can be found straight inside Vertex AI’s Mannequin Backyard, making it simpler to combine into enterprise pipelines.
"Context engines would be the large story of 2026," Friedman stated. "Each enterprise might want to construct their very own second mind if they need AI that really understands and helps them."
As AI methods turn into extra embedded in software program growth, instruments like Qodo are displaying how the suitable context — delivered on the proper second — can rework how groups construct, ship, and scale code throughout the enterprise.
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